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8,392,284 | 1 | 8 | 1. A method implemented by one or more computing devices for automatically generating a naturally reading narrative product summary including assertions about a specific product selected by a user, said method comprising: determining, by at least one of the one or more computing devices, at least one attribute associated with said specific product; selecting, by at least one of the one or more computing devices, an alternative product based on said at least one attribute; and generating, by at least one of the one or more computing devices, a naturally reading narrative including assertions about the specific product and a recommendation of the alternative product. | 1. A method implemented by one or more computing devices for automatically generating a naturally reading narrative product summary including assertions about a specific product selected by a user, said method comprising: determining, by at least one of the one or more computing devices, at least one attribute associated with said specific product; selecting, by at least one of the one or more computing devices, an alternative product based on said at least one attribute; and generating, by at least one of the one or more computing devices, a naturally reading narrative including assertions about the specific product and a recommendation of the alternative product. 8. The method of claim 1 , wherein the naturally reading narrative is generating using assertion models that comprise a plurality of assertion templates, each assertion template including a natural sentence pattern and at least one field corresponding to said at least one attribute. | 0.501761 |
9,886,946 | 1 | 8 | 1. A computer-implemented method comprising: receiving, by an automated speech recognizer (ASR) system that includes (i) a context selector, (ii) a language model biaser, (iii) an ASR, (iv) a particular language model, and (v) a previously biased language model, audio data corresponding to an utterance of a user; determining, by the context selector of the ASR system, that the user is likely no longer within a particular context that is associated the previously biased language model; in response to determining that the user is likely no longer within a particular context that is associated with the previously biased language model, selecting, by the language model biaser of the ASR system, the particular language model for use in transcribing utterances; after selecting the baseline language model, generating, by the ASR of the ASR system, a transcription of the utterance using the particular language model; and providing a representation of the transcription for output. | 1. A computer-implemented method comprising: receiving, by an automated speech recognizer (ASR) system that includes (i) a context selector, (ii) a language model biaser, (iii) an ASR, (iv) a particular language model, and (v) a previously biased language model, audio data corresponding to an utterance of a user; determining, by the context selector of the ASR system, that the user is likely no longer within a particular context that is associated the previously biased language model; in response to determining that the user is likely no longer within a particular context that is associated with the previously biased language model, selecting, by the language model biaser of the ASR system, the particular language model for use in transcribing utterances; after selecting the baseline language model, generating, by the ASR of the ASR system, a transcription of the utterance using the particular language model; and providing a representation of the transcription for output. 8. The method of claim 1 , wherein providing a representation of the transcription for output comprising providing a textual representation of the transcription for display on a graphical user interface. | 0.730053 |
7,729,595 | 9 | 11 | 9. A method of reproducing a data structure for managing reproduction of subtitle data from a recording medium, comprising: reproducing, by a reproducing device, at least one main audio-visual (AV) data and at least one subtitle information segment from the recording medium, each subtitle information segment being represented by a PES packet of transport packets and having a one-to-one correspondence with the PES packet, the PES packet including a packet identifier for identifying a type of the packet, wherein the at least one subtitle information segment includes a segment identifier identifying the subtitle information segment as one of text data and graphic data; wherein a first subtitle information segment of the at least one subtitle information segment identified as the text data includes a palette identifier identifying palette information for controlling color attributes of the text data, wherein a second subtitle information segment of the at least one subtitle information segment identified as the text data includes at most two text subtitle regions, and each text subtitle region is linked to at least one first style information defined in the first subtitle information segment using an identifier, wherein the second subtitle information segment of the at least one subtitle information segment identified as the text data includes second style information for managing reproduction of the text data by the reproducing device, and wherein a third subtitle information segment of the at least one subtitle information segment identified as the graphic data is multiplexed with the at least one main AV data into a file. | 9. A method of reproducing a data structure for managing reproduction of subtitle data from a recording medium, comprising: reproducing, by a reproducing device, at least one main audio-visual (AV) data and at least one subtitle information segment from the recording medium, each subtitle information segment being represented by a PES packet of transport packets and having a one-to-one correspondence with the PES packet, the PES packet including a packet identifier for identifying a type of the packet, wherein the at least one subtitle information segment includes a segment identifier identifying the subtitle information segment as one of text data and graphic data; wherein a first subtitle information segment of the at least one subtitle information segment identified as the text data includes a palette identifier identifying palette information for controlling color attributes of the text data, wherein a second subtitle information segment of the at least one subtitle information segment identified as the text data includes at most two text subtitle regions, and each text subtitle region is linked to at least one first style information defined in the first subtitle information segment using an identifier, wherein the second subtitle information segment of the at least one subtitle information segment identified as the text data includes second style information for managing reproduction of the text data by the reproducing device, and wherein a third subtitle information segment of the at least one subtitle information segment identified as the graphic data is multiplexed with the at least one main AV data into a file. 11. The method of claim 9 , wherein the style information indicates at least one of font size, font style and font set for the text data. | 0.838443 |
8,291,319 | 13 | 16 | 13. A computer program product stored on a computer readable storage device having computer usable program code embodied thereon that is executable by a computer for retrieving solutions that solve a problem experienced by a user, the computer program product comprising: computer usable program code for generating a candidate solution document set for solving the problem, wherein a customized solution procedure for solving the problem is generated from a plurality of stored solution documents, and wherein a modified solution procedure with another set of instruction steps is generated for solving the problem based on the computer receiving an input rejecting one or more instruction steps included in the customized solution procedure; computer usable program code for generating a document object model tree for the generated candidate solution document set; computer usable program code for simplifying the generated document object model tree for the generated candidate solution document set by filtering out nodes in the generated document object model tree that do not have structural effects; computer usable program code for generating a template based on the simplified document object model tree; computer usable program code for calculating a structural similarity score for solution documents by comparing document object model trees of the solution documents with the generated template; computer usable program code for determining whether the structural similarity score for the solution documents is greater than a predetermined threshold; computer usable program code for storing the solution documents with structural similarity scores greater than the predetermined threshold in response to the computer determining that the structural similarity score is greater than the predetermined threshold; computer usable program code for sending relevant candidate solutions to the problem in response to receiving a query describing the problem, wherein the relevant candidate solutions include unstructured hypertext markup language solution documents found on a world wide web, and wherein the unstructured hypertext markup language solution documents include solution data found in web logs, instant messaging chat sessions, and online message boards; computer usable program code for analyzing instructions steps within one relevant candidate solution selected in response to receiving a selection of the one relevant candidate solution from the relevant candidate solutions; computer usable program code for calculating an instruction step similarity between the instruction steps within the one relevant candidate solution selected and other instructions steps within the stored solution documents; and computer usable program code for sending similar solutions containing similar instruction steps to the instruction steps contained within the one relevant candidate solution selected based on the calculated instruction step similarity. | 13. A computer program product stored on a computer readable storage device having computer usable program code embodied thereon that is executable by a computer for retrieving solutions that solve a problem experienced by a user, the computer program product comprising: computer usable program code for generating a candidate solution document set for solving the problem, wherein a customized solution procedure for solving the problem is generated from a plurality of stored solution documents, and wherein a modified solution procedure with another set of instruction steps is generated for solving the problem based on the computer receiving an input rejecting one or more instruction steps included in the customized solution procedure; computer usable program code for generating a document object model tree for the generated candidate solution document set; computer usable program code for simplifying the generated document object model tree for the generated candidate solution document set by filtering out nodes in the generated document object model tree that do not have structural effects; computer usable program code for generating a template based on the simplified document object model tree; computer usable program code for calculating a structural similarity score for solution documents by comparing document object model trees of the solution documents with the generated template; computer usable program code for determining whether the structural similarity score for the solution documents is greater than a predetermined threshold; computer usable program code for storing the solution documents with structural similarity scores greater than the predetermined threshold in response to the computer determining that the structural similarity score is greater than the predetermined threshold; computer usable program code for sending relevant candidate solutions to the problem in response to receiving a query describing the problem, wherein the relevant candidate solutions include unstructured hypertext markup language solution documents found on a world wide web, and wherein the unstructured hypertext markup language solution documents include solution data found in web logs, instant messaging chat sessions, and online message boards; computer usable program code for analyzing instructions steps within one relevant candidate solution selected in response to receiving a selection of the one relevant candidate solution from the relevant candidate solutions; computer usable program code for calculating an instruction step similarity between the instruction steps within the one relevant candidate solution selected and other instructions steps within the stored solution documents; and computer usable program code for sending similar solutions containing similar instruction steps to the instruction steps contained within the one relevant candidate solution selected based on the calculated instruction step similarity. 16. The computer program product of claim 13 , wherein the instruction steps include click-through instruction steps and core instruction steps, and wherein the click-through instruction steps and core instruction steps are labeled for identification. | 0.870485 |
8,805,756 | 18 | 19 | 18. The system as set forth in claim 17 wherein the automatically performing a crowd sourcing operation comprises performing a historical analysis of similar questions, clues or combination of questions and clues to determine potentially missing information from the received question or received clue, and performing an action to collect the missing information, and wherein the supplying a crowd-sourced enhancement to the deep question-answer computing system comprises including the collected missing information with the a crowd-sourced enhancement. | 18. The system as set forth in claim 17 wherein the automatically performing a crowd sourcing operation comprises performing a historical analysis of similar questions, clues or combination of questions and clues to determine potentially missing information from the received question or received clue, and performing an action to collect the missing information, and wherein the supplying a crowd-sourced enhancement to the deep question-answer computing system comprises including the collected missing information with the a crowd-sourced enhancement. 19. The system as set forth in claim 18 wherein the collection of missing information comprises an action selected from the group consisting of prompting a user via a user interface for the missing information, retrieving the missing information from a data repository, and querying one or more domain expert users via communications devices for the missing information, wherein the queried domain expert users are selected from a list of subject domain experts. | 0.85249 |
9,378,423 | 14 | 18 | 14. A method, comprising: identifying a plurality of shots in video content; creating a lattice of nodes that comprises at least one of a scene boundary node or a non-scene boundary node for each shot in the plurality of shots, wherein the lattice of nodes defines a plurality of paths beginning at a first shot of the plurality of shots and ending at a last shot of the plurality of shots; ranking the plurality of paths; selecting, based on the ranking, which one of the plurality of paths is to define where boundaries of a scene are located in the video content; for the scene, identifying first confidence values that are representative of features of the scene and that are a result of a video recognition process; for the scene, identifying second confidence values that are representative of features of the scene and that are a result of an audio recognition process; and based on the first confidence values and the second confidence values, determining, by a computing device, at least one identifier that defines whether an entity is present in the scene. | 14. A method, comprising: identifying a plurality of shots in video content; creating a lattice of nodes that comprises at least one of a scene boundary node or a non-scene boundary node for each shot in the plurality of shots, wherein the lattice of nodes defines a plurality of paths beginning at a first shot of the plurality of shots and ending at a last shot of the plurality of shots; ranking the plurality of paths; selecting, based on the ranking, which one of the plurality of paths is to define where boundaries of a scene are located in the video content; for the scene, identifying first confidence values that are representative of features of the scene and that are a result of a video recognition process; for the scene, identifying second confidence values that are representative of features of the scene and that are a result of an audio recognition process; and based on the first confidence values and the second confidence values, determining, by a computing device, at least one identifier that defines whether an entity is present in the scene. 18. The method of claim 14 , further comprising: determining a measurement that numerically indicates an importance of the entity to the scene; determining that the measurement satisfies a salience threshold; and inserting an identifier of the entity into a listing of entities that are present and salient to the scene. | 0.665971 |
7,689,418 | 14 | 25 | 14. A method for verifying user identity, comprising: receiving multi-modal inputs from a user interacting with a conversational system during a user session and transforming the received multi-modal inputs into formal commands executable by a program of instructions executable by a processor; extracting features from the multi-modal inputs and formal commands, wherein the extracted features include a combination of input modalities representative of the user's current interaction behavior for performing a task during the user session; and comparing the combination of input modalities representative of the user's current interaction behavior for performing the task to a behavior model representative of the user's past interaction behavior comprising a known combination of input modalities for performing the task used by the user during one or more previous user sessions to determine the identity of the user. | 14. A method for verifying user identity, comprising: receiving multi-modal inputs from a user interacting with a conversational system during a user session and transforming the received multi-modal inputs into formal commands executable by a program of instructions executable by a processor; extracting features from the multi-modal inputs and formal commands, wherein the extracted features include a combination of input modalities representative of the user's current interaction behavior for performing a task during the user session; and comparing the combination of input modalities representative of the user's current interaction behavior for performing the task to a behavior model representative of the user's past interaction behavior comprising a known combination of input modalities for performing the task used by the user during one or more previous user sessions to determine the identity of the user. 25. The system as recited in claim 14 , wherein the extracted features include features representative of a dialog state between the user and the system. | 0.826923 |
8,379,027 | 1 | 5 | 1. A method comprising: supplying a character code to a text rendering engine being tested to cause the text rendering engine to render a character corresponding to the character code, identifying a graphical reference image for the character, wherein the graphical reference image was produced by a source known to produce correct images; presenting the graphical reference image and the rendered character on a bitmapped display screen of a computer system to facilitate a visual comparison between a visual representation of the graphical reference image produced by the source known to produce correct images and a visual representation of the rendered character produced by the text rendering engine to be tested; receiving, by the computer system, user input indicating user evaluation of the graphical reference image and the rendered character, the user evaluation being based on the comparison between the visual representation of the graphical reference image and the visual representation of the rendered character, the user evaluation identifying one or more differences between the graphical reference image produced by the source known to produce correct images and the rendered character produced by the text rendering engine to be tested; and storing, by the computer system, the user evaluation in a database for subsequent debugging of the text rendering engine. | 1. A method comprising: supplying a character code to a text rendering engine being tested to cause the text rendering engine to render a character corresponding to the character code, identifying a graphical reference image for the character, wherein the graphical reference image was produced by a source known to produce correct images; presenting the graphical reference image and the rendered character on a bitmapped display screen of a computer system to facilitate a visual comparison between a visual representation of the graphical reference image produced by the source known to produce correct images and a visual representation of the rendered character produced by the text rendering engine to be tested; receiving, by the computer system, user input indicating user evaluation of the graphical reference image and the rendered character, the user evaluation being based on the comparison between the visual representation of the graphical reference image and the visual representation of the rendered character, the user evaluation identifying one or more differences between the graphical reference image produced by the source known to produce correct images and the rendered character produced by the text rendering engine to be tested; and storing, by the computer system, the user evaluation in a database for subsequent debugging of the text rendering engine. 5. The method of claim 1 , wherein a difference between the graphical reference image and the rendered character indicates at least one of an error in the graphical reference image, an error in encoding the rendered character, an error in a font of the rendered character or an error in a text rendering process. | 0.713235 |
8,239,287 | 14 | 20 | 14. A computer system for detecting associations between items represented in a data repository, the system comprising: a probabilistic analysis component comprising computer hardware, the probabilistic analysis component configured to: access user data comprising information about item selections by a plurality of users; and programmatically generate associations between first and second items based at least in part on the user data by at least: determining, from the user data, a first probability that first users selected both the first and second items, estimating, from the user data, a second probability that second users who selected the first item would have selected the second item based at least partly on a number of item selections other than the selection of the first item made by each of the second users, deriving an association score for the first and second items based at least in part on the first and second probabilities, the association score reflecting a degree of the association between the first and second items, and store the association in a data repository. | 14. A computer system for detecting associations between items represented in a data repository, the system comprising: a probabilistic analysis component comprising computer hardware, the probabilistic analysis component configured to: access user data comprising information about item selections by a plurality of users; and programmatically generate associations between first and second items based at least in part on the user data by at least: determining, from the user data, a first probability that first users selected both the first and second items, estimating, from the user data, a second probability that second users who selected the first item would have selected the second item based at least partly on a number of item selections other than the selection of the first item made by each of the second users, deriving an association score for the first and second items based at least in part on the first and second probabilities, the association score reflecting a degree of the association between the first and second items, and store the association in a data repository. 20. The system of claim 14 , wherein the computer system comprises multiple physical computers. | 0.9459 |
8,917,842 | 5 | 9 | 5. The method of claim 1 further including generating a prompt at said destination telephone to record an additional voice memo after the originating telephone hangs up and prior to hanging-up said destination telephone. | 5. The method of claim 1 further including generating a prompt at said destination telephone to record an additional voice memo after the originating telephone hangs up and prior to hanging-up said destination telephone. 9. The method of claim 5 further including receiving a call memo message that is recorded using a personal digital assistant (“PDA”) device and sent as a message to said call management system. | 0.939574 |
8,595,355 | 1 | 3 | 1. A method for processing requests from a computer network, said method comprising: accessing the computer network from a computer system; receiving a request from a requesting computer attached to the computer network, the request including a data string comprising a plurality of character codes from one of a plurality of character sets; storing the request; determining a character set used to encode the data string, based upon analysis of a character code of the character codes in the data string; converting the data string from the one of the character sets to a uniform language code; and storing the uniform language code. | 1. A method for processing requests from a computer network, said method comprising: accessing the computer network from a computer system; receiving a request from a requesting computer attached to the computer network, the request including a data string comprising a plurality of character codes from one of a plurality of character sets; storing the request; determining a character set used to encode the data string, based upon analysis of a character code of the character codes in the data string; converting the data string from the one of the character sets to a uniform language code; and storing the uniform language code. 3. The method of claim 1 , said method further comprising: reading the stored uniform language code; determining the character set of the requesting computer; converting the uniform language code into a character set specific return data string; and sending the character set specific return data string to the requesting computer. | 0.58625 |
8,874,555 | 1 | 5 | 1. A computer-implemented method comprising: calculating one or more time trend statistics for a plurality of quality of result statistics for a first document as a search result for a first query, each of the quality of result statistics corresponding to a different time period, the one or more time trend statistics estimating changes in the quality of result statistics over time, wherein each of the one or more time trend statistics comprises a quality of result difference between a first quality of result statistic for the first document as a search result for the first query during a first time period and a second quality of result statistic for the first document as a search result for the first query during a second time period; generating a first modified quality of result statistic by modifying a first quality of result statistic for the first document as a search result for the first query by a factor, where the factor is based on the one or more time trend statistics; and providing the first modified quality of result statistic as an input to a document ranking process for the first document and the first query. | 1. A computer-implemented method comprising: calculating one or more time trend statistics for a plurality of quality of result statistics for a first document as a search result for a first query, each of the quality of result statistics corresponding to a different time period, the one or more time trend statistics estimating changes in the quality of result statistics over time, wherein each of the one or more time trend statistics comprises a quality of result difference between a first quality of result statistic for the first document as a search result for the first query during a first time period and a second quality of result statistic for the first document as a search result for the first query during a second time period; generating a first modified quality of result statistic by modifying a first quality of result statistic for the first document as a search result for the first query by a factor, where the factor is based on the one or more time trend statistics; and providing the first modified quality of result statistic as an input to a document ranking process for the first document and the first query. 5. The method of claim 1 , further comprising determining that a first past version of the first document during the first time period is different from a second past version of the first document during the second time period before modifying the first quality of result statistic. | 0.814961 |
8,156,250 | 16 | 19 | 16. A non-transitory machine-accessible medium containing instructions which, when the instructions are executed by a computer system cause the computer system to perform operations, comprising: detecting a trigger in a transmission by executing program instruction in the computer system; receiving a transmission in a 3270 compatible format, the transmission including encapsulated browser compatible data; activating a browser engine in response to the detected trigger to by executing program instruction in the computer system; detecting if a page that is currently displayed relates to a last received page and taking corrective action when the current page is the last received page; and processing the browser compatible data with the browser engine by executing program instruction in the computer system. | 16. A non-transitory machine-accessible medium containing instructions which, when the instructions are executed by a computer system cause the computer system to perform operations, comprising: detecting a trigger in a transmission by executing program instruction in the computer system; receiving a transmission in a 3270 compatible format, the transmission including encapsulated browser compatible data; activating a browser engine in response to the detected trigger to by executing program instruction in the computer system; detecting if a page that is currently displayed relates to a last received page and taking corrective action when the current page is the last received page; and processing the browser compatible data with the browser engine by executing program instruction in the computer system. 19. The non-transitory machine-accessible medium of claim 16 , that when executed causes the computer system to accept input responsive to user interactions with a graphical user interface and to transmit the trigger in response to the user interaction. | 0.850296 |
9,471,772 | 12 | 14 | 12. The system of claim 10 , wherein the score is based on a function of the first and second similarity scores. | 12. The system of claim 10 , wherein the score is based on a function of the first and second similarity scores. 14. The system of claim 12 , wherein the operations further comprise determining a strength score for the first password and the score is further based on a function of the strength score. | 0.921207 |
9,390,108 | 7 | 8 | 7. A computer-implemented method for displaying information regarding a query to a user in a graphical user interface, the method comprising the steps of: executing the query, wherein the query comprises an object oriented query execution data structure that comprises a plurality of object oriented nodes, wherein each node includes an object oriented monitor method that enables collection of monitored data from the node and an object oriented dump method that outputs the monitored data; receiving the monitored data from the object oriented query execution data structure; and displaying to a user a graphical representation of the query execution data structure that allows the user to enable monitoring of the plurality of object oriented nodes on a node-by-node basis, to view information dumped from the query execution data structure as the query executes, and to perform query debug functions using the monitored data. | 7. A computer-implemented method for displaying information regarding a query to a user in a graphical user interface, the method comprising the steps of: executing the query, wherein the query comprises an object oriented query execution data structure that comprises a plurality of object oriented nodes, wherein each node includes an object oriented monitor method that enables collection of monitored data from the node and an object oriented dump method that outputs the monitored data; receiving the monitored data from the object oriented query execution data structure; and displaying to a user a graphical representation of the query execution data structure that allows the user to enable monitoring of the plurality of object oriented nodes on a node-by-node basis, to view information dumped from the query execution data structure as the query executes, and to perform query debug functions using the monitored data. 8. The method of claim 7 wherein the monitored data is written to a file, and the step of receiving the monitored data comprises the step of reading the file. | 0.824053 |
