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8,448,242 | 59 | 62 | 59. A system for outputting data based on anomaly detection, comprising: a digital processing device that: receives an input dataset; uses a binary anomaly detection model to determine whether an input dataset is likely to contain an anomaly, wherein the binary anomaly detection model is used to determine whether the input dataset is likely to contain an anomaly by checking the binary anomaly detection model for an n-gram in the input dataset; if the input dataset is determined to be likely to contain an anomaly, drops the input dataset; and if the input dataset is determined to be unlikely to contain an anomaly, outputs the input dataset based on whether the input dataset contains an anomaly. | 59. A system for outputting data based on anomaly detection, comprising: a digital processing device that: receives an input dataset; uses a binary anomaly detection model to determine whether an input dataset is likely to contain an anomaly, wherein the binary anomaly detection model is used to determine whether the input dataset is likely to contain an anomaly by checking the binary anomaly detection model for an n-gram in the input dataset; if the input dataset is determined to be likely to contain an anomaly, drops the input dataset; and if the input dataset is determined to be unlikely to contain an anomaly, outputs the input dataset based on whether the input dataset contains an anomaly. 62. The system of claim 59 , wherein the digital processing device also generates a content anomaly signature using the input dataset. | 0.850446 |
8,009,913 | 1 | 10 | 1. A method of capturing human-readable text displayed on a unit dose package, said method comprising: capturing identification information associated with a unit dose package; determining, based at least in part on the identification information, a location on the unit dose package at which human-readable text is displayed; and electronically capturing the human-readable text at the determined location, wherein determining, based at least in part on the identification information, a location on the unit dose package at which human-readable text is displayed comprises accessing a mapping of the identification information associated with one or more unit dose packages to information describing the location on the corresponding unit dose package at which the human-readable text is displayed. | 1. A method of capturing human-readable text displayed on a unit dose package, said method comprising: capturing identification information associated with a unit dose package; determining, based at least in part on the identification information, a location on the unit dose package at which human-readable text is displayed; and electronically capturing the human-readable text at the determined location, wherein determining, based at least in part on the identification information, a location on the unit dose package at which human-readable text is displayed comprises accessing a mapping of the identification information associated with one or more unit dose packages to information describing the location on the corresponding unit dose package at which the human-readable text is displayed. 10. The method of claim 1 , wherein the identification information is selected from a group consisting of a medication type and a manufacturer associated with the unit dose package. | 0.895977 |
9,298,695 | 11 | 15 | 11. A non-transitory machine-readable storage device comprising computer instructions which, responsive to being executed by a processor, cause the processor to perform operations comprising: detecting in a corrected text message an auto-correction of a target word of a group of words, the target word having a type; detecting an input command at a first communication device requesting a transmission of the corrected text message to a second communication device; responsive to the detecting of the input command, generating a correction alert for presentation at the first communication device indicating the target word that has been auto-corrected; and in response to the presenting of the correction alert, generating an option for presentation at the first communication device for modifying the target word, wherein the transmission of the corrected text message to the second communication device is limited to after the option for modifying is presented at the first communication device, and wherein a type of the correction alert corresponds to the type of the target word. | 11. A non-transitory machine-readable storage device comprising computer instructions which, responsive to being executed by a processor, cause the processor to perform operations comprising: detecting in a corrected text message an auto-correction of a target word of a group of words, the target word having a type; detecting an input command at a first communication device requesting a transmission of the corrected text message to a second communication device; responsive to the detecting of the input command, generating a correction alert for presentation at the first communication device indicating the target word that has been auto-corrected; and in response to the presenting of the correction alert, generating an option for presentation at the first communication device for modifying the target word, wherein the transmission of the corrected text message to the second communication device is limited to after the option for modifying is presented at the first communication device, and wherein a type of the correction alert corresponds to the type of the target word. 15. The non-transitory machine-readable storage device of claim 11 , wherein the processor includes a group of processors, wherein a first processor of the group of processors is located at the first communication device, and wherein a second processor of the group of processors is located remotely from the first communication device. | 0.793103 |
8,045,054 | 1 | 2 | 1. A method comprising: receiving a video signal for a video program with closed captioning from a video source, wherein the video signal is concurrently delivered to customer equipment by the video source; extracting original closed captioning text from the video signal; translating the original closed captioning text of a first language to translated closed captioning text of a second language; and sending closed captioning information comprising the translated closed captioning text toward the customer equipment. | 1. A method comprising: receiving a video signal for a video program with closed captioning from a video source, wherein the video signal is concurrently delivered to customer equipment by the video source; extracting original closed captioning text from the video signal; translating the original closed captioning text of a first language to translated closed captioning text of a second language; and sending closed captioning information comprising the translated closed captioning text toward the customer equipment. 2. The method of claim 1 wherein the customer equipment receives the video signal provided by the video source and displays the translated closed captioning text from the closed captioning information along with the video program. | 0.716049 |
7,689,562 | 1 | 6 | 1. An access control system providing access control to at least one information resource associated with at least one application within a computer network, the access control system comprising: a plurality of context sources being relevant to the at least one application and providing context information; a constraint specification console providing an interface to specify application specific constraints based on the context sources; a rule engine capable of handling facts and applying inference rules on those facts, the rule engine storing the rules; an application specific constraint enforcement point configured to receive access requests, hence querying facts and further being responsible to make access decisions regarding the information resource based on those facts and on application specific constraints; and a rule engine adaptor acting as connecting component to interconnect the rule engine with the context sources, the constraint specification console and the enforcement point, respectively, and as intermediary in communication of the rule engine with the context sources, the constraint specification console and the enforcement point, respectively, so as to allow access control to the at least one information resource based on specified application specific constraints with regard to context information originating from the context sources, context information being provided by a context source being expressed as the facts within the rule engine. | 1. An access control system providing access control to at least one information resource associated with at least one application within a computer network, the access control system comprising: a plurality of context sources being relevant to the at least one application and providing context information; a constraint specification console providing an interface to specify application specific constraints based on the context sources; a rule engine capable of handling facts and applying inference rules on those facts, the rule engine storing the rules; an application specific constraint enforcement point configured to receive access requests, hence querying facts and further being responsible to make access decisions regarding the information resource based on those facts and on application specific constraints; and a rule engine adaptor acting as connecting component to interconnect the rule engine with the context sources, the constraint specification console and the enforcement point, respectively, and as intermediary in communication of the rule engine with the context sources, the constraint specification console and the enforcement point, respectively, so as to allow access control to the at least one information resource based on specified application specific constraints with regard to context information originating from the context sources, context information being provided by a context source being expressed as the facts within the rule engine. 6. An access control system according to claim 1 , wherein the rule engine adaptor comprises: a. a context converter component providing an access to the rule engine adaptor for the various context sources, and acting as intermediary in communication of the rule engine with any one of the context sources, b. a constraint converter component acting as intermediary in communication of the rule engine with the constraint specification console so as to enable the rule engine to enforce respective specified constraints when the context sources update their respective context information, and c. a query converter component providing an access to the rule engine adaptor for the constraint enforcement point to query about certain types of facts and acting as intermediary in communication of the rule engine with the enforcement point. | 0.500597 |
8,972,259 | 14 | 15 | 14. A non-transitory processor-readable medium storing code representing instructions to be executed by a processor, the code comprising code to cause the processor to: encode a first prosodic speech signal to generate a musically encoded first prosodic speech signal by mapping each syllable of the first prosodic speech signal to a musical note; store the musically encoded first prosodic speech signal; audibly play the musically encoded first prosodic speech signal to a language student; prompt the student to recite the speech segment from which the musically encoded first prosodic speech signal originated; record an utterance from the language student in response to the prompt; delexicalize the utterance to generate a second prosodic speech signal; and calculate at least one error signal based on a difference between: a difference between a duration of a first syllable in the first prosodic speech signal and a duration of a second syllable in the first prosodic speech signal, the first syllable in the first prosodic speech signal being non-adjacent to the second syllable in the first prosodic speech signal; and a difference between a duration of a first syllable in the second prosodic speech signal and a duration of a second syllable in the second prosodic speech signal, the first syllable in the second prosodic