9,189,467 | 11 | 13 | 11. A non-transitory computer-readable medium having stored thereon instructions that are executable to cause a computer system to perform operations comprising: opening a first electronic document; creating a second electronic document superimposed over the first electronic document to receive annotations in reference to the first electronic document; creating, in response to creating the second electronic document to receive annotations, an association between the first electronic document and the second electronic document; saving the second document as a separate document independent of the first document; in response to a second opening of the first electronic document, and based upon the association between the first electronic document and the second electronic document that was annotated during a previous viewing of the first electronic document, automatically opening the second electronic document without user intervention; superimposing the second electronic document with the annotations over the first electronic document; and receiving additional annotations in reference to the first electronic document within the second electronic document concurrently while viewing the first electronic document beneath the second electronic document. | 11. A non-transitory computer-readable medium having stored thereon instructions that are executable to cause a computer system to perform operations comprising: opening a first electronic document; creating a second electronic document superimposed over the first electronic document to receive annotations in reference to the first electronic document; creating, in response to creating the second electronic document to receive annotations, an association between the first electronic document and the second electronic document; saving the second document as a separate document independent of the first document; in response to a second opening of the first electronic document, and based upon the association between the first electronic document and the second electronic document that was annotated during a previous viewing of the first electronic document, automatically opening the second electronic document without user intervention; superimposing the second electronic document with the annotations over the first electronic document; and receiving additional annotations in reference to the first electronic document within the second electronic document concurrently while viewing the first electronic document beneath the second electronic document. 13. The non-transitory computer-readable medium of claim 11 , wherein the first electronic document and second electronic document are of a same file type. | 0.803797 |
8,868,410 | 1 | 2 | 1. A non-dialogue-based learning apparatus, comprising: a speech input unit for inputting speeches; a speech recognition unit for recognizing the input speech; and a behavior and dialogue controller for controlling behaviors and dialogues according to speech recognition results, wherein the behavior and dialogue controller has a topic recognition expert to memorise contents of utterances and to retrieve the topic that best matches the speech recognition results, and a mode switching expert to control mode switching, wherein the mode switching expert switches operation modes of the apparatus between a learning mode and an execution mode in accordance with a command in the recognized speech input through a user utterance, wherein in the learning mode, the topic recognition expert creates a word graph from each speech that the user utters as corresponding to a new topic, the word graph being a plurality of candidate words or sentences that are constructed by words contained in a predetermined dictionary together with associated matching probabilities with respect to the uttered speech, and the topic recognition expert registers each of words constituting the word graph together with associated occurrence frequencies as representing said new topic, the topic recognition expert registering, as representing said new topic, words and associated occurrence frequencies generated by a plurality of the word graphs when the user utters plural speeches as corresponding to the new topic, and wherein in the execution mode, the topic recognition expert performs searches from among topics that have been registered in the learning mode, and selects the maximum likelihood topic. | 1. A non-dialogue-based learning apparatus, comprising: a speech input unit for inputting speeches; a speech recognition unit for recognizing the input speech; and a behavior and dialogue controller for controlling behaviors and dialogues according to speech recognition results, wherein the behavior and dialogue controller has a topic recognition expert to memorise contents of utterances and to retrieve the topic that best matches the speech recognition results, and a mode switching expert to control mode switching, wherein the mode switching expert switches operation modes of the apparatus between a learning mode and an execution mode in accordance with a command in the recognized speech input through a user utterance, wherein in the learning mode, the topic recognition expert creates a word graph from each speech that the user utters as corresponding to a new topic, the word graph being a plurality of candidate words or sentences that are constructed by words contained in a predetermined dictionary together with associated matching probabilities with respect to the uttered speech, and the topic recognition expert registers each of words constituting the word graph together with associated occurrence frequencies as representing said new topic, the topic recognition expert registering, as representing said new topic, words and associated occurrence frequencies generated by a plurality of the word graphs when the user utters plural speeches as corresponding to the new topic, and wherein in the execution mode, the topic recognition expert performs searches from among topics that have been registered in the learning mode, and selects the maximum likelihood topic. 2. The non-dialogue-based learning apparatus of claim 1 , wherein in the learning mode, when the user's speech includes a plurality of words undefined in the predetermined dictionary, each undefined word is substituted with words defined in the dictionary or a combination of words defined in the dictionary having a similar phoneme sequence when the topic recognition expert creates the word graph therefor. | 0.75122 |
8,560,301 | 7 | 9 | 7. The apparatus of claim 1 , further comprising: an information collecting unit configured to collect the sensing information from the user's environment through at least one of a hardware sensor and a software sensor. | 7. The apparatus of claim 1 , further comprising: an information collecting unit configured to collect the sensing information from the user's environment through at least one of a hardware sensor and a software sensor. 9. The apparatus of claim 7 , wherein the software sensor collects sensing data from at least one of an electronic calendar application, a scheduler application, an e-mail management application, a message management application, a communication application, a social network application, and a web site management application. | 0.8459 |
10,095,766 | 12 | 14 | 12. One or more non-transitory computer readable storage mediums storing one or more sequences of instructions, which when executed by one or more processors, causes automatic refinement of a template warehouse star schema, by performing the steps of: automatically comparing a customized source database with a baseline template warehouse to gather a custom information selected from i) a data type or a length of custom columns, ii) frequently used expressions on custom columns, iii) custom entities or information on constituent columns of the custom entities, iv) custom entity keys, v) relationships of custom entities with other entities, vi) relationships within custom entities, vii) a contribution of custom entities to specialized entities, viii) an update frequency of custom entities and columns, ix) a grouping of custom entity and column updates, and x) a contribution of custom entities and columns to workflows and correlated attributes, wherein the custom information is gathered based on a combination of a usage analysis technique by automatically analyzing queries on the custom source database, a data profiling technique by automatically analyzing data in the custom source database and an operational reporting environment analysis technique by automatically analyzing an underlying model and queries in operational reports; automatically determining facts by determining frequently updated numeric non key custom columns and the custom entities; automatically determining dimensions by determining non-numeric custom columns and the custom entities that are infrequently updated and that are linked to the facts by foreign keys; automatically determining dimension hierarchies based on hierarchical relationships within custom entities determined to be dimensions; and automatically suggesting at least one of (i) subsumption of a custom entity into an entity of the template warehouse star schema based on a type of mapped target of related entities, a column type of the custom entity or an update frequency of the custom entity if the custom entity is related to the entity of the template warehouse star schema, (ii) creation of new facts or dimensions for the custom entity based on a column type of the custom entity or an update frequency of the custom entity if the custom entity is a standalone entity, or (iii) creation of specialized entities or augmentation of specialized entities in the template warehouse star schema if the custom entity is related to a currency or a unit of measure. | 12. One or more non-transitory computer readable storage mediums storing one or more sequences of instructions, which when executed by one or more processors, causes automatic refinement of a template warehouse star schema, by performing the steps of: automatically comparing a customized source database with a baseline template warehouse to gather a custom information selected from i) a data type or a length of custom columns, ii) frequently used expressions on custom columns, iii) custom entities or information on constituent columns of the custom entities, iv) custom entity keys, v) relationships of custom entities with other entities, vi) relationships within custom entities, vii) a contribution of custom entities to specialized entities, viii) an update frequency of custom entities and columns, ix) a grouping of custom entity and column updates, and x) a contribution of custom entities and columns to workflows and correlated attributes, wherein the custom information is gathered based on a combination of a usage analysis technique by automatically analyzing queries on the custom source database, a data profiling technique by automatically analyzing data in the custom source database and an operational reporting environment analysis technique by automatically analyzing an underlying model and queries in operational reports; automatically determining facts by determining frequently updated numeric non key custom columns and the custom entities; automatically determining dimensions by determining non-numeric custom columns and the custom entities that are infrequently updated and that are linked to the facts by foreign keys; automatically determining dimension hierarchies based on hierarchical relationships within custom entities determined to be dimensions; and automatically suggesting at least one of (i) subsumption of a custom entity into an entity of the template warehouse star schema based on a type of mapped target of related entities, a column type of the custom entity or an update frequency of the custom entity if the custom entity is related to the entity of the template warehouse star schema, (ii) creation of new facts or dimensions for the custom entity based on a column type of the custom entity or an update frequency of the custom entity if the custom entity is a standalone entity, or (iii) creation of specialized entities or augmentation of specialized entities in the template warehouse star schema if the custom entity is related to a currency or a unit of measure. 14. The one or more non-transitory computer readable storage mediums storing one or more sequences of instructions of claim 12 , which when executed by the one or more processors further causes (a) automatic incorporation of new workflow specific measures into the template warehouse if the custom entities or the custom columns contribute to workflows or workflow correlated attributes, or (b) automatic suggestion of workflow specific measures and workflow correlated attributes specific measures that are selected from a group comprising (i) temporal measures based on custom temporal attributes and the workflow stage, (ii) progress tracking measures based on custom progress tracking attributes and the workflow stage, (iii) priority related measures based on custom priority related attributes and the workflow stage, or (iv) ownership related measures based on custom ownership related attributes and the workflow stage. | 0.674508 |
4,852,171 | 15 | 16 | 15. Method as claimed in claim 14, comprising the further step of: providing a bias template having a plurality of positions corresponding to said plurality of positions of said utterance template each said position having a value stored therein, said stored value representing the probability of a particular binary value occurring in the corresponding position in any said utterance template; and establishing a second score, indicative of a relative match between said bias template and said utterance template, by adding the values stored in the positions of the bias template corresponding to positions of the utterance template having the particular binary value stored therein. | 15. Method as claimed in claim 14, comprising the further step of: providing a bias template having a plurality of positions corresponding to said plurality of positions of said utterance template each said position having a value stored therein, said stored value representing the probability of a particular binary value occurring in the corresponding position in any said utterance template; and establishing a second score, indicative of a relative match between said bias template and said utterance template, by adding the values stored in the positions of the bias template corresponding to positions of the utterance template having the particular binary value stored therein. 16. Method as claimed in claim 15, wherein said second score establishing step includes: providing outputs from said bias template, each output corresponding to one of said plurality of positions of said bias template and having a value determined by the value stored therein; and summing the outputs of the bias template corresponding to positions of the utterance template having the particular binary value stored therein. | 0.901666 |
8,868,431 | 2 | 3 | 2. The recognition dictionary creation device according to claim 1 , wherein said language identification unit outputs a predetermined number of languages each having a score showing a higher likelihood that the reading of said target text to be registered is described in the language, among a plurality of languages which are targets for language identification, as results of the identification, said reading addition unit adds a reading with phonemes in each of said predetermined number of languages identified by said language identification unit to said target text to be registered, and said reading conversion unit converts the reading of said target text to be registered from the phonemes in each of said predetermined number of languages identified by said language identification unit to phonemes in the language to be recognized. | 2. The recognition dictionary creation device according to claim 1 , wherein said language identification unit outputs a predetermined number of languages each having a score showing a higher likelihood that the reading of said target text to be registered is described in the language, among a plurality of languages which are targets for language identification, as results of the identification, said reading addition unit adds a reading with phonemes in each of said predetermined number of languages identified by said language identification unit to said target text to be registered, and said reading conversion unit converts the reading of said target text to be registered from the phonemes in each of said predetermined number of languages identified by said language identification unit to phonemes in the language to be recognized. 3. The recognition dictionary creation device according to claim 2 , wherein said language identification unit outputs said language to be recognized as a result of the identification when said score is smaller than a predetermined threshold. | 0.943825 |
9,372,942 | 1 | 7 | 1. A method comprising: on at least one server computer, receiving a request for data visualization; wherein the request specifies input data, at least one user query, and a data-visualization type; inferring, by the at least one server computer without user-modification of the request, at least one additional query not specified by the at least one user query based on a user-interface (UI) range of freedom associated with the request, wherein the UI range of freedom encompasses a plurality of modifications collectively enabled by a plurality of UI controls of a visualization interface on which the requested visualization will be served; the at least one server computer causing a map-reduce framework to process the input data according to the at least one user query and the at least one additional query, the causing yielding resultant data; the at least one server computer storing in a cache a portion of the resultant data that relates to the at least one additional query; the at least one server computer generating the requested data visualization based on a portion of the resultant data that relates to the at least one user query; and serving the requested visualization on the visualization interface. | 1. A method comprising: on at least one server computer, receiving a request for data visualization; wherein the request specifies input data, at least one user query, and a data-visualization type; inferring, by the at least one server computer without user-modification of the request, at least one additional query not specified by the at least one user query based on a user-interface (UI) range of freedom associated with the request, wherein the UI range of freedom encompasses a plurality of modifications collectively enabled by a plurality of UI controls of a visualization interface on which the requested visualization will be served; the at least one server computer causing a map-reduce framework to process the input data according to the at least one user query and the at least one additional query, the causing yielding resultant data; the at least one server computer storing in a cache a portion of the resultant data that relates to the at least one additional query; the at least one server computer generating the requested data visualization based on a portion of the resultant data that relates to the at least one user query; and serving the requested visualization on the visualization interface. 7. The method of claim 1 , wherein: the UI range of freedom comprises at least one additional data relationship; and fashioning at least one query related to the at least one additional data relationship. | 0.807183 |
8,316,302 | 16 | 17 | 16. The apparatus of claim 11 wherein the processor is further configured to present the user with a plurality of different user-selectable operational states defining a mode in which the speech request is to be received. | 16. The apparatus of claim 11 wherein the processor is further configured to present the user with a plurality of different user-selectable operational states defining a mode in which the speech request is to be received. 17. The apparatus of claim 16 wherein the user-selectable operational states are presented as a GUI on the display device. | 0.911337 |
7,639,257 | 69 | 71 | 69. The computer readable medium encoded with the computer program of claim 65 , wherein: the reference includes one or more out-of-band values not defined in an encoding standard. | 69. The computer readable medium encoded with the computer program of claim 65 , wherein: the reference includes one or more out-of-band values not defined in an encoding standard. 71. The computer readable medium encoded with the computer program of claim 69 , the computer program further comprising instructions that when executed by the programmable processor cause the programmable processor to perform operations comprising: embedding the identified glyphlet in the text document; wherein the one or more out-of-band values are uniquely associated with the identified glyphlet. | 0.895584 |
8,886,581 | 1 | 9 | 1. A method for predicting affective response of a user to a stream of token instances, comprising: receiving the stream of token instances; partitioning the stream of token instances into consecutive temporal windows of token instances; wherein the temporal windows of token instances are ordered according to their start time; predicting affective response of the user to a first temporal window of token instances by providing a machine learning-based predictor with input data comprising: a vector of values derived from the first temporal window of token instances, and an initial state value derived from a value of a measurement channel of the user taken at time corresponding to start of the first temporal window of token instances; and for each successive temporal window of token instances after the first temporal window of token instances: predicting affective response of the user to the successive temporal window of token instances by providing the machine learning-based predictor with input data comprising: a vector of values derived from the successive temporal window of token instances, and an initial state value derived from prediction of affective response of the user to a temporal window of token instances preceding the successive temporal window of token instances. | 1. A method for predicting affective response of a user to a stream of token instances, comprising: receiving the stream of token instances; partitioning the stream of token instances into consecutive temporal windows of token instances; wherein the temporal windows of token instances are ordered according to their start time; predicting affective response of the user to a first temporal window of token instances by providing a machine learning-based predictor with input data comprising: a vector of values derived from the first temporal window of token instances, and an initial state value derived from a value of a measurement channel of the user taken at time corresponding to start of the first temporal window of token instances; and for each successive temporal window of token instances after the first temporal window of token instances: predicting affective response of the user to the successive temporal window of token instances by providing the machine learning-based predictor with input data comprising: a vector of values derived from the successive temporal window of token instances, and an initial state value derived from prediction of affective response of the user to a temporal window of token instances preceding the successive temporal window of token instances. 9. The method of claim 1 , wherein the machine learning-based predictor utilizes a maximum entropy model classifier. | 0.905074 |
7,765,201 | 4 | 6 | 4. The apparatus according to claim 1 , further comprising a third interface unit configured to provide a user with a user interface screen for causing the user to specify the concatenation condition, the third interface unit storing in the storage unit the tuning information including the concatenation condition specified by the user on the user interface screen. | 4. The apparatus according to claim 1 , further comprising a third interface unit configured to provide a user with a user interface screen for causing the user to specify the concatenation condition, the third interface unit storing in the storage unit the tuning information including the concatenation condition specified by the user on the user interface screen. 6. The apparatus according to claim 4 , wherein: the user interface screen includes an area which causes a user to specify a maximum number of lines of a document which is usable for concatenation by the concatenation unit, and when the number of lines of a document to be concatenated exceeds the maximum number of lines specified by the user on the user interface screen, the concatenation unit uses an abstract of the document instead of the document to be concatenated. | 0.927409 |
9,070,103 | 3 | 4 | 3. A method of processing legal information, the method comprising: assigning to each of a plurality of documents which each comprises legal information relating to a plurality of legal topics at least one identifier associated with (a) at least one of the legal topics and (b) at least one of a plurality of types of legal information; formatting the documents according to a protocol; storing the formatted documents in at least one database; checking formatted documents for compliance with a document receiving protocol; generating a notice of defects automatically upon determination that a checked document does not comply with the protocol; using identifiers associated with the stored documents to identify documents within the at least one database responsive to a request received from a user terminal for information related to at least one of the plurality of legal topics; and causing legal information associated with the identified documents to be provided for display on the display device, automatically tabulated by type according to the identifiers associated with the respective identified documents. | 3. A method of processing legal information, the method comprising: assigning to each of a plurality of documents which each comprises legal information relating to a plurality of legal topics at least one identifier associated with (a) at least one of the legal topics and (b) at least one of a plurality of types of legal information; formatting the documents according to a protocol; storing the formatted documents in at least one database; checking formatted documents for compliance with a document receiving protocol; generating a notice of defects automatically upon determination that a checked document does not comply with the protocol; using identifiers associated with the stored documents to identify documents within the at least one database responsive to a request received from a user terminal for information related to at least one of the plurality of legal topics; and causing legal information associated with the identified documents to be provided for display on the display device, automatically tabulated by type according to the identifiers associated with the respective identified documents. 4. The method of claim 3 , comprising automatically performing the assigning and formatting steps on the non-complying document. | 0.503876 |
9,372,853 | 1 | 11 | 1. A method comprising: preprocessing a document to be translated by a translation service by: identifying in the document information which is not to be translated, removing the not to be translated information from the document, associating each one unit of the not to be translated information with one placeholder which holds a place for the one unit of not to be translated in the document, replacing the not to be translated information with placeholders in the document, and storing the not to be translated information as metadata; sending the preprocessed document to the translation service for translation; receiving a translated version of the preprocessed document from the translation service; and postprocessing the received translated document by: retrieving the stored metadata, and replacing each one of the placeholders with its associated one unit of not to be translated information. | 1. A method comprising: preprocessing a document to be translated by a translation service by: identifying in the document information which is not to be translated, removing the not to be translated information from the document, associating each one unit of the not to be translated information with one placeholder which holds a place for the one unit of not to be translated in the document, replacing the not to be translated information with placeholders in the document, and storing the not to be translated information as metadata; sending the preprocessed document to the translation service for translation; receiving a translated version of the preprocessed document from the translation service; and postprocessing the received translated document by: retrieving the stored metadata, and replacing each one of the placeholders with its associated one unit of not to be translated information. 11. The method according to claim 1 and also comprising: adding at least one random bogus sentence or at least one random bogus clause to the document; and storing in the metadata an identity and a location of the at least one random bogus sentence or the at least one random bogus clause which were added to the document. | 0.75569 |
9,563,680 | 12 | 13 | 12. The method of claim 1 wherein after converting the document from the first format to the second format, the method further comprising: the at least one static object associated with the converted document is rendered in the converted document when the converted document is accessed via the at least one web portal, the at least one static object including at least one of a multi-media file, a glossary, an index and a table of contents. | 12. The method of claim 1 wherein after converting the document from the first format to the second format, the method further comprising: the at least one static object associated with the converted document is rendered in the converted document when the converted document is accessed via the at least one web portal, the at least one static object including at least one of a multi-media file, a glossary, an index and a table of contents. 13. The method of claim 12 wherein the at least one static object is associated with an identifier and the method further comprises generating a static object mapping file associated with the document that maps the identifier of the at least one static object to at least one of the document and a location in the document and transmitting the static object mapping file to the at least one web portal. | 0.838813 |
8,639,552 | 24 | 25 | 24. The system of claim 22 , wherein the task analytics module is programmed to generate a graphical map of task participant relationships. | 24. The system of claim 22 , wherein the task analytics module is programmed to generate a graphical map of task participant relationships. 25. The system of claim 24 , wherein the graphical map is generated by identifying, for each task participant, other task participants that have participated in one or more tasks with the task participant. | 0.911101 |
9,646,612 | 17 | 18 | 17. The non-transitory computer-readable storage medium of claim 15 , wherein the operations further comprise: allowing the user-provided hotword associated with the action to be used in further communication with the computing device. | 17. The non-transitory computer-readable storage medium of claim 15 , wherein the operations further comprise: allowing the user-provided hotword associated with the action to be used in further communication with the computing device. 18. The non-transitory computer-readable storage medium of claim 17 , wherein allowing the user-provided hotword associated with the action to be used in further communication with the computing device comprises: allowing the user-provided hotword to initiate the action, associated with the user-provided hotword, on the computing device. | 0.843779 |
8,234,262 | 1 | 28 | 1. A method comprising: detecting an inaudible physiological reaction by a person to an instance of at least two instances of a displayed first content, the at least two instances of the first content having a common contextual attribute, the detecting being performed while the person is viewing at least one of the at least two instances of the displayed first content; determining a content attribute of the instance of the at least two instances of the displayed first content that is at least substantially absent from other instances of the at least two instances of the displayed first content; initiating a search for a second content using a search parameter corresponding to the detected inaudible physiological reaction and to the determined content attribute of the instance; selecting the second content from a result of the initiated search, the selecting being automated and performed at least in part with a processing device; and facilitating a display of the selected second content in a manner perceivable by the person. | 1. A method comprising: detecting an inaudible physiological reaction by a person to an instance of at least two instances of a displayed first content, the at least two instances of the first content having a common contextual attribute, the detecting being performed while the person is viewing at least one of the at least two instances of the displayed first content; determining a content attribute of the instance of the at least two instances of the displayed first content that is at least substantially absent from other instances of the at least two instances of the displayed first content; initiating a search for a second content using a search parameter corresponding to the detected inaudible physiological reaction and to the determined content attribute of the instance; selecting the second content from a result of the initiated search, the selecting being automated and performed at least in part with a processing device; and facilitating a display of the selected second content in a manner perceivable by the person. 28. The method of claim 1 , wherein the initiating a search for a second content using a search parameter corresponding to the detected inaudible physiological reaction and to the determined content attribute of the instance further includes: initiating a search for a second content using a search parameter corresponding to the detected inaudible physiological reaction and to a determined content attribute of the instance of the at least two instances of the displayed first content. | 0.765188 |
7,774,207 | 1 | 2 | 1. A computer-readable medium storing computer-executable instructions that, when executed by a processor of a server, cause the server to: receive intellectual property asset data corresponding to a plurality of intellectual property assets; assess, based on the intellectual property asset data, an enforcement potential of each intellectual property asset of a subset of the plurality of intellectual property assets, wherein the subset of the plurality of intellectual property assets includes multiple intellectual property assets, and wherein assessing the enforcement potential includes analyzing information specific to each intellectual property asset of the subset of the plurality of intellectual property assets; and determine, based on the intellectual property asset data, an enforcement priority of each intellectual property asset of the subset of the plurality of intellectual property assets, wherein the determined enforcement priority provides a basis of comparison among the subset of the plurality of intellectual property assets for selecting for enforcement at least one intellectual property asset among the subset of the plurality of intellectual property assets. | 1. A computer-readable medium storing computer-executable instructions that, when executed by a processor of a server, cause the server to: receive intellectual property asset data corresponding to a plurality of intellectual property assets; assess, based on the intellectual property asset data, an enforcement potential of each intellectual property asset of a subset of the plurality of intellectual property assets, wherein the subset of the plurality of intellectual property assets includes multiple intellectual property assets, and wherein assessing the enforcement potential includes analyzing information specific to each intellectual property asset of the subset of the plurality of intellectual property assets; and determine, based on the intellectual property asset data, an enforcement priority of each intellectual property asset of the subset of the plurality of intellectual property assets, wherein the determined enforcement priority provides a basis of comparison among the subset of the plurality of intellectual property assets for selecting for enforcement at least one intellectual property asset among the subset of the plurality of intellectual property assets. 2. The computer-readable medium of claim 1 , containing further computer-executable instructions that, when executed by the processor of the server, cause the server to select an intellectual property asset for enforcement based at least in part on a determined enforcement priority of an intellectual property asset of the subset of the plurality of intellectual property assets. | 0.759798 |
7,720,720 | 1 | 11 | 1. A computer-based method of generating recommendations for potential purchase by a customer, comprising: generating association rules from a transaction history data set; receiving a recommendation context from a customer; using the recommendation context at a computer system to identify a plurality of candidate recommendation rules from the association rules that match the recommendation context, where each candidate recommendation rule recommends at least one recommended item; determining a score for each candidate recommendation rule using a margin value factor for the recommended item, a confidence value factor for the candidate recommendation rule and a predetermined scoring criteria factor; ranking the plurality of candidate recommendation rules using the score for each candidate recommendation rule to identify at least a highest ranking candidate recommendation rule; and issuing at least the highest ranking candidate recommendation rule as a recommendation. | 1. A computer-based method of generating recommendations for potential purchase by a customer, comprising: generating association rules from a transaction history data set; receiving a recommendation context from a customer; using the recommendation context at a computer system to identify a plurality of candidate recommendation rules from the association rules that match the recommendation context, where each candidate recommendation rule recommends at least one recommended item; determining a score for each candidate recommendation rule using a margin value factor for the recommended item, a confidence value factor for the candidate recommendation rule and a predetermined scoring criteria factor; ranking the plurality of candidate recommendation rules using the score for each candidate recommendation rule to identify at least a highest ranking candidate recommendation rule; and issuing at least the highest ranking candidate recommendation rule as a recommendation. 11. The method of claim 1 , where the step of determining a score for each candidate recommendation rule comprises increasing the predetermined scoring criteria factor if there is a marketing emphasis for the recommended item. | 0.682584 |
8,917,853 | 15 | 17 | 15. A non-transitory computer readable storage medium storing a program of instructions executable by a machine to perform a method for enhancing problem resolution at a call center based on speech recognition of a caller, comprising: receiving an incoming call and generating call data based on speech recognition of the incoming call using a computer having a processor; generating and associating annotated metadata about the call data; creating a historical record including the call data and the annotated metadata, the historical record being stored in a storage medium communicating with the computer, the historical record storing solutions associated with the call data and the annotated metadata and indexing callers by issues identified by the call data and the annotated metadata; generating context data for the incoming call by analyzing the historical record to identify: a caller, a topic, a date and a stress level of the caller; comparing the context data to historical records of previous calls; conducting a topic probabilities analysis by comparing the context data to the historical records of previous calls; assigning a weight to each solution in the historical record based on likelihood of success derived from the annotated metadata; and determining a solution for the topic based on the probabilities analysis and further based on the assigned weight of each solution. | 15. A non-transitory computer readable storage medium storing a program of instructions executable by a machine to perform a method for enhancing problem resolution at a call center based on speech recognition of a caller, comprising: receiving an incoming call and generating call data based on speech recognition of the incoming call using a computer having a processor; generating and associating annotated metadata about the call data; creating a historical record including the call data and the annotated metadata, the historical record being stored in a storage medium communicating with the computer, the historical record storing solutions associated with the call data and the annotated metadata and indexing callers by issues identified by the call data and the annotated metadata; generating context data for the incoming call by analyzing the historical record to identify: a caller, a topic, a date and a stress level of the caller; comparing the context data to historical records of previous calls; conducting a topic probabilities analysis by comparing the context data to the historical records of previous calls; assigning a weight to each solution in the historical record based on likelihood of success derived from the annotated metadata; and determining a solution for the topic based on the probabilities analysis and further based on the assigned weight of each solution. 17. The non-transitory computer readable storage medium of claim 15 , wherein a learning phase includes iteratively conducting the topic probabilities analysis to provide a plurality of solutions associated with the topic, saving the plurality of solutions associated with the topic in the historical record. | 0.501618 |
7,836,108 | 1 | 8 | 1. An automated method, comprising: identifying documents in a current clustering operation; assigning the identified documents to one or more clusters; selecting a current representative document for each of the one or more clusters; determining whether the current representative document has been re-crawled; determining a previous representative document with which the current representative document was previously associated in a prior clustering operation, if it is determined that the current representative document has not been re-crawled; determining one of the one or more clusters to which the previous representative document has been assigned in the current clustering operation; combining one of the one or more clusters associated with the current representative document that has not been re-crawled with the one of the one or more clusters associated with the previous representative document into a combined cluster; and storing information regarding the combined cluster. | 1. An automated method, comprising: identifying documents in a current clustering operation; assigning the identified documents to one or more clusters; selecting a current representative document for each of the one or more clusters; determining whether the current representative document has been re-crawled; determining a previous representative document with which the current representative document was previously associated in a prior clustering operation, if it is determined that the current representative document has not been re-crawled; determining one of the one or more clusters to which the previous representative document has been assigned in the current clustering operation; combining one of the one or more clusters associated with the current representative document that has not been re-crawled with the one of the one or more clusters associated with the previous representative document into a combined cluster; and storing information regarding the combined cluster. 8. The automated method of claim 1 , further comprising: generating a measure of quality for each of the identified documents; and selecting the current representative document for each of the one or more clusters based on the measure of quality. | 0.789744 |
9,148,458 | 1 | 15 | 1. A system, comprising: a data input to receive multi-dimensional, non-parametric data obtained from mobile devices; a processor to parametrize the multi-dimensional, non-parametric data into a plurality of different abstraction layers as multi-layered data; a memory to store the parametrized data; an aggregation apparatus configured to aggregate the parametrized data in batches using at least one of time-series, averaging, or sum operations, the aggregation is performed relative to at least one of (1) a time period, (2) a location, (3) a mobile application, (4) a mobile application category, (5) a mobile user, or (6) a mobile user group, the aggregation apparatus is to determine, from at least one of the data batches, at least one of: descriptive higher-level behavioral indicators for the mobile devices, or descriptive higher-level technical indicators for the mobile devices, the aggregation apparatus is configured to be activated based on at least one of a sufficient amount of data becoming available or a trigger; and a data export entity to provide at least one of the behavioral indicators or the technical indicators to an external entity for determining at least one of personalized content or a network performance optimization for at least one of the mobile devices. | 1. A system, comprising: a data input to receive multi-dimensional, non-parametric data obtained from mobile devices; a processor to parametrize the multi-dimensional, non-parametric data into a plurality of different abstraction layers as multi-layered data; a memory to store the parametrized data; an aggregation apparatus configured to aggregate the parametrized data in batches using at least one of time-series, averaging, or sum operations, the aggregation is performed relative to at least one of (1) a time period, (2) a location, (3) a mobile application, (4) a mobile application category, (5) a mobile user, or (6) a mobile user group, the aggregation apparatus is to determine, from at least one of the data batches, at least one of: descriptive higher-level behavioral indicators for the mobile devices, or descriptive higher-level technical indicators for the mobile devices, the aggregation apparatus is configured to be activated based on at least one of a sufficient amount of data becoming available or a trigger; and a data export entity to provide at least one of the behavioral indicators or the technical indicators to an external entity for determining at least one of personalized content or a network performance optimization for at least one of the mobile devices. 15. The system of claim 1 , wherein the aggregation apparatus includes a semantic model entity to associate semantic data including at least one of movement, location names, nature of locations, application consumed, or data type consumed with received non-parametric data for enabling natural language oriented semantic data queries. | 0.598558 |
9,256,453 | 8 | 12 | 8. A computing device for extending functions of a 3D modeling system, the computing device comprising: a network interface coupled to a communication network; a processor; a memory coupled to the processor; a display device coupled to the processor; a browser application stored in the memory that executes on the processor to retrieve content from remote hosts via the communication network interface and render the retrieved content on the display device; and a 3D modeling software module stored in the memory that executes on the processor, the 3D modeling software module including: an interface module that executes on the processor to receive user commands from a text area of the browser application, the user commands defining a script including functions to modify or create one or more components of a 3D model and cause a rendering of the 3D model to be displayed in a window controlled by the browser application, and wherein the script is associated with user profile information for a user; a modeling engine module that executes on the processor as a compiled plug-in component of the browser application, wherein the modeling engine module extends the functionality of the browser application and the modeling engine module includes functions that cause the processor to interpret model data corresponding to the 3D model and to render the 3D model on the display device in accordance with the script; and a script interface layer that executes on the processor to expose the modeling engine functions to the interface module for use by the script functions, wherein the script functions extend the modeling engine module functions, the commands defining the script include functions of the modeling engine module, the user commands defining the script are associated with an interactive control of a user interface of the browser application, and activation of the interactive control causes the processor to execute the user commands defined in the script. | 8. A computing device for extending functions of a 3D modeling system, the computing device comprising: a network interface coupled to a communication network; a processor; a memory coupled to the processor; a display device coupled to the processor; a browser application stored in the memory that executes on the processor to retrieve content from remote hosts via the communication network interface and render the retrieved content on the display device; and a 3D modeling software module stored in the memory that executes on the processor, the 3D modeling software module including: an interface module that executes on the processor to receive user commands from a text area of the browser application, the user commands defining a script including functions to modify or create one or more components of a 3D model and cause a rendering of the 3D model to be displayed in a window controlled by the browser application, and wherein the script is associated with user profile information for a user; a modeling engine module that executes on the processor as a compiled plug-in component of the browser application, wherein the modeling engine module extends the functionality of the browser application and the modeling engine module includes functions that cause the processor to interpret model data corresponding to the 3D model and to render the 3D model on the display device in accordance with the script; and a script interface layer that executes on the processor to expose the modeling engine functions to the interface module for use by the script functions, wherein the script functions extend the modeling engine module functions, the commands defining the script include functions of the modeling engine module, the user commands defining the script are associated with an interactive control of a user interface of the browser application, and activation of the interactive control causes the processor to execute the user commands defined in the script. 12. The computing device of claim 8 , wherein the interface module includes a function that executes on the processor to add an interactive control within a window of the browser application, the interactive control corresponding to the script to draw, format, or edit components of the 3D model. | 0.501684 |
7,555,711 | 6 | 10 | 6. The method of claim 4 , wherein the identifying of at least one boundary comprises determining the extendable ones of the bounding rectangles. | 6. The method of claim 4 , wherein the identifying of at least one boundary comprises determining the extendable ones of the bounding rectangles. 10. The method of claim 6 , wherein the identifying of at least one boundary comprises extending to the candidate boundary respective edges of the bounding rectangles determined to be extendable. | 0.875 |
9,965,459 | 8 | 9 | 8. A computer-readable medium storing instructions, the instructions comprising: one or more instructions that, when executed by one or more processors, cause the one or more processors to: receive a source document to be processed for contextual information relating to the source document; provide, for display on a representation of the source document on a user interface, a first input mechanism for a user; identify, based on a first user interaction with the first input mechanism, a first named entity included in the source document; identify, based on a second user interaction with the first input mechanism, a context associated with the source document by using context terms, of the source document, that are different than the first named entity; provide the first named entity as a search query; identify a first reference document based on providing the first named entity as the search query, the first reference document being associated with a result of the search query, and the first reference document being different from the source document; identify a second named entity included in the source document; identify a second reference document associated with the second named entity; analyze the first reference document and the second reference document; classify the first named entity as a primary entity based on analyzing the first reference document and the second reference document; classify the second named entity as a secondary entity based on analyzing the first reference document and the second reference document; perform a semantic similarity analysis using the context associated with the source document and based on classifying the first named entity as the primary entity and the second named entity as the secondary entity; provide, for display on the user interface, a second input mechanism for the user to cause contextual information to be provided; and identify contextual information, based on performing the semantic similarity analysis and based on a third user interaction with the second input mechanism, the contextual information including one or more reference text sections having a threshold semantic similarity score with respect to the secondary entity and the context associated with the source document, and not being included in the source document. | 8. A computer-readable medium storing instructions, the instructions comprising: one or more instructions that, when executed by one or more processors, cause the one or more processors to: receive a source document to be processed for contextual information relating to the source document; provide, for display on a representation of the source document on a user interface, a first input mechanism for a user; identify, based on a first user interaction with the first input mechanism, a first named entity included in the source document; identify, based on a second user interaction with the first input mechanism, a context associated with the source document by using context terms, of the source document, that are different than the first named entity; provide the first named entity as a search query; identify a first reference document based on providing the first named entity as the search query, the first reference document being associated with a result of the search query, and the first reference document being different from the source document; identify a second named entity included in the source document; identify a second reference document associated with the second named entity; analyze the first reference document and the second reference document; classify the first named entity as a primary entity based on analyzing the first reference document and the second reference document; classify the second named entity as a secondary entity based on analyzing the first reference document and the second reference document; perform a semantic similarity analysis using the context associated with the source document and based on classifying the first named entity as the primary entity and the second named entity as the secondary entity; provide, for display on the user interface, a second input mechanism for the user to cause contextual information to be provided; and identify contextual information, based on performing the semantic similarity analysis and based on a third user interaction with the second input mechanism, the contextual information including one or more reference text sections having a threshold semantic similarity score with respect to the secondary entity and the context associated with the source document, and not being included in the source document. 9. The computer-readable medium of claim 8 , where the one or more instructions, that cause the one or more processors to perform the semantic similarity analysis, cause the one or more processors to: generate a semantic similarity score for a relationship between the context associated with the source document and reference information included in the first reference document or the second reference document; where the one or more instructions, when executed by the one or more processors, further cause the one or more processors to: determine that the semantic similarity score satisfies the threshold semantic similarity score; and where the one or more instructions, that cause the one or more processors to identify the contextual information, cause the one or more processors to: identify the reference information as contextual information based on determining that the semantic similarity score satisfies the threshold. | 0.500536 |
9,715,485 | 15 | 16 | 15. An apparatus, comprising: a processor arranged to execute a logic device; and the logic device arranged to execute a montage application comprising an authoring component operative to provide a presentation surface having multiple presentation tiles, receive control directives to associate content files with presentation tiles, generate tile objects for the content files based on content file types for the content files by deriving a portion of content from each content file to fit within a defined region of a presentation tile based on a set of rules concerning which content to derive from the each content file based upon that content file's content file type, and store the presentation surface and tile objects as a montage capable of navigation using a gesture interface, the authoring component operative to modify the montage in response to user gestures. | 15. An apparatus, comprising: a processor arranged to execute a logic device; and the logic device arranged to execute a montage application comprising an authoring component operative to provide a presentation surface having multiple presentation tiles, receive control directives to associate content files with presentation tiles, generate tile objects for the content files based on content file types for the content files by deriving a portion of content from each content file to fit within a defined region of a presentation tile based on a set of rules concerning which content to derive from the each content file based upon that content file's content file type, and store the presentation surface and tile objects as a montage capable of navigation using a gesture interface, the authoring component operative to modify the montage in response to user gestures. 16. The apparatus of claim 15 , the authoring component comprising multiple type modules corresponding to each content file type, a type module operative to retrieve information from a content file based on a type definition for a content file type, and generate a tile object based on the retrieved information and the type definition. | 0.54717 |
7,962,487 | 6 | 12 | 6. A method of associating a search query to a cluster comprising: receiving a Uniform Resource Locator (URL) returned in response to a search based on a search query; extracting a first set of tokens from the URL; determining a similarity between the first set of tokens and each of a multiple set of tokens extracted from multiple other URLs returned in response to other search queries; and associating the search query with a cluster, wherein the associating is based at least in part on the similarities between the first set of tokens and each of the multiple set of tokens extracted from the other URLs. | 6. A method of associating a search query to a cluster comprising: receiving a Uniform Resource Locator (URL) returned in response to a search based on a search query; extracting a first set of tokens from the URL; determining a similarity between the first set of tokens and each of a multiple set of tokens extracted from multiple other URLs returned in response to other search queries; and associating the search query with a cluster, wherein the associating is based at least in part on the similarities between the first set of tokens and each of the multiple set of tokens extracted from the other URLs. 12. The method of claim 6 , wherein the tokens are words extracted from a URL. | 0.873786 |
9,779,728 | 11 | 19 | 11. A system for modifying a voice file comprising a plurality of words, the system comprising: a silence-detection module configured to detect silences in the voice file and to divide the voice file into multiple segments based on at least the detected silences, an identification module configured to: apply a language model to the voice file as a whole, the language model including a plurality of feature units, a preliminary punctuation state, and a preliminary weight of the preliminary punctuation state, each of the feature units including a word or phrase, a part of speech or sentence element of the word or phrase, the application of the language model to the voice file as a whole identifying in the voice file as a whole one or more first feature units of the plurality of feature units; and identifying in the segments one or more second feature units of the plurality of feature units; and a punctuation-addition module configured to: generate a first aggregate weight R1 based on a combination of the preliminary weights of the preliminary punctuation states corresponding to the identified one or more first feature units; apply the language model to the segments, the application of the language model to the segments to generate a second aggregate weight R2 including a combination of the preliminary weights of the preliminary punctuation states corresponding to the identified one or more second feature units; generate a third aggregate weight R3 determined according to R3=a×R1+(1−a)×R2 where 0<a<1; and modifying the voice file so as to include one or more final punctuations based on at least the third aggregate weight R3. | 11. A system for modifying a voice file comprising a plurality of words, the system comprising: a silence-detection module configured to detect silences in the voice file and to divide the voice file into multiple segments based on at least the detected silences, an identification module configured to: apply a language model to the voice file as a whole, the language model including a plurality of feature units, a preliminary punctuation state, and a preliminary weight of the preliminary punctuation state, each of the feature units including a word or phrase, a part of speech or sentence element of the word or phrase, the application of the language model to the voice file as a whole identifying in the voice file as a whole one or more first feature units of the plurality of feature units; and identifying in the segments one or more second feature units of the plurality of feature units; and a punctuation-addition module configured to: generate a first aggregate weight R1 based on a combination of the preliminary weights of the preliminary punctuation states corresponding to the identified one or more first feature units; apply the language model to the segments, the application of the language model to the segments to generate a second aggregate weight R2 including a combination of the preliminary weights of the preliminary punctuation states corresponding to the identified one or more second feature units; generate a third aggregate weight R3 determined according to R3=a×R1+(1−a)×R2 where 0<a<1; and modifying the voice file so as to include one or more final punctuations based on at least the third aggregate weight R3. 19. The system of claim 11 , being further configured, for establishing the language model, to: perform word separation to divide one or more sentences in a corpus into a plurality of words, the sentences in the corpus including one or more preliminary punctuations; search for one or more third feature units in the corpus according to a predetermined feature template based on semantic features of the words in the corpus; for a particular third feature unit including a word or phrase in the corpus: record a number of occurrences of the preliminary punctuation state associated with the word or phrase of that particular third feature unit; determine the preliminary weight of that preliminary punctuation state based on at least the recorded number of occurrences; and mapping the particular third feature unit to the preliminary weight of that preliminary punctuation state. | 0.517014 |
8,566,305 | 3 | 4 | 3. The method of claim 1 , further comprising: applying a set of detection conditions to the at least one of the plurality of messages containing data that matches the data in at least one of the specified columns. | 3. The method of claim 1 , further comprising: applying a set of detection conditions to the at least one of the plurality of messages containing data that matches the data in at least one of the specified columns. 4. The method of claim 3 , wherein applying the set of detection conditions comprises: determining whether a sender of the at least one of the plurality of messages has a history of prior violations. | 0.934453 |
9,542,382 | 1 | 3 | 1. A method of automated discourse analysis of interaction transcriptions, the method comprising; receiving, by at least one processor, a plurality of interaction transcriptions; for each respective interaction transcript of the plurality of interaction transcriptions: executing, by the at least one processor, a zoning process that segments utterances of the respective interaction transcript into a plurality of meaning units; classifying, by the at least one processor, the plurality of meaning units into dialog acts using a trained dialog act classifier, the dialog acts include at least the following categories: social, information, request, response and repetition, the repetition category being used to identify a repetition of a immediately previous dialog act when one party to a given interaction repeats a meaning unit previously spoken by another party to the given interaction, wherein the dialog act classifier is trained by at least preparing a training file in Attribute-Relation File Format (ARFF) for Waikato Environment for Knowledge Analysis (WEKA) in which each row in the training file includes data about a single respective meaning, unit, and then applying Naïves Bayes to build the classifier from the training file; and identifying, by the at least one processor, durations between the dialog acts; selecting, by the at least one processor, a subset of the plurality of interaction transcriptions based on the durations; extracting, by the at least one processor, syntactic patterns from the selected subset; and outing, by the at least one processor, the extracted patterns as a visual presentation on a graphical display. | 1. A method of automated discourse analysis of interaction transcriptions, the method comprising; receiving, by at least one processor, a plurality of interaction transcriptions; for each respective interaction transcript of the plurality of interaction transcriptions: executing, by the at least one processor, a zoning process that segments utterances of the respective interaction transcript into a plurality of meaning units; classifying, by the at least one processor, the plurality of meaning units into dialog acts using a trained dialog act classifier, the dialog acts include at least the following categories: social, information, request, response and repetition, the repetition category being used to identify a repetition of a immediately previous dialog act when one party to a given interaction repeats a meaning unit previously spoken by another party to the given interaction, wherein the dialog act classifier is trained by at least preparing a training file in Attribute-Relation File Format (ARFF) for Waikato Environment for Knowledge Analysis (WEKA) in which each row in the training file includes data about a single respective meaning, unit, and then applying Naïves Bayes to build the classifier from the training file; and identifying, by the at least one processor, durations between the dialog acts; selecting, by the at least one processor, a subset of the plurality of interaction transcriptions based on the durations; extracting, by the at least one processor, syntactic patterns from the selected subset; and outing, by the at least one processor, the extracted patterns as a visual presentation on a graphical display. 3. The method of claim 1 , further comprising selecting, by the at least one processor, the subset with the longest identified durations between dialog acts. | 0.695736 |
8,543,405 | 5 | 15 | 5. A method of operating a speech dialog system which includes a speech recognition unit which receives spoken inputs from a user, a speech output device which delivers spoken outputs to the user, and a speech dialog control unit which controls a spoken dialog with the user based on a speech dialog description, the method comprising: controlling the spoken dialog with the user based on the speech dialog description; during the spoken dialog, marking an entry position in the spoken dialog with a corresponding marker; storing the marker, an address in the speech dialog description corresponding to the marked position in the spoken dialog, and a user selection for an input to be input at the marked position; in response to the user inputting the stored marker at a later time, selecting the address in the speech dialog description corresponding to the marked entry position and inputting the user selection at the marked entry position such that the spoken dialog continues from the marked position without the user inputting a user selection. | 5. A method of operating a speech dialog system which includes a speech recognition unit which receives spoken inputs from a user, a speech output device which delivers spoken outputs to the user, and a speech dialog control unit which controls a spoken dialog with the user based on a speech dialog description, the method comprising: controlling the spoken dialog with the user based on the speech dialog description; during the spoken dialog, marking an entry position in the spoken dialog with a corresponding marker; storing the marker, an address in the speech dialog description corresponding to the marked position in the spoken dialog, and a user selection for an input to be input at the marked position; in response to the user inputting the stored marker at a later time, selecting the address in the speech dialog description corresponding to the marked entry position and inputting the user selection at the marked entry position such that the spoken dialog continues from the marked position without the user inputting a user selection. 15. The method according to claim 5 , wherein the user selection includes a keyword. | 0.901869 |