speech signal being non-adjacent to the second syllable in the second prosodic speech signal. | 14. A non-transitory processor-readable medium storing code representing instructions to be executed by a processor, the code comprising code to cause the processor to: encode a first prosodic speech signal to generate a musically encoded first prosodic speech signal by mapping each syllable of the first prosodic speech signal to a musical note; store the musically encoded first prosodic speech signal; audibly play the musically encoded first prosodic speech signal to a language student; prompt the student to recite the speech segment from which the musically encoded first prosodic speech signal originated; record an utterance from the language student in response to the prompt; delexicalize the utterance to generate a second prosodic speech signal; and calculate at least one error signal based on a difference between: a difference between a duration of a first syllable in the first prosodic speech signal and a duration of a second syllable in the first prosodic speech signal, the first syllable in the first prosodic speech signal being non-adjacent to the second syllable in the first prosodic speech signal; and a difference between a duration of a first syllable in the second prosodic speech signal and a duration of a second syllable in the second prosodic speech signal, the first syllable in the second prosodic speech signal being non-adjacent to the second syllable in the second prosodic speech signal. 15. The non-transitory processor-readable medium of claim 14 , further comprising code to cause the processor to: determine prosodic characteristics of the first prosodic speech signal and the second prosodic speech signal, the code to cause the processor to calculate includes code to cause the processor to calculate the at least one error signal based on the prosodic characteristics of the first prosodic speech signal and the second prosodic speech signal. | 0.794929 |
7,725,499 | 17 | 21 | 17. The method of claim 14 further comprising the steps of: assigning attributes to information units based on criteria selected to measure information quality; and assigning status identifiers to information units to identify workflow status and history with respect to content and review status of data. | 17. The method of claim 14 further comprising the steps of: assigning attributes to information units based on criteria selected to measure information quality; and assigning status identifiers to information units to identify workflow status and history with respect to content and review status of data. 21. The method of claim 17 wherein manual entry, update status, conversion status, importation process, translation, or other source factors are included in the criteria for quality measurement. | 0.973925 |
8,655,658 | 13 | 15 | 13. A computer-readable storage device having instructions stored which, when executed by a computing device, cause the computing device to perform operations comprising: receiving unconstrained input speech from a user; converting, via a processor, only the unconstrained input speech corresponding to single digits into a string of words, wherein each word in the string of words is modeled using a three segment structure comprising a plurality of heads and a plurality of tails; converting the string of words into a sequence of digits using classes of rules and according to an acoustic model database in which Markov models characterize acoustic features of numeric words; comparing the sequence of digits to a plurality of valid sequences of digits, to yield validity information; and providing the validity information to a device associated with the user. | 13. A computer-readable storage device having instructions stored which, when executed by a computing device, cause the computing device to perform operations comprising: receiving unconstrained input speech from a user; converting, via a processor, only the unconstrained input speech corresponding to single digits into a string of words, wherein each word in the string of words is modeled using a three segment structure comprising a plurality of heads and a plurality of tails; converting the string of words into a sequence of digits using classes of rules and according to an acoustic model database in which Markov models characterize acoustic features of numeric words; comparing the sequence of digits to a plurality of valid sequences of digits, to yield validity information; and providing the validity information to a device associated with the user. 15. The computer-readable storage device of claim 13 , wherein the three segment structure further comprises a body. | 0.824242 |
8,862,255 | 23 | 24 | 23. The system of claim 19 , wherein modifying an attribute associated with the output of the generated audio content based at least in part on the determined length of the segment of textual content includes modifying the attribute if the determined length satisfies a threshold value. | 23. The system of claim 19 , wherein modifying an attribute associated with the output of the generated audio content based at least in part on the determined length of the segment of textual content includes modifying the attribute if the determined length satisfies a threshold value. 24. The system of claim 23 , wherein the threshold value is indicated by at least one of a visual cue, an auditory cue, or a tactile cue. | 0.966022 |
8,209,314 | 2 | 3 | 2. The method of claim 1 , further comprising displaying the summary result and the another summary result in the SCCB format, wherein the one or more search criterion includes at least any one of one or more category words or phrase each delimited by a set of category delimiters and one or more keywords or phrase each delimited by a set of keyword delimiters. | 2. The method of claim 1 , further comprising displaying the summary result and the another summary result in the SCCB format, wherein the one or more search criterion includes at least any one of one or more category words or phrase each delimited by a set of category delimiters and one or more keywords or phrase each delimited by a set of keyword delimiters. 3. The method of claim 2 , wherein the set of category delimiters is a set of brackets. | 0.969149 |