8,756,515 | 1 | 3 | 1. At a computer system that includes one or more processors and system memory, a method for providing a real-time incremental editor for enacting changes to user interface (UI) elements of an active UI, the method comprising: an act of the computer system presenting a first UI of an executing application program based on a UI model that includes declarative model data that drives the behavior of the first UI, the first UI including a plurality of UI elements, the UI model including particular declarative model data that declaratively defines a visual appearance of the plurality of UI elements of the first UI, each of the plurality of UI elements being an instance of the particular declarative model data; an-act of the computer system visually indicating a state of the first UI, including visually indicating within the first UI that a particular UI element of the plurality of UI elements has been selected by a user at the first UI; concurrent to presenting the first UI of the application program, and concurrent to visually indicating within the first UI that the particular UI element has been selected by a user: an act of the computer system presenting a second UI of a real-time incremental editor that is separate from the application program and that is configured to receive one or more editing inputs that are to be applied to the particular declarative model data of the UI model, to edit the first UI of the application program while the application program is executing; an act of the computer system receiving a first editing user input at the second UI of the real-time incremental editor, the first editing input selecting the particular declarative model data that declaratively defines the plurality of UI elements; based on selection of the particular declarative model data within the real-time incremental editor, visually distinguishing each of the plurality of UI elements, including the particular UI element, within the first UI as being selected at the second UI of the real-time incremental editor, while also visually indicating within the first UI that the particular UI element has been selected at the first UI; an act of the computer system receiving a second editing user input at the second UI of the real-time incremental editor, the second editing input indicating one or more desired edits that are to be made to the plurality of UI elements that affect the visual appearance of the plurality of UI elements within the first UI; an act of the computer system determining, based on the received editing input, one or more changes that are to be made to the particular declarative model data corresponding to the plurality of UI elements in order to enact the one or more desired edits to each of the plurality of UI elements; and an act of the computer system updating the first UI to reflect the one or more desired edits to the plurality of UI elements, including: preserving the state of the first UI to reflect the user selection of the particular UI element by continuing to visually indicate that the particular UI element has been selected by the user; and concurrent to continuing to visually indicate that the particular UI element has been selected by the user, altering the visual appearance of each of the plurality of UI elements, including the particular UI element, based on the one or more changes to the particular declarative model data corresponding to the plurality of UI elements. | 1. At a computer system that includes one or more processors and system memory, a method for providing a real-time incremental editor for enacting changes to user interface (UI) elements of an active UI, the method comprising: an act of the computer system presenting a first UI of an executing application program based on a UI model that includes declarative model data that drives the behavior of the first UI, the first UI including a plurality of UI elements, the UI model including particular declarative model data that declaratively defines a visual appearance of the plurality of UI elements of the first UI, each of the plurality of UI elements being an instance of the particular declarative model data; an-act of the computer system visually indicating a state of the first UI, including visually indicating within the first UI that a particular UI element of the plurality of UI elements has been selected by a user at the first UI; concurrent to presenting the first UI of the application program, and concurrent to visually indicating within the first UI that the particular UI element has been selected by a user: an act of the computer system presenting a second UI of a real-time incremental editor that is separate from the application program and that is configured to receive one or more editing inputs that are to be applied to the particular declarative model data of the UI model, to edit the first UI of the application program while the application program is executing; an act of the computer system receiving a first editing user input at the second UI of the real-time incremental editor, the first editing input selecting the particular declarative model data that declaratively defines the plurality of UI elements; based on selection of the particular declarative model data within the real-time incremental editor, visually distinguishing each of the plurality of UI elements, including the particular UI element, within the first UI as being selected at the second UI of the real-time incremental editor, while also visually indicating within the first UI that the particular UI element has been selected at the first UI; an act of the computer system receiving a second editing user input at the second UI of the real-time incremental editor, the second editing input indicating one or more desired edits that are to be made to the plurality of UI elements that affect the visual appearance of the plurality of UI elements within the first UI; an act of the computer system determining, based on the received editing input, one or more changes that are to be made to the particular declarative model data corresponding to the plurality of UI elements in order to enact the one or more desired edits to each of the plurality of UI elements; and an act of the computer system updating the first UI to reflect the one or more desired edits to the plurality of UI elements, including: preserving the state of the first UI to reflect the user selection of the particular UI element by continuing to visually indicate that the particular UI element has been selected by the user; and concurrent to continuing to visually indicate that the particular UI element has been selected by the user, altering the visual appearance of each of the plurality of UI elements, including the particular UI element, based on the one or more changes to the particular declarative model data corresponding to the plurality of UI elements. 3. The method of claim 1 , wherein the one or more changes to the particular declarative model data comprise data indicating how the particular declarative model data is to be transformed into UI changes. | 0.80791 |
8,538,742 | 1 | 3 | 1. A computer-implemented method for translating a social feed, the method comprising: receiving, with one or more processors, social feed data, the social feed data configured to cause a client to display a social feed in a first language; receiving, with the one or more processors, a request for a translation from a first user; determining, with the one or more processors, a social context for the translation, the social context including a second user that is related to the first user in a social graph, the second user communicating with the first user in a second language and wherein the social graph comprises a relationship type between the first and the second user; receiving, with the one or more processors, a user input from the first user specifying the relationship type for which the social feed data should be translated; determining, with the one or more processors, a first portion of the social feed for translation based on the relationship type between the first user and the second user being the relationship type specified by the first user in the user input, the first portion including one or more portions of the social feed data that have been acted upon by the second user; translating, with the one or more processors, the social feed data that is associated with the first portion so that the social feed data causes the client to display the first portion of the social feed in the second language based at least in part on the request and the social context; and transmitting the translated social feed data to the client. | 1. A computer-implemented method for translating a social feed, the method comprising: receiving, with one or more processors, social feed data, the social feed data configured to cause a client to display a social feed in a first language; receiving, with the one or more processors, a request for a translation from a first user; determining, with the one or more processors, a social context for the translation, the social context including a second user that is related to the first user in a social graph, the second user communicating with the first user in a second language and wherein the social graph comprises a relationship type between the first and the second user; receiving, with the one or more processors, a user input from the first user specifying the relationship type for which the social feed data should be translated; determining, with the one or more processors, a first portion of the social feed for translation based on the relationship type between the first user and the second user being the relationship type specified by the first user in the user input, the first portion including one or more portions of the social feed data that have been acted upon by the second user; translating, with the one or more processors, the social feed data that is associated with the first portion so that the social feed data causes the client to display the first portion of the social feed in the second language based at least in part on the request and the social context; and transmitting the translated social feed data to the client. 3. The method of claim 1 , wherein the request is a subset command that includes a first indication that a second portion of the social feed should be translated and a second indication of which portion of the social feed should be translated. | 0.869214 |
7,624,080 | 9 | 10 | 9. A computer program product comprising a computer readable storage medium having computer readable program code portions recorded thereon for enabling a computer, in an ongoing manner, to gain knowledge originating with measurements taken by a sensor, the computer readable program code portions comprising: a first executable portion, for enabling said computer to classify a current measurement as being either recognized or unrecognized in the context of a permanent knowledge base; a second executable portion, for enabling said computer, if the current said measurement is classified as being recognized, to determine whether an outcome associated with the recognized current said measurement is consistent with an outcome of at least one other measurement that exists in said permanent knowledge base and that constitutes a basis for said recognition; a third executable portion, for enabling said computer, if said outcome associated with the recognized current said measurement is consistent with an outcome of at least one other said measurement that exists in said permanent knowledge base and that constitutes a basis for said recognition, to assimilate information in said permanent knowledge base, said assimilated information including the recognized current said measurement and at least one characteristic of the recognized current said measurement; a fourth executable portion, for enabling said computer, if the current said measurement is classified as being unrecognized, to categorize the unrecognized current said measurement in a hierarchal structure in a temporary knowledge base, to associate the unrecognized current said measurement with an outcome, and to determine whether the unrecognized current said measurement is subsequently corroborated by at least one fixture said measurement; and a fifth executable portion, for enabling said computer, if the unrecognized current said measurement is determined to be corroborated, to assimilate, into said permanent knowledge base, information including the corroborated current said measurement and at least one characteristic of the corroborated current said measurement; wherein, if the current said measurement is classified as being unrecognized, then: the unrecognized current said measurement is made during a current event run; said at least one future said measurement is made during at least one fixture said event run; and wherein said measurements constitute a data stream in real time, said computer continually adapting, in real time, to recognize and assimilate new information, to corroborate previous information, and to identify inconsistencies in said data stream, said computer thereby continually determining, in real time, useful collections of values relating to said sensor. | 9. A computer program product comprising a computer readable storage medium having computer readable program code portions recorded thereon for enabling a computer, in an ongoing manner, to gain knowledge originating with measurements taken by a sensor, the computer readable program code portions comprising: a first executable portion, for enabling said computer to classify a current measurement as being either recognized or unrecognized in the context of a permanent knowledge base; a second executable portion, for enabling said computer, if the current said measurement is classified as being recognized, to determine whether an outcome associated with the recognized current said measurement is consistent with an outcome of at least one other measurement that exists in said permanent knowledge base and that constitutes a basis for said recognition; a third executable portion, for enabling said computer, if said outcome associated with the recognized current said measurement is consistent with an outcome of at least one other said measurement that exists in said permanent knowledge base and that constitutes a basis for said recognition, to assimilate information in said permanent knowledge base, said assimilated information including the recognized current said measurement and at least one characteristic of the recognized current said measurement; a fourth executable portion, for enabling said computer, if the current said measurement is classified as being unrecognized, to categorize the unrecognized current said measurement in a hierarchal structure in a temporary knowledge base, to associate the unrecognized current said measurement with an outcome, and to determine whether the unrecognized current said measurement is subsequently corroborated by at least one fixture said measurement; and a fifth executable portion, for enabling said computer, if the unrecognized current said measurement is determined to be corroborated, to assimilate, into said permanent knowledge base, information including the corroborated current said measurement and at least one characteristic of the corroborated current said measurement; wherein, if the current said measurement is classified as being unrecognized, then: the unrecognized current said measurement is made during a current event run; said at least one future said measurement is made during at least one fixture said event run; and wherein said measurements constitute a data stream in real time, said computer continually adapting, in real time, to recognize and assimilate new information, to corroborate previous information, and to identify inconsistencies in said data stream, said computer thereby continually determining, in real time, useful collections of values relating to said sensor. 10. The computer program product according to claim 9 , wherein said computer readable program code portions further include a sixth executable portion, for enabling said computer, if said current measurement is classified as being unrecognized, to place the unrecognized current said measurement in provisional storage at least until the conclusion of the current said event run, said categorizing being performed after the conclusion of the current said event run. | 0.501071 |
8,661,030 | 14 | 18 | 14. One or more computer storage media storing computer-readable instructions that, when executed, instruct one or more processors to perform acts comprising: receiving a query in a first human language; determining that a first ranker that is trained for the first human language does not satisfy one or more criteria to be classified as a trained ranker that is associated with a particular degree of training; upon determining that the first ranker does not satisfy the one or more criteria, selecting a second ranker that is trained for a second human language, the second ranker being trained with a greater amount of training data than the first ranker; utilizing the second ranker to generate a set of search results for the query; utilizing the first ranker to reorder a predefined number of search results of the set of search results; and providing the reordered search results. | 14. One or more computer storage media storing computer-readable instructions that, when executed, instruct one or more processors to perform acts comprising: receiving a query in a first human language; determining that a first ranker that is trained for the first human language does not satisfy one or more criteria to be classified as a trained ranker that is associated with a particular degree of training; upon determining that the first ranker does not satisfy the one or more criteria, selecting a second ranker that is trained for a second human language, the second ranker being trained with a greater amount of training data than the first ranker; utilizing the second ranker to generate a set of search results for the query; utilizing the first ranker to reorder a predefined number of search results of the set of search results; and providing the reordered search results. 18. The one or more computer storage media of claim 14 , wherein at least one of the first ranker or the second ranker is trained based at least in part on input from a user that indicates a degree of relevance a search result has to a query. | 0.502058 |
8,626,627 | 23 | 32 | 23. A web application server comprising: a promotion generation module coupled with a network interface through a communications module, the promotion generation module configured to: access and harvest profile information of users of a social media platform; receive interest in an online promotion from a participant through an entry point; harvest identification information from a social media profile of the participant; generate a personalized promotion for the participant containing at least some of the participant's identification information including securing the personalized promotion by providing a means for verifying integrity of the personalized promotion at a time for redemption wherein the means includes a verifiable portion of the participant's identification information obtained by the accessing; and distribute the personalized promotion to the participant, wherein the identification information on the personalized promotion provides a higher level of security and authenticity than promotions that do not contain such identification information; and a communications module configured to automatically send personalized invitations to enter the online promotion to a list of contacts provided by the participant. | 23. A web application server comprising: a promotion generation module coupled with a network interface through a communications module, the promotion generation module configured to: access and harvest profile information of users of a social media platform; receive interest in an online promotion from a participant through an entry point; harvest identification information from a social media profile of the participant; generate a personalized promotion for the participant containing at least some of the participant's identification information including securing the personalized promotion by providing a means for verifying integrity of the personalized promotion at a time for redemption wherein the means includes a verifiable portion of the participant's identification information obtained by the accessing; and distribute the personalized promotion to the participant, wherein the identification information on the personalized promotion provides a higher level of security and authenticity than promotions that do not contain such identification information; and a communications module configured to automatically send personalized invitations to enter the online promotion to a list of contacts provided by the participant. 32. The web application server of claim 23 , further comprising a data collection and analysis module to harvest information for marketing and branding through the online promotion. | 0.761214 |
9,021,424 | 14 | 18 | 14. A computer program product, tangibly embodied in a non-transitory machine readable medium, comprising instructions operable to: combine the plurality of documents in a unified editable window of a graphical user interface, each document having code fragments comprising switchable code fragments that can be switched in and out during runtime by a switch framework; concurrently displaying a rendering of each of the plurality of documents in a single editor window within a display region, at least some of the plurality of the documents being sub-documents of a parent document, and also displaying comment lines surrounding respective documents of the plurality of documents, the comment lines being non-editable; editing each of the plurality of documents in the single editor window using a unified editor control, the unified editor control providing a control function for disabling the comment lines; executing a line refactoring process to set break-points in the parent document in relation to specific sub-documents; and automatically generating an interface for business application that is created based on the editing, the business application using the plurality of documents and being generated such that the business application can be run without local customization and can run in both centralized and distributed client/server configurations. | 14. A computer program product, tangibly embodied in a non-transitory machine readable medium, comprising instructions operable to: combine the plurality of documents in a unified editable window of a graphical user interface, each document having code fragments comprising switchable code fragments that can be switched in and out during runtime by a switch framework; concurrently displaying a rendering of each of the plurality of documents in a single editor window within a display region, at least some of the plurality of the documents being sub-documents of a parent document, and also displaying comment lines surrounding respective documents of the plurality of documents, the comment lines being non-editable; editing each of the plurality of documents in the single editor window using a unified editor control, the unified editor control providing a control function for disabling the comment lines; executing a line refactoring process to set break-points in the parent document in relation to specific sub-documents; and automatically generating an interface for business application that is created based on the editing, the business application using the plurality of documents and being generated such that the business application can be run without local customization and can run in both centralized and distributed client/server configurations. 18. A computer program product in accordance with claim 14 further comprising code to manage the plurality of documents in the unified editable window according to a set of line numbers for the plurality of documents. | 0.641914 |
9,189,698 | 1 | 4 | 1. A client for converting a textual passage to a synthesized image sequence, comprising: a connection to an image sensor configured to capture an image of text; an output device configured to communicate with a remote server; a display; and processing electronics configured to receive the image of text from the connection to the image sensor, the text being a passage from a source text, and in response to receiving the image of the text: to translate the text of the image of text into a machine readable format; to receive auxiliary information; and to transmit the text in the machine readable format and the auxiliary information to a remote server; wherein the processing electronics are configured to receive from the remote server model information based on the text in the machine readable format and the auxiliary information, wherein the processing electronics are configured to generate the synthesized image sequence based on the model information, and wherein the processing electronics are configured to output the synthesized image sequence using the display. | 1. A client for converting a textual passage to a synthesized image sequence, comprising: a connection to an image sensor configured to capture an image of text; an output device configured to communicate with a remote server; a display; and processing electronics configured to receive the image of text from the connection to the image sensor, the text being a passage from a source text, and in response to receiving the image of the text: to translate the text of the image of text into a machine readable format; to receive auxiliary information; and to transmit the text in the machine readable format and the auxiliary information to a remote server; wherein the processing electronics are configured to receive from the remote server model information based on the text in the machine readable format and the auxiliary information, wherein the processing electronics are configured to generate the synthesized image sequence based on the model information, and wherein the processing electronics are configured to output the synthesized image sequence using the display. 4. The client of claim 1 , wherein the auxiliary information is an image of at least one of an additional passage of text from the source text, an ISBN number corresponding to the source text, a title of source text, a or bar code corresponding to the source text. | 0.501887 |
8,996,584 | 10 | 13 | 10. A content-based healthcare location management system comprising: a microprocessor connected in communication with a system memory having coded instructions for execution by the microprocessor to implement; a correlation services manager to receive a location correlation identifier for a clinical application, and correlate the location correlation identifier with a location instance identifier based on an ontology, wherein the ontology is represented in a directed acyclic graph navigable to identify the ontology for a location, wherein the directed acyclic graph includes a plurality of available ontologies for selection of a location relationship, wherein the plurality of available ontologies includes an “is a” ontology and an “is part of” ontology, wherein the location instance identifier identifies an internal instance of the location correlation identifier to provide location information according to a location schema; a location services manager to update a location map using the location instance identifier, the location services manager to store the location instance identifier in a location relationship object based on at least one location relationship associated with the location instance identifier; and a frame manager to utilize the location relationship object to configure one or more content items forming the clinical application based on the location and the at least one location relationships identified in the location relationship object. | 10. A content-based healthcare location management system comprising: a microprocessor connected in communication with a system memory having coded instructions for execution by the microprocessor to implement; a correlation services manager to receive a location correlation identifier for a clinical application, and correlate the location correlation identifier with a location instance identifier based on an ontology, wherein the ontology is represented in a directed acyclic graph navigable to identify the ontology for a location, wherein the directed acyclic graph includes a plurality of available ontologies for selection of a location relationship, wherein the plurality of available ontologies includes an “is a” ontology and an “is part of” ontology, wherein the location instance identifier identifies an internal instance of the location correlation identifier to provide location information according to a location schema; a location services manager to update a location map using the location instance identifier, the location services manager to store the location instance identifier in a location relationship object based on at least one location relationship associated with the location instance identifier; and a frame manager to utilize the location relationship object to configure one or more content items forming the clinical application based on the location and the at least one location relationships identified in the location relationship object. 13. The system of claim 10 , wherein the location is associated with a name and a role identifier, a related location is associated with a name and a role, and the ontology is associated with an ontology identifier and a relationship type identifier. | 0.57483 |
9,406,294 | 10 | 11 | 10. The method of claim 9 wherein users receiving Shouts are enabled by the local intelligence to reply specifically to received Shouts, and Shouts received by the system and recognized as replies to other Shouts are associated in conversations comprising two or more, or many Shouts, and wherein inclusion in conversations is a criteria for distributing Shouts to other users. | 10. The method of claim 9 wherein users receiving Shouts are enabled by the local intelligence to reply specifically to received Shouts, and Shouts received by the system and recognized as replies to other Shouts are associated in conversations comprising two or more, or many Shouts, and wherein inclusion in conversations is a criteria for distributing Shouts to other users. 11. The method of claim 10 wherein the user is enabled through an interactive screen to review Shout history, and to play retrieved Shouts selectively. | 0.962679 |
8,713,054 | 12 | 41 | 12. A computer-implemented method to assist an information security classification process of an organization for security classification and marking of an electronic document on a computer system, said method comprising: a. performing on at least one computer system, b. establishing an electronic document security regimen comprising at least one criterion of an information security classification process, c. displaying a user classification dialogue for at least one informational portion of an electronic document, where said user classification dialogue comprising a menu of choices, where said menu of choices comprising at least one element for selection, and where said at least one element is associated with said at least one criterion of said electronic document security regimen, d. retrieving said at least one element where said at least one element is selected, e. establishing a classification mark from said at least one criterion associated with the retrieved said at least one element, and f. inserting said classification mark into said electronic document. | 12. A computer-implemented method to assist an information security classification process of an organization for security classification and marking of an electronic document on a computer system, said method comprising: a. performing on at least one computer system, b. establishing an electronic document security regimen comprising at least one criterion of an information security classification process, c. displaying a user classification dialogue for at least one informational portion of an electronic document, where said user classification dialogue comprising a menu of choices, where said menu of choices comprising at least one element for selection, and where said at least one element is associated with said at least one criterion of said electronic document security regimen, d. retrieving said at least one element where said at least one element is selected, e. establishing a classification mark from said at least one criterion associated with the retrieved said at least one element, and f. inserting said classification mark into said electronic document. 41. The method of claim 12 , wherein said electronic document security regimen further comprising at least one classification guidance document registered in said electronic document security regimen, and wherein said method further comprising displaying said classification guidance document to a user of said computer system. | 0.75597 |
10,025,487 | 1 | 7 | 1. A method comprising: detecting a location of a text selection icon on a display of an electronic device; detecting a touch input representing a selection of text displayed on the display using the text selection icon; determining a movement of the text selection icon while the touch input is maintained during the movement, wherein the movement is continuous and drags the text selection icon into a zone adjacent an edge of the display; in response to the determination, enabling a row by row selection mode; and during the movement of the text selection icon while the touch input is maintained and the row by row selection mode is enabled, dynamically increasing a width of said zone based on at least one of a number of rows of displayed text selected by the continuous movement of the text selection icon or a speed of the movement; wherein: if the detected location of the text selection icon is maintained within said zone during the movement while the touch input is maintained, the displayed text is selected on a row by row basis; and, if the detected location of the text selection icon moves outside said zone and moves into the displayed text during the movement while the touch input is maintained, the row by row selection mode is switched to a letter by letter selection mode, wherein in the letter by letter selection mode the displayed text is selected on a letter by letter basis. | 1. A method comprising: detecting a location of a text selection icon on a display of an electronic device; detecting a touch input representing a selection of text displayed on the display using the text selection icon; determining a movement of the text selection icon while the touch input is maintained during the movement, wherein the movement is continuous and drags the text selection icon into a zone adjacent an edge of the display; in response to the determination, enabling a row by row selection mode; and during the movement of the text selection icon while the touch input is maintained and the row by row selection mode is enabled, dynamically increasing a width of said zone based on at least one of a number of rows of displayed text selected by the continuous movement of the text selection icon or a speed of the movement; wherein: if the detected location of the text selection icon is maintained within said zone during the movement while the touch input is maintained, the displayed text is selected on a row by row basis; and, if the detected location of the text selection icon moves outside said zone and moves into the displayed text during the movement while the touch input is maintained, the row by row selection mode is switched to a letter by letter selection mode, wherein in the letter by letter selection mode the displayed text is selected on a letter by letter basis. 7. A method according to claim 1 , wherein the touch input is detected on a track pad. | 0.849123 |
8,725,490 | 1 | 2 | 1. A method of translating text using a mobile device, comprising: in response to an image/video being obtained by a camera of the mobile device, displaying the obtained image/video in a display of the mobile device; in response to an image/video being obtained by the camera of the mobile device and a translation option being selected on the mobile device, sending the image/video from the mobile device to an image recognition server for processing the image/video to determine whether the image/video contains a first text string in a first language; in response to receiving from the image recognition server a determination that the image/video contains the first text string in the first language, sending the first text string to a translation server for obtaining a translation of the first text string into a second text string in a second language that has been associated with a user of the mobile device or the mobile device; after the translation of the first text string into the second text string in the second language is obtained, displaying in the display of the mobile device the second text string in the second language transposed over the first text string in the image/video captured by the camera; and as the camera continuously obtains a new image/video, repeating displaying the new image/video, determining whether the new image/video contains a new text string, obtaining a translation for the new text string, and displaying the translation of the new text string transposed over the new text string in the new image/video. | 1. A method of translating text using a mobile device, comprising: in response to an image/video being obtained by a camera of the mobile device, displaying the obtained image/video in a display of the mobile device; in response to an image/video being obtained by the camera of the mobile device and a translation option being selected on the mobile device, sending the image/video from the mobile device to an image recognition server for processing the image/video to determine whether the image/video contains a first text string in a first language; in response to receiving from the image recognition server a determination that the image/video contains the first text string in the first language, sending the first text string to a translation server for obtaining a translation of the first text string into a second text string in a second language that has been associated with a user of the mobile device or the mobile device; after the translation of the first text string into the second text string in the second language is obtained, displaying in the display of the mobile device the second text string in the second language transposed over the first text string in the image/video captured by the camera; and as the camera continuously obtains a new image/video, repeating displaying the new image/video, determining whether the new image/video contains a new text string, obtaining a translation for the new text string, and displaying the translation of the new text string transposed over the new text string in the new image/video. 2. A method as recited in claim 1 , further comprising: obtaining contextual information associated with the first or second text string; and displaying, in the display of the mobile device, the obtained contextual information. | 0.772088 |
9,466,289 | 4 | 5 | 4. The method of claim 3 , further including: processing the collected plurality of audio samples with a predetermined characteristic extraction protocol so as to obtain a plurality of corresponding audio characteristic sequences; obtaining a characteristic phoneme collection corresponding to the plurality of audio characteristic sequences based on the IPA phoneme mapping collection; training the foreground and background models based on the characteristic phoneme collection and the collected labeled data; and integrating the trained foreground and background models into the acoustic model. | 4. The method of claim 3 , further including: processing the collected plurality of audio samples with a predetermined characteristic extraction protocol so as to obtain a plurality of corresponding audio characteristic sequences; obtaining a characteristic phoneme collection corresponding to the plurality of audio characteristic sequences based on the IPA phoneme mapping collection; training the foreground and background models based on the characteristic phoneme collection and the collected labeled data; and integrating the trained foreground and background models into the acoustic model. 5. The method of claim 4 , wherein: generating the plurality of triphones by linking phonemes in the phoneme collection corresponding to the language includes, for each phoneme in the phoneme collection: obtaining a context phoneme; and generating a triphone by linking the context phoneme to a corresponding monophone for the phoneme; performing Gaussian splitting training on the triphones that are clustered with the decision tree corresponding to the language updates a parameter of the clustered triphone; and training the foreground model further includes: for each phoneme in the characteristic phoneme collection: training an initial hidden Markov model (HMM) for three statuses of a respective phoneme in the characteristic phoneme collection; obtaining data related to the respective phoneme from the collected labeled data; updating the initial HMM with the obtained data so as to obtain a monophone model for the respective phoneme; and after performing the Gaussian splitting training on the triphones that are clustered with a decision tree corresponding to the language, performing minimum phoneme error discriminative training so as to obtain triphone models for respective phonemes in the phoneme collection corresponding to the language; and training the foreground model based on the obtained monophone and triphone models. | 0.758459 |
8,271,410 | 10 | 17 | 10. A computer-readable storage medium storing instructions that, when executed by a processor, cause a computing system to generate a similarity value between a current user context generated on an endpoint machine and a stored user context stored in a context database for use in retrieving one or more application resources for an end-user, by performing the steps of: receiving the current user context from the endpoint machine, wherein the current user context reflects a first flow of operations performed by an end-user when interacting with a software application executing on the endpoint machine and includes a frequency value with which the commands are issued to the software application; comparing the current user context to the stored user context, wherein the stored user context reflects a second flow of operations performed during a prior interaction with the software application or with an instance or a version of the software application; generating the similarity value for the stored user context based on comparing the current user context to the stored user context, wherein the similarity value indicates a degree of similarity between the first flow of operations and the second flow of operations; and transmitting the similarity value to a resource engine for further processing. | 10. A computer-readable storage medium storing instructions that, when executed by a processor, cause a computing system to generate a similarity value between a current user context generated on an endpoint machine and a stored user context stored in a context database for use in retrieving one or more application resources for an end-user, by performing the steps of: receiving the current user context from the endpoint machine, wherein the current user context reflects a first flow of operations performed by an end-user when interacting with a software application executing on the endpoint machine and includes a frequency value with which the commands are issued to the software application; comparing the current user context to the stored user context, wherein the stored user context reflects a second flow of operations performed during a prior interaction with the software application or with an instance or a version of the software application; generating the similarity value for the stored user context based on comparing the current user context to the stored user context, wherein the similarity value indicates a degree of similarity between the first flow of operations and the second flow of operations; and transmitting the similarity value to a resource engine for further processing. 17. The computer-readable storage medium of claim 10 , wherein the current user context is generated by performing the steps of: receiving one or more inputs issued by the end-user to the software application, wherein the one or more inputs include commands issued by the end-user to the software application; receiving one or more outputs generated by the software application in response to the one or more inputs issued by the end-user; and generating the current user context based on the one or more inputs issued by the end-user and the one or more outputs generated by the software application, wherein the current user context includes at least one of command data, external software data, working file data, file content data, or profile data. | 0.78721 |
8,434,001 | 1 | 19 | 1. A method comprising: using at least a processor and memory for: receiving identification of a position within a media item residing on an electronic device, wherein receiving identification of the position comprises receiving identification of the position of the media item that is being presented when the media item closes; determining an indication of a time elapsed since the media item was last accessed; retrieving a mapping that maps pre-generated summarization to the position within the media item; generating a content summary for a portion of the media item based on the identified position, the elapsed time, and the pre-generated summarization; and presenting the content summary, wherein generating the content summary comprises: retrieving the pre-generated summarization of the media item; and truncating the pre-generated summarization based on the position within the media item, wherein presenting the content summary comprises presenting the truncated pre-generated summarization. | 1. A method comprising: using at least a processor and memory for: receiving identification of a position within a media item residing on an electronic device, wherein receiving identification of the position comprises receiving identification of the position of the media item that is being presented when the media item closes; determining an indication of a time elapsed since the media item was last accessed; retrieving a mapping that maps pre-generated summarization to the position within the media item; generating a content summary for a portion of the media item based on the identified position, the elapsed time, and the pre-generated summarization; and presenting the content summary, wherein generating the content summary comprises: retrieving the pre-generated summarization of the media item; and truncating the pre-generated summarization based on the position within the media item, wherein presenting the content summary comprises presenting the truncated pre-generated summarization. 19. The method of claim 1 , wherein generating the content summary comprises generating at least one mapping that maps the content summary to at least one portion of the media item that is relevant to the content summary. | 0.792683 |
9,501,529 | 1 | 2 | 1. A computer-implemented method of searching documents managed by a search engine comprising: receiving a relational database type query in a form for retrieving data from one or more tables of a relational database; translating the relational database type query into a query for the search engine and submitting the translated query to the search engine to retrieve information from the documents with text satisfying the translated query, wherein each document is associated with an indicator corresponding to a table of the relational database; and formatting resulting information of the documents from the search engine into a relational database query result set by extracting data specified in the relational database type query from the resulting information. | 1. A computer-implemented method of searching documents managed by a search engine comprising: receiving a relational database type query in a form for retrieving data from one or more tables of a relational database; translating the relational database type query into a query for the search engine and submitting the translated query to the search engine to retrieve information from the documents with text satisfying the translated query, wherein each document is associated with an indicator corresponding to a table of the relational database; and formatting resulting information of the documents from the search engine into a relational database query result set by extracting data specified in the relational database type query from the resulting information. 2. The computer-implemented method of claim 1 , wherein the relational database type query includes a JOIN operation between first and second tables, and the translating the relational database type query includes: generating a first query for the search engine corresponding to the first table from the relational database type query and submitting the first query to the search engine to retrieve information; generating a second query for the search engine corresponding to the second table from the relational database type query, wherein the second query utilizes information retrieved for the first query; and submitting the second query to the search engine to retrieve information. | 0.500725 |
7,814,054 | 1 | 4 | 1. A computer implemented method for managing objects using predetermined master commands, the computer system comprising the steps of: (a) providing a first object of a first object type; (b) providing a first source application; (c) associating said first source application with a first source operation; (d) associating said first source operation with a first source command; (e) providing a second file of a second object type; (f) providing a second source application; (g) associating said second source application with a second source operation; (h) associating said second source operation with a second source command; (i) associating a master application with said first application and said second application; (j) associating a first master command with said first source operation; (k) associating a second master command with said second source operation; (l) requesting a first source operations list from said first source application; (m) providing said first source operations list from said first source application to said master application; (n) wherein said first source operations list comprises first information relating to said first source operation; (o) requesting a second source operations list from said source application; (p) providing said second source operations list from said second source application to said master application; (q) wherein said second source operations list comprises second information relating to said second source operation; (r) associating a first predetermined command with said master application; (s) associating a second predetermined command with said master application; (t) associating said first predetermined command with said first source operation using said first information from said first source operations list; (u) associating said second predetermined command with said second source operation using said second information from said second source operations list; (v) associating a input device with said master application; (w) using said input device to select said first predetermined command and said first object; (x) executing said first source operation on said first object in said first source application in response to said using said input device to select said first predetermined command and said input device; (y) using said input device to select said second predetermined command and said second object; and (z) executing said second source application on said second object in said second source application in response to said selecting of said second predetermined command with said input device. | 1. A computer implemented method for managing objects using predetermined master commands, the computer system comprising the steps of: (a) providing a first object of a first object type; (b) providing a first source application; (c) associating said first source application with a first source operation; (d) associating said first source operation with a first source command; (e) providing a second file of a second object type; (f) providing a second source application; (g) associating said second source application with a second source operation; (h) associating said second source operation with a second source command; (i) associating a master application with said first application and said second application; (j) associating a first master command with said first source operation; (k) associating a second master command with said second source operation; (l) requesting a first source operations list from said first source application; (m) providing said first source operations list from said first source application to said master application; (n) wherein said first source operations list comprises first information relating to said first source operation; (o) requesting a second source operations list from said source application; (p) providing said second source operations list from said second source application to said master application; (q) wherein said second source operations list comprises second information relating to said second source operation; (r) associating a first predetermined command with said master application; (s) associating a second predetermined command with said master application; (t) associating said first predetermined command with said first source operation using said first information from said first source operations list; (u) associating said second predetermined command with said second source operation using said second information from said second source operations list; (v) associating a input device with said master application; (w) using said input device to select said first predetermined command and said first object; (x) executing said first source operation on said first object in said first source application in response to said using said input device to select said first predetermined command and said input device; (y) using said input device to select said second predetermined command and said second object; and (z) executing said second source application on said second object in said second source application in response to said selecting of said second predetermined command with said input device. 4. The computer implemented method of claim 1 , wherein said first source operations list comprises an association between said first source operation and said first source command. | 0.908308 |
7,904,401 | 33 | 34 | 33. The computer-readable medium of claim 32 further comprising generating a refined simplified ontology and wherein applying the query comprises applying the query to the refined simplified ontology to identify an instance of the query in said refined simplified ontology. | 33. The computer-readable medium of claim 32 further comprising generating a refined simplified ontology and wherein applying the query comprises applying the query to the refined simplified ontology to identify an instance of the query in said refined simplified ontology. 34. The computer-readable medium of claim 33 further comprising identifying additional instances of the query in the original ontology based on the at least one transformation. | 0.935813 |
8,121,838 | 5 | 6 | 5. The method of claim 4 , wherein the first accuracy rating associated with the at least one portion of the first recognized text is a function of the confidence scores associated with the plurality of phrases in the N-best match. | 5. The method of claim 4 , wherein the first accuracy rating associated with the at least one portion of the first recognized text is a function of the confidence scores associated with the plurality of phrases in the N-best match. 6. The method of claim 5 , further comprising: lowering the first accuracy rating when the confidence scores of the plurality of phrases in the N-best match approximate the confidence score associated with the recognized phrase. | 0.965392 |
8,500,604 | 13 | 14 | 13. The method of claim 12 , wherein analyzing the received physiologic data comprises: analyzing the received physiologic data with a multilayer perceptron, support vector machine, or hidden Markov (MSH) model. | 13. The method of claim 12 , wherein analyzing the received physiologic data comprises: analyzing the received physiologic data with a multilayer perceptron, support vector machine, or hidden Markov (MSH) model. 14. The method of claim 13 , wherein analyzing the received physiologic data with the MSH model comprises: determining a change in a x-axis orientation of the plurality of parts of a user; determining a change in a y-axis orientation of the plurality of parts of the user; determining a change in a z-axis orientation of the plurality of parts of the user; determining a change in a three dimensional velocity of the plurality of parts of the user; determining a range of motion based on the physiologic data; determining the strength of a muscle based on the physiologic data; and recommending a corrective action. | 0.748157 |
8,346,759 | 26 | 32 | 26. An article of manufacture comprising at least one of a hardware device implementing logic and a computer storage media having computer executable code to cause operations to be performed, the operations comprising: accessing document identifiers for documents including at least one value that is a member of a set of values; generating a number of posting lists associated with a first level, wherein each posting list is associated with a range of consecutive values within the set of values and includes document identifiers for documents including at least one value within the range of consecutive values associated with the posting list, and wherein each document identifier is associated with one value in the set of values included in the document identified by the document identifier; performing at least one iteration of generating posting lists for an additional level, wherein each posting list generated for the additional level is formed by merging at least two posting lists associated with a previous level, wherein each generated posting list at one additional level is associated with consecutive values in the set of values, wherein each document in the generated posting list at the additional level includes one value in the consecutive values associated with the posting list at the additional level, and wherein a new additional level and posting lists associated therewith are generated with each iteration; receiving a query on a query range of values within the set of values; determining a minimum number of posting lists associated with one or more levels having consecutive values that include the query range of values; merging the determined posting lists to form a merged posting list including document identifiers of documents including values within the query range; and returning the document identifiers in the merged posting list. | 26. An article of manufacture comprising at least one of a hardware device implementing logic and a computer storage media having computer executable code to cause operations to be performed, the operations comprising: accessing document identifiers for documents including at least one value that is a member of a set of values; generating a number of posting lists associated with a first level, wherein each posting list is associated with a range of consecutive values within the set of values and includes document identifiers for documents including at least one value within the range of consecutive values associated with the posting list, and wherein each document identifier is associated with one value in the set of values included in the document identified by the document identifier; performing at least one iteration of generating posting lists for an additional level, wherein each posting list generated for the additional level is formed by merging at least two posting lists associated with a previous level, wherein each generated posting list at one additional level is associated with consecutive values in the set of values, wherein each document in the generated posting list at the additional level includes one value in the consecutive values associated with the posting list at the additional level, and wherein a new additional level and posting lists associated therewith are generated with each iteration; receiving a query on a query range of values within the set of values; determining a minimum number of posting lists associated with one or more levels having consecutive values that include the query range of values; merging the determined posting lists to form a merged posting list including document identifiers of documents including values within the query range; and returning the document identifiers in the merged posting list. 32. The article of manufacture of claim 26 , wherein determining the minimum number of posting lists comprises determining a minimum number of posting lists including values outside of the query range of values that are filtered before merging the posting lists. | 0.78057 |
9,786,281 | 1 | 37 | 1. A device comprising: a profile building component in communication with an electronic data store; a speech recognition component; and a sensor configured to detect movement of a user independent of a direction of the user's gaze and without detecting physical contact between the user and the device; wherein the profile building component is configured to: receive, from the sensor, an indication that presence of the user was detected; begin listening for utterances from the user in response to receiving the indication; detect a first voice signal corresponding to a first utterance of the user; determine an identity of the user using the first voice signal; process the first voice signal to determine acoustic information about the user, wherein the acoustic information comprises at least one of an age, a gender, an accent type, a native language, or a type of speech pattern of the user; perform speech recognition on the first voice signal to obtain a transcript; process the transcript to determine language information relating to the user, wherein the language information comprises at least one of a name, hobbies, habits, or preferences of the user; store, in a user profile associated with the identity of the user, the acoustic information and the language information; determine acoustic model information using at least one of the first voice signal, the acoustic information, or the language information; and determine language model information using at least one of the transcript, the acoustic information, or the language information; and wherein the speech recognition component is configured to: receive a second voice signal corresponding to a second utterance of the user; determine the identity of the user using the second voice signal; perform speech recognition on the second voice signal using at least one of the acoustic model information or the language model information to obtain a word sequence that indicates that a third utterance corresponding to a language characteristic will be uttered by a second user different than the user at a time after a current time; and select a second user acoustic model corresponding to the language characteristic for performing speech recognition at the time after the current time. | 1. A device comprising: a profile building component in communication with an electronic data store; a speech recognition component; and a sensor configured to detect movement of a user independent of a direction of the user's gaze and without detecting physical contact between the user and the device; wherein the profile building component is configured to: receive, from the sensor, an indication that presence of the user was detected; begin listening for utterances from the user in response to receiving the indication; detect a first voice signal corresponding to a first utterance of the user; determine an identity of the user using the first voice signal; process the first voice signal to determine acoustic information about the user, wherein the acoustic information comprises at least one of an age, a gender, an accent type, a native language, or a type of speech pattern of the user; perform speech recognition on the first voice signal to obtain a transcript; process the transcript to determine language information relating to the user, wherein the language information comprises at least one of a name, hobbies, habits, or preferences of the user; store, in a user profile associated with the identity of the user, the acoustic information and the language information; determine acoustic model information using at least one of the first voice signal, the acoustic information, or the language information; and determine language model information using at least one of the transcript, the acoustic information, or the language information; and wherein the speech recognition component is configured to: receive a second voice signal corresponding to a second utterance of the user; determine the identity of the user using the second voice signal; perform speech recognition on the second voice signal using at least one of the acoustic model information or the language model information to obtain a word sequence that indicates that a third utterance corresponding to a language characteristic will be uttered by a second user different than the user at a time after a current time; and select a second user acoustic model corresponding to the language characteristic for performing speech recognition at the time after the current time. 37. The device of claim 1 , wherein the speech recognition component is further configured to select the second user acoustic model prior to the third utterance being uttered. | 0.853188 |
9,141,713 | 10 | 11 | 10. The non-transitory computer readable storage medium of claim 1 , wherein the instructions that cause the computer to generate, for each search criterion of the plurality of search criteria, a search criterion map further cause the computer to assign a relevance score to one or more products in at least one search criterion map. | 10. The non-transitory computer readable storage medium of claim 1 , wherein the instructions that cause the computer to generate, for each search criterion of the plurality of search criteria, a search criterion map further cause the computer to assign a relevance score to one or more products in at least one search criterion map. 11. The non-transitory computer readable storage medium of claim 10 , wherein the instructions that cause the computer to generate a product map cause the computer to assign a relevance score to one or more of the search criteria in the product map based on a corresponding relevance score in the at least one search criterion map. | 0.923627 |
7,917,841 | 21 | 25 | 21. A non-transitory computer readable storage medium having stored thereon computer-executable instructions for viewing information associated with data in a spreadsheet, which, when executed by a processor, perform a method the comprising: providing a document including data and information associated with the data; parsing the document to retrieve the associated information; code for processing the associated information to break the associated information down into at least one sentence; categorizing the at least one sentence based on a likelihood that the at least one sentence corresponds to at least one category in a taxonomy corresponding to the data resulting in at least one categorized sentence; assigning an association strength to the at least one categorized sentence, the association strength indicating the likelihood that the at least on categorized sentence corresponds to the at least one category in the taxonomy; filtering the at least one categorized sentence based on the association strength; matching one or more of the at least one categorized sentence with the at least one category in the taxonomy, based on the filtering, resulting in an at least one categorized sentence matched with the at least one category in the taxonomy; and outputting only the at least one categorized sentence matched with the at least one category in the taxonomy to the spreadsheet. | 21. A non-transitory computer readable storage medium having stored thereon computer-executable instructions for viewing information associated with data in a spreadsheet, which, when executed by a processor, perform a method the comprising: providing a document including data and information associated with the data; parsing the document to retrieve the associated information; code for processing the associated information to break the associated information down into at least one sentence; categorizing the at least one sentence based on a likelihood that the at least one sentence corresponds to at least one category in a taxonomy corresponding to the data resulting in at least one categorized sentence; assigning an association strength to the at least one categorized sentence, the association strength indicating the likelihood that the at least on categorized sentence corresponds to the at least one category in the taxonomy; filtering the at least one categorized sentence based on the association strength; matching one or more of the at least one categorized sentence with the at least one category in the taxonomy, based on the filtering, resulting in an at least one categorized sentence matched with the at least one category in the taxonomy; and outputting only the at least one categorized sentence matched with the at least one category in the taxonomy to the spreadsheet. 25. The non-transitory computer readable storage medium of claim 21 , the method further comprising converting the retrieved associated information into a text file prior to processing the associated information. | 0.574297 |
8,166,074 | 1 | 5 | 1. A digital storage medium having an index data structure for one or more data objects encoded thereon, the index data structure comprising: a) a plurality of index keys for uniquely identifying potential context nodes in a data object, each index key being associated with one or more potential context nodes, the index key having a label that provides semantic content to a user; and b) one or more routing tables associated with each index key, the one or more routing tables comprising at least 5 path references selected from a preceding peer-to-peer graph, a following peer-to-peer graph, an ancestor peer-to-peer graph, and descendent peer-to-peer graph. | 1. A digital storage medium having an index data structure for one or more data objects encoded thereon, the index data structure comprising: a) a plurality of index keys for uniquely identifying potential context nodes in a data object, each index key being associated with one or more potential context nodes, the index key having a label that provides semantic content to a user; and b) one or more routing tables associated with each index key, the one or more routing tables comprising at least 5 path references selected from a preceding peer-to-peer graph, a following peer-to-peer graph, an ancestor peer-to-peer graph, and descendent peer-to-peer graph. 5. The digital storage medium of claim 1 wherein the one or more routing tables comprising at least 10 path references. | 0.889199 |
8,117,242 | 2 | 94 | 2. A computer program product embodied on a non-transitory computer-readable medium, comprising: code for registering a global unique user login information configured to allow access to a plurality of different online applications in association with an online application system, the different online applications including a first online application that provides access to a first one or more files stored at one or more servers associated with the first online application, a second online application that provides access to a second one or more files stored at one or more servers associated with the second online application, a third online application that provides access to a third one or more files stored at one or more servers associated with the third online application, and a fourth online application that provides access to a fourth one or more files stored at one or more servers associated with the fourth online application; code for receiving the global unique user login information in connection with a user logging in; code for identifying at least one first online application identifier associated with the first online application for registration purposes; code for identifying at least one second online application identifier associated with the second online application for registration purposes; code for identifying at least one third online application identifier associated with the third online application for registration purposes; code for identifying at least one fourth online application identifier associated with the fourth online application for registration purposes; code for receiving an indication to add access to the first online application for registration purposes; code for receiving an indication to add access to the second online application for registration purposes; code for receiving an indication to add access to the third online application for registration purposes; code for receiving an indication to add access to the fourth online application for registration purposes; code for, in connection with the at least one first online application identifier associated with the first online application, allowing registration of the first online application by: utilizing data required for the first online application, and receiving preference information associated with the first online application; code for, in connection with the at least one second online application identifier associated with the second online application, allowing registration of the second online application by: utilizing data required for the second online application, and receiving preference information associated with the second online application; code for, in connection with the at least one third online application identifier associated with the third online application, allowing registration of the third online application by: utilizing data required for the third online application, and receiving preference information associated with the third online application; code for, in connection with the at least one fourth online application identifier associated with the fourth online application, allowing registration of the fourth online application by: utilizing data required for the fourth online application, and receiving preference information associated with the fourth online application; code for displaying the at least one first online application identifier associated with the first online application for access purposes; code for displaying the at least one second online application identifier associated with the second online application for access purposes; code for displaying the at least one third online application identifier associated with the third online application for access purposes; code for displaying the at least one fourth online application identifier associated with the fourth online application for access purposes; code for receiving a selection of the at least one first online application identifier associated with the first online application for access purposes; code for receiving a selection of the at least one second online application identifier associated with the second online application for access purposes; code for receiving a selection of the at least one third online application identifier associated with the third online application for access purposes; code for receiving a selection of the at least one fourth online application identifier associated with the fourth online application for access purposes; code for, in response to the selection of the at least one first online application identifier associated with the first online application for access purposes, allowing access to the first online application, utilizing the data required for the first online application; code for, in response to the selection of the at least one second online application identifier associated with the second online application for access purposes, allowing access to the second online application, utilizing the data required for the second online application; code for, in response to the selection of the at least one third online application identifier associated with the third online application for access purposes, allowing access to the third online application, utilizing the data required for the third online application; code for, in response to the selection of the at least one fourth online application identifier associated with the fourth online application for access purposes, allowing access to the fourth online application, utilizing the data required for the fourth online application; code for identifying a document in association with the online application system; code for receiving a request from a logged-in user; code for, in response to the request, displaying an interface for receiving an indication of one or more tags, the interface including: at least one text box for receiving manually inserted tags, and a list of potential tags; code for, utilizing the interface, receiving the indication of the one or more tags; and code for correlating the one or more tags with the document. | 2. A computer program product embodied on a non-transitory computer-readable medium, comprising: code for registering a global unique user login information configured to allow access to a plurality of different online applications in association with an online application system, the different online applications including a first online application that provides access to a first one or more files stored at one or more servers associated with the first online application, a second online application that provides access to a second one or more files stored at one or more servers associated with the second online application, a third online application that provides access to a third one or more files stored at one or more servers associated with the third online application, and a fourth online application that provides access to a fourth one or more files stored at one or more servers associated with the fourth online application; code for receiving the global unique user login information in connection with a user logging in; code for identifying at least one first online application identifier associated with the first online application for registration purposes; code for identifying at least one second online application identifier associated with the second online application for registration purposes; code for identifying at least one third online application identifier associated with the third online application for registration purposes; code for identifying at least one fourth online application identifier associated with the fourth online application for registration purposes; code for receiving an indication to add access to the first online application for registration purposes; code for receiving an indication to add access to the second online application for registration purposes; code for receiving an indication to add access to the third online application for registration purposes; code for receiving an indication to add access to the fourth online application for registration purposes; code for, in connection with the at least one first online application identifier associated with the first online application, allowing registration of the first online application by: utilizing data required for the first online application, and receiving preference information associated with the first online application; code for, in connection with the at least one second online application identifier associated with the second online application, allowing registration of the second online application by: utilizing data required for the second online application, and receiving preference information associated with the second online application; code for, in connection with the at least one third online application identifier associated with the third online application, allowing registration of the third online application by: utilizing data required for the third online application, and receiving preference information associated with the third online application; code for, in connection with the at least one fourth online application identifier associated with the fourth online application, allowing registration of the fourth online application by: utilizing data required for the fourth online application, and receiving preference information associated with the fourth online application; code for displaying the at least one first online application identifier associated with the first online application for access purposes; code for displaying the at least one second online application identifier associated with the second online application for access purposes; code for displaying the at least one third online application identifier associated with the third online application for access purposes; code for displaying the at least one fourth online application identifier associated with the fourth online application for access purposes; code for receiving a selection of the at least one first online application identifier associated with the first online application for access purposes; code for receiving a selection of the at least one second online application identifier associated with the second online application for access purposes; code for receiving a selection of the at least one third online application identifier associated with the third online application for access purposes; code for receiving a selection of the at least one fourth online application identifier associated with the fourth online application for access purposes; code for, in response to the selection of the at least one first online application identifier associated with the first online application for access purposes, allowing access to the first online application, utilizing the data required for the first online application; code for, in response to the selection of the at least one second online application identifier associated with the second online application for access purposes, allowing access to the second online application, utilizing the data required for the second online application; code for, in response to the selection of the at least one third online application identifier associated with the third online application for access purposes, allowing access to the third online application, utilizing the data required for the third online application; code for, in response to the selection of the at least one fourth online application identifier associated with the fourth online application for access purposes, allowing access to the fourth online application, utilizing the data required for the fourth online application; code for identifying a document in association with the online application system; code for receiving a request from a logged-in user; code for, in response to the request, displaying an interface for receiving an indication of one or more tags, the interface including: at least one text box for receiving manually inserted tags, and a list of potential tags; code for, utilizing the interface, receiving the indication of the one or more tags; and code for correlating the one or more tags with the document. 94. The computer program product of claim 2 , wherein the computer program product is configured such that a picture associated with the first online application is subject of a publishing function of the second online application. | 0.959259 |
9,275,023 | 25 | 26 | 25. A web proxy computing apparatus comprising: one or more processors; a memory coupled to the one or more processors which are configured to be capable of executing programmed instructions stored in the memory comprising: identifying a plurality of rules matching one or more elements in a HyperText Markup Language (HTML) document; identifying a plurality of actions associated with each of the identified plurality of rules; grouping each of the matching identified plurality of actions together into one or more corresponding groups; filtering the grouped plurality of actions based on one or more filtering rules when two or more of the grouped actions associated with each of the identified plurality of rules match; removing one or more of the plurality of actions in each of the one or more corresponding groups based on the one or more filtering rules; applying the remaining grouped plurality of actions that match after the filtering to transform the matching one or more elements in the HTML document; and providing the transformed HTML document. | 25. A web proxy computing apparatus comprising: one or more processors; a memory coupled to the one or more processors which are configured to be capable of executing programmed instructions stored in the memory comprising: identifying a plurality of rules matching one or more elements in a HyperText Markup Language (HTML) document; identifying a plurality of actions associated with each of the identified plurality of rules; grouping each of the matching identified plurality of actions together into one or more corresponding groups; filtering the grouped plurality of actions based on one or more filtering rules when two or more of the grouped actions associated with each of the identified plurality of rules match; removing one or more of the plurality of actions in each of the one or more corresponding groups based on the one or more filtering rules; applying the remaining grouped plurality of actions that match after the filtering to transform the matching one or more elements in the HTML document; and providing the transformed HTML document. 26. The apparatus as set forth in claim 25 wherein the one or more processors is further configured to be capable of executing programmed instructions stored in the memory further comprising: determining when two or more of the identified plurality of actions associated with each of the identified plurality of rules match. | 0.697196 |
8,595,268 | 6 | 8 | 6. A non-transitory computer-readable medium containing computer-executable instructions that, when executed by a processor, cause the processor to perform a method for compressing objects, the method comprising: receiving a request to write a first object including a first key and a first value, wherein the first object is of a given type; receiving a request to write a second object including a second key and a second value, wherein the second object is of the given type; classifying the first object to a compression dictionary according to at least one rule based on a value of the first object and/or the key of the first object; classifying the second object to the compression dictionary according to at least one rule based on a value of the second object and/or the key of the second object; and compressing the first object and the second object based on the compression dictionary; identifying first matching patterns in a pair of objects; determining if the number of first matching patterns exceeds a first threshold; when the number of first matching patterns is determined to exceed the first threshold, selecting an object from the pair of objects and identifying second matching patterns in the selected object and the compression dictionary; determining if the number of second matching patterns exceeds a second threshold; and when the number of second matching patterns is determined to exceed the second threshold, assigning the pair of objects to the compression dictionary. | 6. A non-transitory computer-readable medium containing computer-executable instructions that, when executed by a processor, cause the processor to perform a method for compressing objects, the method comprising: receiving a request to write a first object including a first key and a first value, wherein the first object is of a given type; receiving a request to write a second object including a second key and a second value, wherein the second object is of the given type; classifying the first object to a compression dictionary according to at least one rule based on a value of the first object and/or the key of the first object; classifying the second object to the compression dictionary according to at least one rule based on a value of the second object and/or the key of the second object; and compressing the first object and the second object based on the compression dictionary; identifying first matching patterns in a pair of objects; determining if the number of first matching patterns exceeds a first threshold; when the number of first matching patterns is determined to exceed the first threshold, selecting an object from the pair of objects and identifying second matching patterns in the selected object and the compression dictionary; determining if the number of second matching patterns exceeds a second threshold; and when the number of second matching patterns is determined to exceed the second threshold, assigning the pair of objects to the compression dictionary. 8. The non-transitory medium of claim 6 , the method further comprising writing the first object to an in-memory, non-relational data store as an uncompressed object before the first object is compressed, and overwriting the uncompressed object with a compressed form of the first objected object when the first object is compressed. | 0.501497 |
9,223,897 | 16 | 19 | 16. A system comprising: one or more server devices comprising: one or more memory devices to store instructions; and one or more processors to execute the instructions to: obtain a rank position map, the rank position map specifying, for a plurality of rank positions, an expected utility rate for a document presented at a particular one of the plurality of rank positions as a search result, the expected utility rate being derived from an aggregate quantity of selections of the document presented as a search result at the particular one of the plurality of rank positions; determine an expected utility rate, for a particular document, based on the obtained rank position map and based on a quantity of times that the particular document was presented as a search result at particular ones of the plurality of rank positions; determine an actual utility rate for the particular document based on an actual quantity of selections of the particular document and based on a total quantity of times that the particular document was presented as a search result; calculate a correction factor, for the particular document, based on the determined expected utility rate and the determined actual utility rate; determine a confidence level for the correction factor, the confidence level representing a measure of confidence associated with the correction factor; adjust the correction factor based on the determined confidence level; select, based on determining that the confidence level is less than a threshold, a set of documents, the particular document being included in the set of documents; calculate an aggregated correction factor for the set of documents; use the aggregated correction factor as the correction factor for the particular document; adjust a score of the particular document based on the correction factor; and provide, for presentation, a ranked list of search results, one of the search results corresponding to the particular document, and a ranking of the search result corresponding to the particular document being based on the adjusted score. | 16. A system comprising: one or more server devices comprising: one or more memory devices to store instructions; and one or more processors to execute the instructions to: obtain a rank position map, the rank position map specifying, for a plurality of rank positions, an expected utility rate for a document presented at a particular one of the plurality of rank positions as a search result, the expected utility rate being derived from an aggregate quantity of selections of the document presented as a search result at the particular one of the plurality of rank positions; determine an expected utility rate, for a particular document, based on the obtained rank position map and based on a quantity of times that the particular document was presented as a search result at particular ones of the plurality of rank positions; determine an actual utility rate for the particular document based on an actual quantity of selections of the particular document and based on a total quantity of times that the particular document was presented as a search result; calculate a correction factor, for the particular document, based on the determined expected utility rate and the determined actual utility rate; determine a confidence level for the correction factor, the confidence level representing a measure of confidence associated with the correction factor; adjust the correction factor based on the determined confidence level; select, based on determining that the confidence level is less than a threshold, a set of documents, the particular document being included in the set of documents; calculate an aggregated correction factor for the set of documents; use the aggregated correction factor as the correction factor for the particular document; adjust a score of the particular document based on the correction factor; and provide, for presentation, a ranked list of search results, one of the search results corresponding to the particular document, and a ranking of the search result corresponding to the particular document being based on the adjusted score. 19. The system of claim 16 , where the rank position map is associated with at least one of a particular language, a particular document type, or a particular query type. | 0.896719 |
8,375,014 | 28 | 30 | 28. A system comprising: one or more processors; an input handler executed by at least one of the processors and configured to receive a keyword by which to search a content index of a database for a corresponding data source from a plurality of data sources associated with the database, the data sources including fields and data populating the database; a search engine executed by at least one of the processors and configured to search the content index for the keyword, and provide a data source, field and/or data list from the content index corresponding to the keyword; the input handler being configured to receive a selection from the data source, field and/or data list; a schema parser configured to identify the corresponding data source, field and/or data list for the selected data source; and a query engine executed by at least one of the processors and configured to provide, responsive to the selection of the data source, field and/or data list, a first graphical icon in a graphical user interface, the first graphical icon representing the selected data source, field and/or data list and to provide in the graphical user interface a second graphical icon representing a corresponding second data source, second field and/or second data list, wherein the input handler is configured to determine that the first graphical icon is graphically associated within the graphical user interface with the second graphical icon; and a translation engine configured to provide, responsive to the graphical association of the first and second graphical icons, a list of operations to perform between the selected data source, field and/or data list and the second data source, second field and/or second data list, wherein the operations are provided in a natural language expression of the operations, wherein the query engine is configured to receive a selection of one of the provided operations and to query the database using query parameters based on the selected data source, field and/or data list, the second data source, second field and/or second data list and the selected operation; and a logic engine executed by at least one of the processors and configured to assemble a machine readable structured query language (SQL) query based on the graphical depiction of the query, wherein the logic engine comprises a plurality of different subroutines, each different subroutine being configured to process a different type of operation represented by a particular type of element in the graphical query to generate a SQL substatement of the elements operation, and wherein the logic engine is further configured to incorporate the SQL substatements into a complete machine readable SQL query. | 28. A system comprising: one or more processors; an input handler executed by at least one of the processors and configured to receive a keyword by which to search a content index of a database for a corresponding data source from a plurality of data sources associated with the database, the data sources including fields and data populating the database; a search engine executed by at least one of the processors and configured to search the content index for the keyword, and provide a data source, field and/or data list from the content index corresponding to the keyword; the input handler being configured to receive a selection from the data source, field and/or data list; a schema parser configured to identify the corresponding data source, field and/or data list for the selected data source; and a query engine executed by at least one of the processors and configured to provide, responsive to the selection of the data source, field and/or data list, a first graphical icon in a graphical user interface, the first graphical icon representing the selected data source, field and/or data list and to provide in the graphical user interface a second graphical icon representing a corresponding second data source, second field and/or second data list, wherein the input handler is configured to determine that the first graphical icon is graphically associated within the graphical user interface with the second graphical icon; and a translation engine configured to provide, responsive to the graphical association of the first and second graphical icons, a list of operations to perform between the selected data source, field and/or data list and the second data source, second field and/or second data list, wherein the operations are provided in a natural language expression of the operations, wherein the query engine is configured to receive a selection of one of the provided operations and to query the database using query parameters based on the selected data source, field and/or data list, the second data source, second field and/or second data list and the selected operation; and a logic engine executed by at least one of the processors and configured to assemble a machine readable structured query language (SQL) query based on the graphical depiction of the query, wherein the logic engine comprises a plurality of different subroutines, each different subroutine being configured to process a different type of operation represented by a particular type of element in the graphical query to generate a SQL substatement of the elements operation, and wherein the logic engine is further configured to incorporate the SQL substatements into a complete machine readable SQL query. 30. The system of claim 28 wherein the search engine is configured to provide the data source list based on level of relevance between each data source and the keyword. | 0.894605 |
8,831,946 | 1 | 8 | 1. A computer-implemented method of searching speech data comprising in-vocabulary and out-of-vocabulary words, the method comprising, via a computer processor executing stored program instructions: Receiving, by the computer processor, a search query comprising a phrase comprising at least one in-vocabulary word and at least one out-of-vocabulary word; extracting, by the computer processor, search terms from the phrase, the search terms comprising at least one in-vocabulary search term and at least one out-of-vocabulary search term; retrieving, by the computer processor, a first list of occurrences of words for the at least one in-vocabulary search term, the first list retrieved from a first index of words having first timestamps; retrieving, by the computer processor, a second list of occurrences of sub-words for the at least one out-of-vocabulary search term, the second list retrieved from a second index of sub-words having second timestamps; and merging, by the computer processor, the first list of occurrences of words and the second list of occurrences of sub-words to create a merged list, wherein merging the first list and the second list comprises evaluating the first timestamps and the second timestamps and adding to the merged list occurrences of combinations of words from the first list and sub-words from the second list that satisfy at least one evaluation criterion, wherein the at least one evaluation criterion comprises a threshold for a difference between first and second timestamps. | 1. A computer-implemented method of searching speech data comprising in-vocabulary and out-of-vocabulary words, the method comprising, via a computer processor executing stored program instructions: Receiving, by the computer processor, a search query comprising a phrase comprising at least one in-vocabulary word and at least one out-of-vocabulary word; extracting, by the computer processor, search terms from the phrase, the search terms comprising at least one in-vocabulary search term and at least one out-of-vocabulary search term; retrieving, by the computer processor, a first list of occurrences of words for the at least one in-vocabulary search term, the first list retrieved from a first index of words having first timestamps; retrieving, by the computer processor, a second list of occurrences of sub-words for the at least one out-of-vocabulary search term, the second list retrieved from a second index of sub-words having second timestamps; and merging, by the computer processor, the first list of occurrences of words and the second list of occurrences of sub-words to create a merged list, wherein merging the first list and the second list comprises evaluating the first timestamps and the second timestamps and adding to the merged list occurrences of combinations of words from the first list and sub-words from the second list that satisfy at least one evaluation criterion, wherein the at least one evaluation criterion comprises a threshold for a difference between first and second timestamps. 8. The method of claim 1 , further comprising comparing the first timestamps and the second timestamps to check that a difference in time between adjacent words and sub-words is under the threshold. | 0.830479 |
8,086,442 | 1 | 4 | 1. One or more tangible computer-readable storage media that store executable instructions to divide an input into segments, wherein the instructions, when executed by a computer, cause the computer to perform acts comprising: representing one or more segment breaking rules in a first regular expression; combining a plurality of exceptions to said one or more segment breaking rules disjunctively into a second regular expression, said second regular expression being distinct from said first regular expression; finding first strings in said input that match said second regular expression; replacing said first strings with placeholders to create a second string, wherein said second string comprises said input but with said placeholders in place of said first strings; subsequent to said finding and said replacing, using said first regular expression to detect segment break points in said second string; and subsequent to detecting said segment break points, replacing said placeholders in said second string with said first strings. | 1. One or more tangible computer-readable storage media that store executable instructions to divide an input into segments, wherein the instructions, when executed by a computer, cause the computer to perform acts comprising: representing one or more segment breaking rules in a first regular expression; combining a plurality of exceptions to said one or more segment breaking rules disjunctively into a second regular expression, said second regular expression being distinct from said first regular expression; finding first strings in said input that match said second regular expression; replacing said first strings with placeholders to create a second string, wherein said second string comprises said input but with said placeholders in place of said first strings; subsequent to said finding and said replacing, using said first regular expression to detect segment break points in said second string; and subsequent to detecting said segment break points, replacing said placeholders in said second string with said first strings. 4. The one or more tangible computer-readable storage media of claim 1 , wherein a first one of said exceptions has both a before rule and an after rule, and wherein said first one of said exceptions is included in said second regular expression as a regular expression representing said before rule followed by a non-capture group containing a regular expression representing said after rule. | 0.823609 |
9,319,469 | 1 | 3 | 1. A method for securely communicating between a host and a service application running on a selected external application server to allow a service application running on the external application server to access a document maintained by the host, said method comprising the steps of: initiating a transaction, by the host, with the selected external application server by transmitting an action request from the host to the service application running on the selected external application server, the action request being against an entry point address associated with the service application; initiating a communication with the selected external application server to obtain a proof key adapted to validate a proof signature; receiving said proof key in response to said communication; providing the selected external application server with an access token and a document identifier for use in fulfilling said action request; receiving a metadata request comprising said access token and said document identifier; validating said access token prior to responding to said metadata request; sending a metadata response comprising selected metadata based on said action request when said access token is valid; receiving a content request comprising said access token and said document identifier; validating said access token prior to responding to said content request; and sending a content response comprising content from the document identified by said document identifier when said access token is valid. | 1. A method for securely communicating between a host and a service application running on a selected external application server to allow a service application running on the external application server to access a document maintained by the host, said method comprising the steps of: initiating a transaction, by the host, with the selected external application server by transmitting an action request from the host to the service application running on the selected external application server, the action request being against an entry point address associated with the service application; initiating a communication with the selected external application server to obtain a proof key adapted to validate a proof signature; receiving said proof key in response to said communication; providing the selected external application server with an access token and a document identifier for use in fulfilling said action request; receiving a metadata request comprising said access token and said document identifier; validating said access token prior to responding to said metadata request; sending a metadata response comprising selected metadata based on said action request when said access token is valid; receiving a content request comprising said access token and said document identifier; validating said access token prior to responding to said content request; and sending a content response comprising content from the document identified by said document identifier when said access token is valid. 3. The method of claim 1 characterized in that at least one of said access token and said document identifier are provided in said action request. | 0.9234 |
10,061,769 | 4 | 5 | 4. The machine translation method according to claim 1 , wherein the information output device has a processor and a display; and wherein the machine translation method further comprises: displaying the plurality of backward-translated sentences in a first area on the display; and displaying the translation-source sentence in a second area on the display, the second area being different from the first area. | 4. The machine translation method according to claim 1 , wherein the information output device has a processor and a display; and wherein the machine translation method further comprises: displaying the plurality of backward-translated sentences in a first area on the display; and displaying the translation-source sentence in a second area on the display, the second area being different from the first area. 5. The machine translation method according to claim 4 , further comprising: displaying, in a third area on the display, the forward-translated sentence corresponding to the selected backward-translated sentence. | 0.933081 |
9,690,935 | 1 | 12 | 1. A method comprising: obtaining, by a visual algorithm stored in memory and executed by at least one processor of a first computer, a candidate character string associated with a potentially malicious computer item operating on a second computer; generating, by the visual algorithm during execution by the at least one processor, a first visual identifier (ID) by at least translating the candidate character string into the first visual ID in accordance with one or more translation rules stored on the first computer, the first visual ID is different from the candidate character string; generating a value representing a characteristic of the potentially malicious computer item, the characteristic being associated with a size of the potentially malicious computer item or a memory location associated with the potentially malicious computer item; analyzing the first virtual ID with a reference ID where a comparison between the first virtual ID and the reference ID is used to determine whether the potentially malicious computer item should be identified as a malicious computer item; and in response to the comparison between the first virtual ID and the reference ID being indeterminate as to whether the potentially malicious computer item is to be identified as a malicious computer item, further analyzing the characteristic of the potentially malicious computer item by determining whether the value falls outside an expected range of values associated with a non-malicious computer item. | 1. A method comprising: obtaining, by a visual algorithm stored in memory and executed by at least one processor of a first computer, a candidate character string associated with a potentially malicious computer item operating on a second computer; generating, by the visual algorithm during execution by the at least one processor, a first visual identifier (ID) by at least translating the candidate character string into the first visual ID in accordance with one or more translation rules stored on the first computer, the first visual ID is different from the candidate character string; generating a value representing a characteristic of the potentially malicious computer item, the characteristic being associated with a size of the potentially malicious computer item or a memory location associated with the potentially malicious computer item; analyzing the first virtual ID with a reference ID where a comparison between the first virtual ID and the reference ID is used to determine whether the potentially malicious computer item should be identified as a malicious computer item; and in response to the comparison between the first virtual ID and the reference ID being indeterminate as to whether the potentially malicious computer item is to be identified as a malicious computer item, further analyzing the characteristic of the potentially malicious computer item by determining whether the value falls outside an expected range of values associated with a non-malicious computer item. 12. The method of claim 1 , displaying the first visual ID with a second visual ID on a visual display wherein, the first visual ID and the second visual IDs are arranged according to a timeline of computer events involving associated computer items under analysis by the visual algorithm being executed by the at least one processor. | 0.647679 |
9,679,001 | 14 | 16 | 14. A consensus search method comprising: (a) providing a semantic data index by dividing and indexing an electronic document describing at least one content object into segments, which refer to a predetermined minimal number of words, phrases, clauses, sentences or paragraphs; and (b) retrieving a query object related to a query based on the semantic data index, wherein the semantic data index is generated by extracting at least one semantic descriptor from the each segmented text data, and matching each of the extracted semantic descriptor to the content object and the each segmented text data, wherein the text data is divided into the segments by units of meaning, the semantic descriptor is at least one word that has a meaning, and the content object represents a topic or an entity that a user intends to describe in the electronic document, wherein the process of (b) comprises: (b1) generating at least one search keyword by dividing the query into words, (b2) generating extended keywords including at least one of a synonym, a hyponym and a hypernym of the search keywords, (b3) searching a semantic descriptor from each keyword and searching segmented text data included in the content object corresponding to the each semantic descriptor based on the semantic data index, (b4) detecting first searched objects of which identification information is identical among all of the searched the content objects for included in the search and the semantic descriptors and extracting some segmented text data of which identification information is identical among all of the segmented text data included in the first searched objects, (b5) if there are, in the query, two semantic descriptors corresponding to the keywords of which sentiments are opposite, detecting second searched objects of which identification is identical among all of the searched the content objects for included in the search and the two semantic descriptors and extracting all of the segmented text data in the second searched objects for the two semantic descriptors, (b6) giving a score to the each of the first and second searched objects depending on a number of the extracted segmented text data, wherein a weight to the first and/or second searched object matched to the semantic descriptor corresponding to the search keywords is set higher than a weight to the first and/or second searched object matched to the semantic descriptor corresponding to the extended keywords, and (b7) outputting the first and second searched objects as a search result in a descending order of the score. | 14. A consensus search method comprising: (a) providing a semantic data index by dividing and indexing an electronic document describing at least one content object into segments, which refer to a predetermined minimal number of words, phrases, clauses, sentences or paragraphs; and (b) retrieving a query object related to a query based on the semantic data index, wherein the semantic data index is generated by extracting at least one semantic descriptor from the each segmented text data, and matching each of the extracted semantic descriptor to the content object and the each segmented text data, wherein the text data is divided into the segments by units of meaning, the semantic descriptor is at least one word that has a meaning, and the content object represents a topic or an entity that a user intends to describe in the electronic document, wherein the process of (b) comprises: (b1) generating at least one search keyword by dividing the query into words, (b2) generating extended keywords including at least one of a synonym, a hyponym and a hypernym of the search keywords, (b3) searching a semantic descriptor from each keyword and searching segmented text data included in the content object corresponding to the each semantic descriptor based on the semantic data index, (b4) detecting first searched objects of which identification information is identical among all of the searched the content objects for included in the search and the semantic descriptors and extracting some segmented text data of which identification information is identical among all of the segmented text data included in the first searched objects, (b5) if there are, in the query, two semantic descriptors corresponding to the keywords of which sentiments are opposite, detecting second searched objects of which identification is identical among all of the searched the content objects for included in the search and the two semantic descriptors and extracting all of the segmented text data in the second searched objects for the two semantic descriptors, (b6) giving a score to the each of the first and second searched objects depending on a number of the extracted segmented text data, wherein a weight to the first and/or second searched object matched to the semantic descriptor corresponding to the search keywords is set higher than a weight to the first and/or second searched object matched to the semantic descriptor corresponding to the extended keywords, and (b7) outputting the first and second searched objects as a search result in a descending order of the score. 16. The consensus search method of claim 14 , wherein in the process of (b4), assigning an extra point based on at least one of an author of the segmented text data, a website on which the text data is posted, time point when the text data is posted and information of users' evaluation upon the text data. | 0.586486 |
9,294,591 | 8 | 9 | 8. A device, comprising: a memory storing executable instructions; and a processor coupled to the memory, wherein the executable instructions facilitate performance of operations comprising: receiving a query from a terminating call session control function server, wherein the query comprises a fully qualified domain name associated with a calling device, and wherein the query is received responsive to the terminating call session control function receiving a request from an originating call session control function server for a call session for the calling device and a terminating device over a communication network; and transmitting an answer to the terminating call session control function server responsive to the query, wherein the answer comprises one of an internet protocol version 4 address with a first error indicator, an internet protocol version 6 address with a second error indicator, or a combination thereof, wherein the answer is analyzed by the terminating call session control function server to determine an answer combination according to a combination of the first error indicator and the second error indicator, and wherein a response is transmitted by the terminating call session control function server to the calling device via one of the internet protocol version 4 address or the internet protocol version 6 address according to the answer combination that is determined. | 8. A device, comprising: a memory storing executable instructions; and a processor coupled to the memory, wherein the executable instructions facilitate performance of operations comprising: receiving a query from a terminating call session control function server, wherein the query comprises a fully qualified domain name associated with a calling device, and wherein the query is received responsive to the terminating call session control function receiving a request from an originating call session control function server for a call session for the calling device and a terminating device over a communication network; and transmitting an answer to the terminating call session control function server responsive to the query, wherein the answer comprises one of an internet protocol version 4 address with a first error indicator, an internet protocol version 6 address with a second error indicator, or a combination thereof, wherein the answer is analyzed by the terminating call session control function server to determine an answer combination according to a combination of the first error indicator and the second error indicator, and wherein a response is transmitted by the terminating call session control function server to the calling device via one of the internet protocol version 4 address or the internet protocol version 6 address according to the answer combination that is determined. 9. The device of claim 8 , wherein the query comprises an A type query and an AAAA type query based on the fully qualified domain name. | 0.800885 |
10,162,850 | 8 | 13 | 8. A processor readable non-transitory storage media that includes instructions for managing document over a network, wherein execution of the instructions by one or more processors on one or more network computers performs actions, comprising: instantiating a document engine to identify one or more clauses of natural language words in a document, wherein the document is associated with one or more document types, and wherein the one or more clauses are included in a data object as one or more self-referential records to improve the performance of computing resources employed to execute the instructions; and instantiating a validation engine to perform actions, including: providing one or more evaluations for the one or more clauses based on one or more evaluators and one or more machine learning (ML) models, wherein the one or more evaluations are employed to assign each of the clauses to one of a plurality of categories, and wherein each of the one or more clauses is associated with a confidence score based on the one or more evaluations; providing one or more semantic evaluators based on the one or more document types; assigning the category and the confidence score to the one or more clauses based on the one or more evaluations performed by one or more of textual evaluators or the semantic evaluators; determining the one or more ML models based on the category assigned to the one or more clauses; classifying the one or more clauses based on the one or more ML models; employing a result of the classification to modify the confidence score associated with the one or more clauses; monitoring one or more actions associated with the one or more clauses, wherein the one or more actions include one or more updates to content of the one or more clauses; and identifying one or more inconsistent evaluations, wherein the inconsistent evaluations are associated with the one or more clauses that have a confidence score that exceeds a high threshold value and also a quantity of content updates that exceeds another high threshold value; and instantiating a machine learning (ML) engine to perform actions including: retraining a portion of the one or more ML models based on the updated content of the one or more clauses associated with the inconsistent evaluations, wherein the retrained portion of the one or more ML models associate one or more increased confidence scores with each of the one or more clauses that already include content that is equivalent to the content updates of the one or more clauses associated with the inconsistent evaluations, and wherein the retrained portion of the one or more ML models associate one or more decreased confidence scores with the one or more clauses associated with the one or more inconsistent evaluations. | 8. A processor readable non-transitory storage media that includes instructions for managing document over a network, wherein execution of the instructions by one or more processors on one or more network computers performs actions, comprising: instantiating a document engine to identify one or more clauses of natural language words in a document, wherein the document is associated with one or more document types, and wherein the one or more clauses are included in a data object as one or more self-referential records to improve the performance of computing resources employed to execute the instructions; and instantiating a validation engine to perform actions, including: providing one or more evaluations for the one or more clauses based on one or more evaluators and one or more machine learning (ML) models, wherein the one or more evaluations are employed to assign each of the clauses to one of a plurality of categories, and wherein each of the one or more clauses is associated with a confidence score based on the one or more evaluations; providing one or more semantic evaluators based on the one or more document types; assigning the category and the confidence score to the one or more clauses based on the one or more evaluations performed by one or more of textual evaluators or the semantic evaluators; determining the one or more ML models based on the category assigned to the one or more clauses; classifying the one or more clauses based on the one or more ML models; employing a result of the classification to modify the confidence score associated with the one or more clauses; monitoring one or more actions associated with the one or more clauses, wherein the one or more actions include one or more updates to content of the one or more clauses; and identifying one or more inconsistent evaluations, wherein the inconsistent evaluations are associated with the one or more clauses that have a confidence score that exceeds a high threshold value and also a quantity of content updates that exceeds another high threshold value; and instantiating a machine learning (ML) engine to perform actions including: retraining a portion of the one or more ML models based on the updated content of the one or more clauses associated with the inconsistent evaluations, wherein the retrained portion of the one or more ML models associate one or more increased confidence scores with each of the one or more clauses that already include content that is equivalent to the content updates of the one or more clauses associated with the inconsistent evaluations, and wherein the retrained portion of the one or more ML models associate one or more decreased confidence scores with the one or more clauses associated with the one or more inconsistent evaluations. 13. The media of claim 8 , wherein the document engine performs further actions including: scanning the document to determine its format and file type; and parsing the document based on its file format and file type to provide the one or more clauses. | 0.855913 |
8,682,677 | 8 | 13 | 8. A system comprising: a processor; and a computer-readable storage medium having instructions stored which, when executed by the processor, cause the processor to perform operations comprising: identifying features from a set of user interactions; identifying a policy for using the features in developing a dialog manager; performing, based on the policy, a linear evaluation on the features, to yield a set of features; repeating a cubic policy process on the set of features until the set of features results in a reduced set of features having a quantity below a threshold, the cubic policy process comprising a least-squares policy iteration algorithm; and generating the dialog manager using a modified set of user interactions, the modified set of user interactions being selected based on the reduced set of features. | 8. A system comprising: a processor; and a computer-readable storage medium having instructions stored which, when executed by the processor, cause the processor to perform operations comprising: identifying features from a set of user interactions; identifying a policy for using the features in developing a dialog manager; performing, based on the policy, a linear evaluation on the features, to yield a set of features; repeating a cubic policy process on the set of features until the set of features results in a reduced set of features having a quantity below a threshold, the cubic policy process comprising a least-squares policy iteration algorithm; and generating the dialog manager using a modified set of user interactions, the modified set of user interactions being selected based on the reduced set of features. 13. The system of claim 8 , wherein the set of user interactions is received in real-time. | 0.825581 |
10,121,466 | 9 | 14 | 9. A method for re-training a speaker-independent speech recognition system with respect to a word of an application vocabulary, wherein a generic voice template is assigned to said word in the application vocabulary, the method comprising: acquiring from a user a speech sample of said word using the speaker-independent speech recognition system; comparing, via at least one processor, the speech sample to generic voice templates in the application vocabulary; and if the speech sample matches more than one of the generic voice templates in the application vocabulary, then: prompting, via the at least one processor, the user to create a custom voice template for a substitute word, training, via the at least one processor, the speaker-independent speech recognition system on the substitute word to create the custom voice template for the substitute word, and replacing, via the at least one processor, in the application vocabulary the generic voice template for said word with the custom voice template for the substitute word; and otherwise, if the speech sample matches the generic voice template for said word, using, via the at least one processor, the generic voice template for the word; wherein, during the re-training, the comparison of the speech sample of said word to generic voice templates in the application vocabulary is performed until the custom voice template for the substitute word which is different from the generic voice templates and having no template similarity with the generic voice templates is created; wherein the re-training is initiated after an initial enrollment training performed for the speech recognition system before use based on an outcome of a performance evaluation performed periodically by the speech recognition system; and wherein the performance evaluation is associated with recognition performance for the word. | 9. A method for re-training a speaker-independent speech recognition system with respect to a word of an application vocabulary, wherein a generic voice template is assigned to said word in the application vocabulary, the method comprising: acquiring from a user a speech sample of said word using the speaker-independent speech recognition system; comparing, via at least one processor, the speech sample to generic voice templates in the application vocabulary; and if the speech sample matches more than one of the generic voice templates in the application vocabulary, then: prompting, via the at least one processor, the user to create a custom voice template for a substitute word, training, via the at least one processor, the speaker-independent speech recognition system on the substitute word to create the custom voice template for the substitute word, and replacing, via the at least one processor, in the application vocabulary the generic voice template for said word with the custom voice template for the substitute word; and otherwise, if the speech sample matches the generic voice template for said word, using, via the at least one processor, the generic voice template for the word; wherein, during the re-training, the comparison of the speech sample of said word to generic voice templates in the application vocabulary is performed until the custom voice template for the substitute word which is different from the generic voice templates and having no template similarity with the generic voice templates is created; wherein the re-training is initiated after an initial enrollment training performed for the speech recognition system before use based on an outcome of a performance evaluation performed periodically by the speech recognition system; and wherein the performance evaluation is associated with recognition performance for the word. 14. The method according to claim 9 , wherein the substitute word comprises a new word chosen by a user that is different from the word. | 0.942373 |
9,135,314 | 19 | 20 | 19. A non-transient computer readable medium comprising: executable code that when executed by a processor of a computer system cause the processer to: load a multidimensional data set having a plurality of data fields into a memory of the computer system, retrieve, automatically without user intervention, an analysis digest specification associated with a particular user, wherein: the analysis digest specification includes a plurality of specifications for discrete analysis digests, each of the plurality of specifications specifies: one or more definitions for a particular analysis results, wherein the one or more definitions specify a pre-determined subset of data fields, in the plurality of data fields, customized by a first user, and a visual representation of the particular analysis results, parse the multidimensional data set according to the analysis digest specification to generate a first parsed multidimensional data set, generates a first plurality of analysis digests in response to the first parsed multidimensional data set and the analysis digest specification, determines that a third user is associated with the particular user, retrieves, automatically without user intervention, another analysis digest specification associated with the third user that is different than the analysis digest specification associated with the particular user, generates, an additional analysis digest in response to the first parsed multidimensional data set and the another analysis digest specification, wherein the additional analysis digest is different than the plurality of analysis digests, and assigns the first plurality of analysis digests and the additional analysis digest to a second user by causing the first plurality of analysis digests and the additional analysis digest to be automatically displayed to the second user, wherein the first user is associated with a first business role, and the second user is associated with a second business role different from the first business role. | 19. A non-transient computer readable medium comprising: executable code that when executed by a processor of a computer system cause the processer to: load a multidimensional data set having a plurality of data fields into a memory of the computer system, retrieve, automatically without user intervention, an analysis digest specification associated with a particular user, wherein: the analysis digest specification includes a plurality of specifications for discrete analysis digests, each of the plurality of specifications specifies: one or more definitions for a particular analysis results, wherein the one or more definitions specify a pre-determined subset of data fields, in the plurality of data fields, customized by a first user, and a visual representation of the particular analysis results, parse the multidimensional data set according to the analysis digest specification to generate a first parsed multidimensional data set, generates a first plurality of analysis digests in response to the first parsed multidimensional data set and the analysis digest specification, determines that a third user is associated with the particular user, retrieves, automatically without user intervention, another analysis digest specification associated with the third user that is different than the analysis digest specification associated with the particular user, generates, an additional analysis digest in response to the first parsed multidimensional data set and the another analysis digest specification, wherein the additional analysis digest is different than the plurality of analysis digests, and assigns the first plurality of analysis digests and the additional analysis digest to a second user by causing the first plurality of analysis digests and the additional analysis digest to be automatically displayed to the second user, wherein the first user is associated with a first business role, and the second user is associated with a second business role different from the first business role. 20. The non-transient computer readable medium of claim 19 wherein the one or more definitions for the particular analysis results comprise an association with an associated user. | 0.839029 |
9,679,155 | 8 | 13 | 8. A system for performing prefix search of encrypted cloud stored data, comprising: a network proxy server comprising a computer and configured as a network intermediary between a user device and a cloud storage service storing encrypted files being encrypted using an order-preserving encryption algorithm, the network proxy server being configured to receive a search request with a search term directed to encrypted files stored in a cloud storage service, to generate a minimum possible plaintext string using the search term as prefix and padding the search term to a first character length using one or more trailing characters indicative of a minimum possible value related to the search term, to generate a maximum possible plaintext string using the search term as prefix and padding the search term to the first character length using one or more trailing characters indicative of a maximum possible value related to the search term, to encrypt the minimum possible plaintext string and the maximum possible plaintext string using the order-preserving encryption algorithm used to encrypt the cloud stored encrypted files, to generate a minimum ciphertext from the minimum possible plaintext string and a maximum ciphertext from the maximum possible plaintext string, to determine a set of common leading digits from the minimum ciphertext and the maximum ciphertext, to generate a cloud service search request including the set of common leading digits as an encrypted prefix search term, to send the cloud service search request with the encrypted prefix search term to the cloud storage service, and to receive a search result from the cloud storage service. | 8. A system for performing prefix search of encrypted cloud stored data, comprising: a network proxy server comprising a computer and configured as a network intermediary between a user device and a cloud storage service storing encrypted files being encrypted using an order-preserving encryption algorithm, the network proxy server being configured to receive a search request with a search term directed to encrypted files stored in a cloud storage service, to generate a minimum possible plaintext string using the search term as prefix and padding the search term to a first character length using one or more trailing characters indicative of a minimum possible value related to the search term, to generate a maximum possible plaintext string using the search term as prefix and padding the search term to the first character length using one or more trailing characters indicative of a maximum possible value related to the search term, to encrypt the minimum possible plaintext string and the maximum possible plaintext string using the order-preserving encryption algorithm used to encrypt the cloud stored encrypted files, to generate a minimum ciphertext from the minimum possible plaintext string and a maximum ciphertext from the maximum possible plaintext string, to determine a set of common leading digits from the minimum ciphertext and the maximum ciphertext, to generate a cloud service search request including the set of common leading digits as an encrypted prefix search term, to send the cloud service search request with the encrypted prefix search term to the cloud storage service, and to receive a search result from the cloud storage service. 13. The system of claim 8 , wherein the network proxy server is further configured to determine the character type of the search term and to pad one or more trailing characters to the search term using the maximum possible value associated with the character type of the search term. | 0.629581 |
7,610,547 | 20 | 21 | 20. The computer readable storage medium of claim 14 and wherein the input comprises speech, and further including instructions, which when implemented, comprise: processing the input speech to provide data indicative of the input speech. | 20. The computer readable storage medium of claim 14 and wherein the input comprises speech, and further including instructions, which when implemented, comprise: processing the input speech to provide data indicative of the input speech. 21. The computer readable storage medium of claim 20 wherein processing includes normalizing the data indicative of the input speech. | 0.951672 |
7,987,443 | 8 | 9 | 8. The computer-based user interface according to claim 7 wherein: the dialog control element is configured for modifying information associated with creating and/or destroying controls in the dialog according to the user interaction. | 8. The computer-based user interface according to claim 7 wherein: the dialog control element is configured for modifying information associated with creating and/or destroying controls in the dialog according to the user interaction. 9. The computer-based user interface according to claim 8 wherein: the dialog control element is configured for creating and/or destroying objects in object properties in reflection of user addition and/or removal of objects from a vector control. | 0.905653 |
9,740,689 | 7 | 11 | 7. A method for detecting dates in digital text written in Farsi, the method comprising operation of: (a) receiving, with one or more processors, a string of digital text from a social media source; (b) searching, with the one or more processors, for a date in the string of digital text by searching for one of the words yesterday, today and tomorrow in Farsi as follows: a. if one of the words is found, then providing temporal textCreationDate tag for the text based on the date the digital text was created and the found word and then stopping for the string of digital text; b. if one of the words is not found, then continuing; (c) performing, by the one or more processors, a month search in the string of digital text by searching for a name of a month; a. if a name of the month is found, then performing a day of the month search; i. if a day of the month is found, then converting the day of the month to Gregorian calendar and logging the day of the month; ii. if a day of the month is not found, then stopping; b. if a name of the month is not found, then continuing; (d) performing, by the one or more processors, a weekday name search by looking for weekday names within the string of text followed by a word in Farsi having a meaning of “next”; a. if a weekday name is found following by a word in Farsi having a meaning of “next,” then identifying a date for a first occurrence of name of a weekday after the date the digital text was created; b. if a weekday name is not found, then continuing; (e) searching, by the one or more processors, for a year-month-date label in the string of digital text; a. if a year-month-date label is not found, then stopping; b. if a year-month-date label is found, then continuing; and (f) convening, by the one or more processors, the year-month-date label to Gregorian calendar and logging the day of the month. | 7. A method for detecting dates in digital text written in Farsi, the method comprising operation of: (a) receiving, with one or more processors, a string of digital text from a social media source; (b) searching, with the one or more processors, for a date in the string of digital text by searching for one of the words yesterday, today and tomorrow in Farsi as follows: a. if one of the words is found, then providing temporal textCreationDate tag for the text based on the date the digital text was created and the found word and then stopping for the string of digital text; b. if one of the words is not found, then continuing; (c) performing, by the one or more processors, a month search in the string of digital text by searching for a name of a month; a. if a name of the month is found, then performing a day of the month search; i. if a day of the month is found, then converting the day of the month to Gregorian calendar and logging the day of the month; ii. if a day of the month is not found, then stopping; b. if a name of the month is not found, then continuing; (d) performing, by the one or more processors, a weekday name search by looking for weekday names within the string of text followed by a word in Farsi having a meaning of “next”; a. if a weekday name is found following by a word in Farsi having a meaning of “next,” then identifying a date for a first occurrence of name of a weekday after the date the digital text was created; b. if a weekday name is not found, then continuing; (e) searching, by the one or more processors, for a year-month-date label in the string of digital text; a. if a year-month-date label is not found, then stopping; b. if a year-month-date label is found, then continuing; and (f) convening, by the one or more processors, the year-month-date label to Gregorian calendar and logging the day of the month. 11. The method as set forth in claim 7 , wherein performing the month search further includes searching for names of the month from both Persian and Gregorian calendars and French pronunciations of Gregorian month names. | 0.614035 |
8,316,019 | 1 | 6 | 1. A computer-implemented method, comprising: receiving a search query, the search query associated with a user identifier; receiving a ranked list of related search queries for the received search query, the related search queries being suggested alternate queries for the search query and ranked according to a first order; accessing a profile tree associated with the user identifier and including a hierarchy of nodes, the hierarchy of nodes including a root node and a plurality of child nodes, each child node descending from the root node or another child node, the profile tree defining a plurality of levels, each level including child nodes that descend from the root node at a same depth, and each node of the profile tree representing a respective topic that is derived from search history data associated with the user identifier, and each node of the profile tree corresponding to at least one of a term or a phrase, and wherein the terms and phrases of the profile tree correspond to the nodes of the profile tree according to the respective topics to which the search terms and phrases belong; for each of the related search queries: identifying, by a computer, in the profile tree one or more nodes that match the related search query; determining, by the computer, the respective levels of the one or more nodes that match the related search query; determining, by the computer, a respective child count for each of the one or more nodes that match the related search query, the child count for each node being proportional to a number of child nodes descending directly from the node and a number of child nodes descending indirectly from the node; and deriving, by the computer, a respective relevance score for the related search query based on the respective levels of the one or more nodes that match the related search query and the respective child counts of the one or more nodes that match the related search query, wherein the relevance score is directly proportional to depths of the respective levels of the one or more nodes that match the related search query, and is inversely proportional to the respective child counts of the one or more nodes that match the related search query; adjusting, by the computer, the rank of at least one of the related search queries in the list based on the respective relevance scores of the related search queries so that the search queries are ranked according to a second order different from the first order; and providing, by the computer, suggested query data to a client device associated with the user identifier, the suggested query data operable to cause the client device to present a plurality of top-ranked related search queries according to the second order. | 1. A computer-implemented method, comprising: receiving a search query, the search query associated with a user identifier; receiving a ranked list of related search queries for the received search query, the related search queries being suggested alternate queries for the search query and ranked according to a first order; accessing a profile tree associated with the user identifier and including a hierarchy of nodes, the hierarchy of nodes including a root node and a plurality of child nodes, each child node descending from the root node or another child node, the profile tree defining a plurality of levels, each level including child nodes that descend from the root node at a same depth, and each node of the profile tree representing a respective topic that is derived from search history data associated with the user identifier, and each node of the profile tree corresponding to at least one of a term or a phrase, and wherein the terms and phrases of the profile tree correspond to the nodes of the profile tree according to the respective topics to which the search terms and phrases belong; for each of the related search queries: identifying, by a computer, in the profile tree one or more nodes that match the related search query; determining, by the computer, the respective levels of the one or more nodes that match the related search query; determining, by the computer, a respective child count for each of the one or more nodes that match the related search query, the child count for each node being proportional to a number of child nodes descending directly from the node and a number of child nodes descending indirectly from the node; and deriving, by the computer, a respective relevance score for the related search query based on the respective levels of the one or more nodes that match the related search query and the respective child counts of the one or more nodes that match the related search query, wherein the relevance score is directly proportional to depths of the respective levels of the one or more nodes that match the related search query, and is inversely proportional to the respective child counts of the one or more nodes that match the related search query; adjusting, by the computer, the rank of at least one of the related search queries in the list based on the respective relevance scores of the related search queries so that the search queries are ranked according to a second order different from the first order; and providing, by the computer, suggested query data to a client device associated with the user identifier, the suggested query data operable to cause the client device to present a plurality of top-ranked related search queries according to the second order. 6. The method of claim 1 , wherein for each of the related search queries, the one or more nodes that match the related search query each represents a respective topical category that includes a concept represented by terms of the related search query. | 0.632653 |
8,495,156 | 12 | 20 | 12. A device comprising a machine-readable medium storing instructions for causing one or more hardware processors to perform operations comprising: determining that a user of a first instant messaging service is associated with a second instant messaging service different from the first instant messaging service; identifying user identities for users other than the user, wherein the user identities are associated with the first instant messaging service and the user identities are stored in a first list associated with the user; determining if at least one of the user identities from the first list of user identities has a matching user identity associated with the second instant messaging service by identifying if a unique portion of the at least one of the user identities from the first list has at least one common feature with a unique portion of a user identity associated with the second instant messaging service; determining a degree of uniqueness of an identified user identity; adjusting the required amount of common features between the unique portion of the identified user identity and the unique portion of a matching user identity associated with the second instant messaging service based on the determined degree of uniqueness of the identified use identity; and modifying a second list associated with the second instant messaging service to include the matching user identities. | 12. A device comprising a machine-readable medium storing instructions for causing one or more hardware processors to perform operations comprising: determining that a user of a first instant messaging service is associated with a second instant messaging service different from the first instant messaging service; identifying user identities for users other than the user, wherein the user identities are associated with the first instant messaging service and the user identities are stored in a first list associated with the user; determining if at least one of the user identities from the first list of user identities has a matching user identity associated with the second instant messaging service by identifying if a unique portion of the at least one of the user identities from the first list has at least one common feature with a unique portion of a user identity associated with the second instant messaging service; determining a degree of uniqueness of an identified user identity; adjusting the required amount of common features between the unique portion of the identified user identity and the unique portion of a matching user identity associated with the second instant messaging service based on the determined degree of uniqueness of the identified use identity; and modifying a second list associated with the second instant messaging service to include the matching user identities. 20. The device of claim 12 , further comprising determining if each identified user identity has a matching user identity associated with the second messaging service. | 0.838178 |
9,690,452 | 9 | 14 | 9. A method of browser functions for accessing and navigating one or more options by a user who is one or more of the following: a novice user, or a user with disabilities, characterized in that the method comprises the following steps; a. initiating a browser session by authenticating the user; b. recognizing an expertise level of the user for the browser session; c. applying a browser plug-in component operable with a browser interface and thereby providing one or more function options selectable by a user for accessing, navigating within, and moving between one or more activity pages to the user, said one or more activity pages including one or more webpages, and generating the one or more function options based upon the expertise level of the user and any disability of the user and thereby presenting said one or more function options in the one or more activity pages that are untransformed to be compatible in format, positioning and availability with the expertise level of the user and a disability accommodation for the user, and said function options including browser functions; and d. the user utilizing the one or more function options to access, and navigate within, and move between the one or more activity pages. | 9. A method of browser functions for accessing and navigating one or more options by a user who is one or more of the following: a novice user, or a user with disabilities, characterized in that the method comprises the following steps; a. initiating a browser session by authenticating the user; b. recognizing an expertise level of the user for the browser session; c. applying a browser plug-in component operable with a browser interface and thereby providing one or more function options selectable by a user for accessing, navigating within, and moving between one or more activity pages to the user, said one or more activity pages including one or more webpages, and generating the one or more function options based upon the expertise level of the user and any disability of the user and thereby presenting said one or more function options in the one or more activity pages that are untransformed to be compatible in format, positioning and availability with the expertise level of the user and a disability accommodation for the user, and said function options including browser functions; and d. the user utilizing the one or more function options to access, and navigate within, and move between the one or more activity pages. 14. The method of claim 9 , characterized in that it comprises the further steps of the user setting one or more user preferences; and the one or more user preferences being stored in one or more data storage means. | 0.890306 |
8,612,205 | 1 | 15 | 1. A method for generating word alignments from pairs of aligned text strings comprising: from a corpus of text strings, receiving a pair of text strings comprising a first text string in a first language and a second text string in a second language; with a first alignment tool, generating a first alignment between the first and second text strings which creates links between the first and second text string, each link linking a single token of the first text string to a single token of the second text string, the tokens of the first and second text strings including words; with a second alignment tool, generating a second alignment between the first and second text strings which creates links between the first and second text strings, each link linking at least one token of the first text string to at least one token of the second text string, and generating a modified first alignment by selectively modifying links in the first alignment which include a word which is infrequent in the corpus, based on links generated in the second alignment, the selective modification of the links comprising identifying links in the first alignment to be retained which include the infrequent word and a linked target word where there is a corresponding link present in the second alignment which includes the infrequent word and the same linked target word and identifying for removal, at least a portion of the links in the first alignment which include the infrequent word and a linked target word for which there is no corresponding link between the infrequent word and the linked target word in the second alignment, wherein the generation of at least one of the first, second, and modified alignments is performed with a computer processor. | 1. A method for generating word alignments from pairs of aligned text strings comprising: from a corpus of text strings, receiving a pair of text strings comprising a first text string in a first language and a second text string in a second language; with a first alignment tool, generating a first alignment between the first and second text strings which creates links between the first and second text string, each link linking a single token of the first text string to a single token of the second text string, the tokens of the first and second text strings including words; with a second alignment tool, generating a second alignment between the first and second text strings which creates links between the first and second text strings, each link linking at least one token of the first text string to at least one token of the second text string, and generating a modified first alignment by selectively modifying links in the first alignment which include a word which is infrequent in the corpus, based on links generated in the second alignment, the selective modification of the links comprising identifying links in the first alignment to be retained which include the infrequent word and a linked target word where there is a corresponding link present in the second alignment which includes the infrequent word and the same linked target word and identifying for removal, at least a portion of the links in the first alignment which include the infrequent word and a linked target word for which there is no corresponding link between the infrequent word and the linked target word in the second alignment, wherein the generation of at least one of the first, second, and modified alignments is performed with a computer processor. 15. The method of claim 1 , further comprising extracting bi-phrases based on the links in the modified alignment. | 0.866197 |
9,135,583 | 1 | 2 | 1. A non-transitory computer-readable medium having stored thereon processor-executable instructions that when executed by a processor result in the following: receiving, at a query technique engine, continuous query definition parameters from a user via a graphical user interface; retrieving, at the query technique engine, semantic layer information associated with an event processing engine, the event processing engine being adapted to receive an event stream; based on the continuous query definition parameters from the user, automatically creating at the query technique engine a pre-fetch query to pre-fetch historical data from a database using a pull-model; automatically creating an event processing language statement, by a processor at the query technique engine, the event processing language statement being created based on (i) the continuous query definition parameters from the user and (ii) the semantic layer information; and providing the event processing language statement to the complex event processing engine so as to establish a continuous query, the continuous query providing at least one output data value based on both the pre-fetched historical data and new push-model events in the event stream. | 1. A non-transitory computer-readable medium having stored thereon processor-executable instructions that when executed by a processor result in the following: receiving, at a query technique engine, continuous query definition parameters from a user via a graphical user interface; retrieving, at the query technique engine, semantic layer information associated with an event processing engine, the event processing engine being adapted to receive an event stream; based on the continuous query definition parameters from the user, automatically creating at the query technique engine a pre-fetch query to pre-fetch historical data from a database using a pull-model; automatically creating an event processing language statement, by a processor at the query technique engine, the event processing language statement being created based on (i) the continuous query definition parameters from the user and (ii) the semantic layer information; and providing the event processing language statement to the complex event processing engine so as to establish a continuous query, the continuous query providing at least one output data value based on both the pre-fetched historical data and new push-model events in the event stream. 2. The computer-readable medium of claim 1 , wherein execution of the instructions further results in: joining the historical data with information associated with events in the event stream. | 0.851246 |
8,677,262 | 4 | 17 | 4. A method, comprising: rendering, by a system including a processor, a visualization comprising a first display object and a second display object, wherein the first display object and the second display object are related to at least one hardware device; obtaining, by the system, context information for a user identity of an industrial automation system comprising a state of the at least one hardware device within an industrial automation system; determining, by the system, a first level of relevance of the first display object to the user identity based on the context information; determining, by the system, a second level of relevance of the second display object to the user identity based on the context information, wherein the first level of relevance is determined to be higher than the second level of relevance; initiating, by the system, an update of the visualization to render the first display object more prominently than the second display object based on the first level of relevance being higher than the second level of relevance. | 4. A method, comprising: rendering, by a system including a processor, a visualization comprising a first display object and a second display object, wherein the first display object and the second display object are related to at least one hardware device; obtaining, by the system, context information for a user identity of an industrial automation system comprising a state of the at least one hardware device within an industrial automation system; determining, by the system, a first level of relevance of the first display object to the user identity based on the context information; determining, by the system, a second level of relevance of the second display object to the user identity based on the context information, wherein the first level of relevance is determined to be higher than the second level of relevance; initiating, by the system, an update of the visualization to render the first display object more prominently than the second display object based on the first level of relevance being higher than the second level of relevance. 17. The method of claim 4 , wherein the context information is further based on a logical location of the device or a physical location of the at least one device. | 0.879971 |
8,832,615 | 1 | 2 | 1. A method for detecting anomalies in signal behaviors in a simulation of a low power integrated circuit (IC), the method comprising: receiving, at an computing device, a circuit design and a power specification comprising a power domain including a portion of the circuit design; determining, with one or more processing units associated with the computing device, at least one power sequence rule based on the power specification; simulating the circuit design and the power specification to obtain one or more simulation results; identifying, with the one or more processing units associated with the computing device, at least one anomaly of the at least one power sequence rule based on the one or more simulation results; and generating, with the one or more processing units, information relevant to the identified anomaly of the at least one power sequence rule. | 1. A method for detecting anomalies in signal behaviors in a simulation of a low power integrated circuit (IC), the method comprising: receiving, at an computing device, a circuit design and a power specification comprising a power domain including a portion of the circuit design; determining, with one or more processing units associated with the computing device, at least one power sequence rule based on the power specification; simulating the circuit design and the power specification to obtain one or more simulation results; identifying, with the one or more processing units associated with the computing device, at least one anomaly of the at least one power sequence rule based on the one or more simulation results; and generating, with the one or more processing units, information relevant to the identified anomaly of the at least one power sequence rule. 2. The method according to claim 1 , further comprising: setting up a context in a debugger for debugging the anomaly. | 0.796552 |
8,947,322 | 18 | 19 | 18. A non-transitory computer-readable medium having stored thereon instructions executable by a computing device to cause the computing device to perform functions comprising: receiving, by the head-mountable computing device, input data that is indicative of head position; providing, by the head-mountable computing device, a user-interface that comprises a content region containing a set of selectable content objects that are horizontally arranged, wherein the content region is located at a fixed height within the user-interface; defining, by the head-mountable computing device, a view region that is movable within the user-interface, wherein the view region is smaller than the user-interface; associating, by the head-mountable computing device, a forward-looking head position with a first location of the view region within the user-interface, wherein the fixed height of the content region is such that at least a portion of the content region is located above the view region, when the view region is at the first location associated with forward-looking head position; based on head movement data, moving the view region within the user-interface, wherein the head movement data is determined based on the input data that is indicative of head position; as the view region moves within the user-interface, displaying a portion of the user-interface corresponding to the view region in the see-through display; determining a first user-context associated with the head-mountable device; and dynamically changing the set of selectable content objects contained in the content region based on the determined first user-context. | 18. A non-transitory computer-readable medium having stored thereon instructions executable by a computing device to cause the computing device to perform functions comprising: receiving, by the head-mountable computing device, input data that is indicative of head position; providing, by the head-mountable computing device, a user-interface that comprises a content region containing a set of selectable content objects that are horizontally arranged, wherein the content region is located at a fixed height within the user-interface; defining, by the head-mountable computing device, a view region that is movable within the user-interface, wherein the view region is smaller than the user-interface; associating, by the head-mountable computing device, a forward-looking head position with a first location of the view region within the user-interface, wherein the fixed height of the content region is such that at least a portion of the content region is located above the view region, when the view region is at the first location associated with forward-looking head position; based on head movement data, moving the view region within the user-interface, wherein the head movement data is determined based on the input data that is indicative of head position; as the view region moves within the user-interface, displaying a portion of the user-interface corresponding to the view region in the see-through display; determining a first user-context associated with the head-mountable device; and dynamically changing the set of selectable content objects contained in the content region based on the determined first user-context. 19. The non-transitory computer-readable medium of claim 18 , wherein determining the user-context associated with the wearable computing device comprises: transmitting one or more user-context signals to a remote server system, wherein the one or more user-context signals are associated with the head-mountable device; and receiving from the remote server system an indication of a user-context associated with the head-mountable device. | 0.918401 |
8,824,785 | 12 | 13 | 12. The method of claim 5 further comprising: determining a region of the document where the handwritten information is located; determining a portion of the typographic information corresponding to the region of the document; identifying a data field associated with the portion of the typographic information based on the document type corresponding to the typographic information; and storing the plurality of characters corresponding to the handwritten information located in the region in a record of a database corresponding to the data field. | 12. The method of claim 5 further comprising: determining a region of the document where the handwritten information is located; determining a portion of the typographic information corresponding to the region of the document; identifying a data field associated with the portion of the typographic information based on the document type corresponding to the typographic information; and storing the plurality of characters corresponding to the handwritten information located in the region in a record of a database corresponding to the data field. 13. The method of claim 12 wherein determining the region of the document corresponding to the handwritten information further comprises: (a) determining a top edge, a bottom edge, a right edge and a left edge of the document, wherein the edges are determined based on an orientation of the typographic information relative to the document; (b) identifying an upper-left most pixel of the plurality of pixels of the handwritten information, wherein the upper-left most pixel comprises a pixel located closest to the top edge of the document and closest to the left edge of the document; (c) determining a top bound of the region of the document based on a top line running through the upper-left most pixel, wherein the top line is parallel to the top edge of the document and the bottom edge of the document; (d) determining a bottom pixel of the plurality of pixels of the handwritten information wherein the bottom pixel is located a height number of pixels below the upper-left most pixel; (e) determining a bottom bound of the region of the document based on a bottom line running through the bottom pixel, wherein the bottom line is parallel to the top edge of the document and the bottom edge of the document; (f) determining a leftmost pixel of the plurality of pixels of the handwritten information located within the top bound and the bottom bound, wherein the leftmost pixel is located at the left edge of the document, or the leftmost pixel comprises a closet pixel to the left edge of the document which is located within the top bound and the bottom bound and has no other pixels within a buffer number of pixels to the left; (g) determining a left bound of the region of the document based on a left line running through the leftmost pixel, wherein the left line is parallel to the left edge of the document and the right edge of the document; (h) determining a rightmost pixel of the plurality of pixels of the handwritten information located within the top bound and the bottom bound, wherein the rightmost pixel is located at the right edge of the document or the rightmost pixel comprises a closest pixel to the right edge of the document which is located within the top bound and the bottom bound and has no other pixels with the buffer number of pixels to the right; (i) determining a right bound of the region of the document based on a right line running through the rightmost pixel, wherein the right line is parallel to the left edge of the document and the right edge of the document; (j) determining the bottom pixel of the plurality of pixels of the handwritten information which is located within the left bound and the right bound, wherein the bottom pixel is located at the bottom edge of the document or the bottom pixel is a closest pixel to the bottom edge of the document with no other pixels within the buffer number of pixels below; (k) determining the bottom bound of the region of the document based on a bottom line running through the bottom pixel, wherein the bottom line is parallel to the top edge of the document and the bottom edge of the document; (l) repeating steps (d)-(k) using the bottom bound determined in step (k), the left bound determined in step (g), and the right bound determined in step (i) until the bottom bound, left bound and right bound remain constant; and (m) determining the region of the document based on an area enclosed by the top bound, the bottom bound, the left bound and the right bound. | 0.500144 |
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