8,788,603 | 1 | 3 | 1. A method for displaying a plurality of chat threads on a wireless mobile terminal comprising a user input device and a display, the method comprising: receiving a plurality of inbound chat messages corresponding to the plurality of chat threads, each of the plurality of inbound chat messages comprising message content; transmitting a plurality of outbound chat messages corresponding to the plurality of chat threads, each of the plurality of outbound chat messages comprising message content and being transmitted to at least one user-selected recipient to thereby become a sent chat message; simultaneously displaying the message content of each of the pluralities of inbound and sent chat messages corresponding to the plurality of chat threads in a single content region of a chat history display; receiving at the user input device a user selection of the displayed message content of a user-selected displayed chat message corresponding to one of the displayed pluralities of inbound and sent chat messages; and in response to the user selection of the displayed message content of the user-selected displayed chat message: visually highlighting in the single content region of the chat history display the displayed message content of each of the displayed inbound and sent chat messages associated with a particular chat thread corresponding to the user-selected displayed chat message; and displaying in a region of the chat history display distinct from the single content region a private nickname of a sender of the user-selected displayed chat message when a buddy identifier of the sender is in the nickname records of a recipient of the one or more recipients of the user-selected displayed chat message, otherwise displaying in the region of the chat history display distinct from the single content region the public nickname of the sender, wherein the region of the chat history display distinct from the single content region is a title bar region of the chat history display. | 1. A method for displaying a plurality of chat threads on a wireless mobile terminal comprising a user input device and a display, the method comprising: receiving a plurality of inbound chat messages corresponding to the plurality of chat threads, each of the plurality of inbound chat messages comprising message content; transmitting a plurality of outbound chat messages corresponding to the plurality of chat threads, each of the plurality of outbound chat messages comprising message content and being transmitted to at least one user-selected recipient to thereby become a sent chat message; simultaneously displaying the message content of each of the pluralities of inbound and sent chat messages corresponding to the plurality of chat threads in a single content region of a chat history display; receiving at the user input device a user selection of the displayed message content of a user-selected displayed chat message corresponding to one of the displayed pluralities of inbound and sent chat messages; and in response to the user selection of the displayed message content of the user-selected displayed chat message: visually highlighting in the single content region of the chat history display the displayed message content of each of the displayed inbound and sent chat messages associated with a particular chat thread corresponding to the user-selected displayed chat message; and displaying in a region of the chat history display distinct from the single content region a private nickname of a sender of the user-selected displayed chat message when a buddy identifier of the sender is in the nickname records of a recipient of the one or more recipients of the user-selected displayed chat message, otherwise displaying in the region of the chat history display distinct from the single content region the public nickname of the sender, wherein the region of the chat history display distinct from the single content region is a title bar region of the chat history display. 3. The method of claim 1 , further comprising if the buddy identifier of the sender is not found in the recipients nickname records displaying in the region of the chat history display distinct from the single content region the public shortname of the sender. | 0.81241 |
7,711,573 | 1 | 4 | 1. A system for using a computer to improve a precision ratio when searching a resume database, comprising: a memory device resident in the computer; and a processor disposed in communication with the memory device, the processor configured to: receive a resume; parse the resume to: locate at least one skill or experience-related phrase in the resume; determine an experience range for each said at least one skill or experience-related phrase by examining a use of each said at least one skill or experience-related phrase in the resume; and compute a term of experience for each said at least one skill or experience-related phrase based on the experience range, wherein the term of experience for each said at least one skill or experience-related phrase is a summation of the term of experience for each occurrence of the phrase associated with a different experience range; store the resume in the resume database; create a parsed resume based on the resume, the parsed resume including each said at least one skill or experience-related phrase located in the resume, the term of experience computed for each said at least one skill or experience-related phrase, and a relationship between the term of experience and each said at least one skill or experience-related phrase; store the parsed resume in the resume database; send a database query to the resume database, the database query including a job description that includes at least one job requirement, each said at least one job requirement including a required skill or experience-related phrase, and a required term of experience; and receive a result set in response to the database query, wherein the result set includes the resume when the parsed resume satisfies the job description. | 1. A system for using a computer to improve a precision ratio when searching a resume database, comprising: a memory device resident in the computer; and a processor disposed in communication with the memory device, the processor configured to: receive a resume; parse the resume to: locate at least one skill or experience-related phrase in the resume; determine an experience range for each said at least one skill or experience-related phrase by examining a use of each said at least one skill or experience-related phrase in the resume; and compute a term of experience for each said at least one skill or experience-related phrase based on the experience range, wherein the term of experience for each said at least one skill or experience-related phrase is a summation of the term of experience for each occurrence of the phrase associated with a different experience range; store the resume in the resume database; create a parsed resume based on the resume, the parsed resume including each said at least one skill or experience-related phrase located in the resume, the term of experience computed for each said at least one skill or experience-related phrase, and a relationship between the term of experience and each said at least one skill or experience-related phrase; store the parsed resume in the resume database; send a database query to the resume database, the database query including a job description that includes at least one job requirement, each said at least one job requirement including a required skill or experience-related phrase, and a required term of experience; and receive a result set in response to the database query, wherein the result set includes the resume when the parsed resume satisfies the job description. 4. The system of claim 1 , wherein the resume comprises a curriculum vitae. | 0.965149 |
9,754,210 | 1 | 6 | 1. A computer-implemented method for determining user interests, comprising: receiving user activity data that describes an interaction between a user and digital content; generating first interest-level data that represents a first level of interest between said user and a first entity and a second entity that are a topic of said digital content, wherein said first entity and said second entity are included within a knowledge base comprising a plurality of entities, said knowledge base comprising an ontology comprising a knowledge graph that indicates relationships between said plurality of entities; identifying a candidate entity based on said candidate entity having a relationship to both the first entity and the second entity within the knowledge base; generating second interest-level data that represents a second level of interest between said user and said candidate entity based on an analysis of said relationships between the candidate entry and the first entity and the second entity; linking a user ID associated with the user to the candidate entity, thereby indicating the user is interested in the candidate entity; receiving a search query from the user; and generating a search result comprising digital contents, in response to the received search query, wherein the digital contents are ranked using the generated first interest-level data and the generated second interest-level data. | 1. A computer-implemented method for determining user interests, comprising: receiving user activity data that describes an interaction between a user and digital content; generating first interest-level data that represents a first level of interest between said user and a first entity and a second entity that are a topic of said digital content, wherein said first entity and said second entity are included within a knowledge base comprising a plurality of entities, said knowledge base comprising an ontology comprising a knowledge graph that indicates relationships between said plurality of entities; identifying a candidate entity based on said candidate entity having a relationship to both the first entity and the second entity within the knowledge base; generating second interest-level data that represents a second level of interest between said user and said candidate entity based on an analysis of said relationships between the candidate entry and the first entity and the second entity; linking a user ID associated with the user to the candidate entity, thereby indicating the user is interested in the candidate entity; receiving a search query from the user; and generating a search result comprising digital contents, in response to the received search query, wherein the digital contents are ranked using the generated first interest-level data and the generated second interest-level data. 6. The computer-implemented method of claim 1 , further comprising updating said first level of interest between said user and said first entity or second entity that is said topic of said digital content based on a recency of said first entity or second entity being a topic of digital content. | 0.824405 |
8,838,512 | 14 | 18 | 14. One or more computer-readable storage media storing computer-executable instructions that, when executed on one or more processors, cause the one or more processors to perform operations comprising: receiving an input query transmitted over a network; determining an input query pattern associated with the input query; accessing a library comprising one or more query patterns associated with one or more predefined search task categories by one or more transition probabilities, each of the one or more predefined search task categories associated with a task to be accomplished; classifying the input query pattern into one or more search tasks according to the one or more predefined search task categories, the classifying based at least in part on a lookup of the input query pattern in the library and identifying one or more tasks to be accomplished associated with the one or more predefined search task categories associated with a query pattern of the one or more query patterns similar to the input query pattern; and determining a search result based at least in part on the classified one or more search tasks, the search result based at least in part on the identified one or more tasks to be accomplished. | 14. One or more computer-readable storage media storing computer-executable instructions that, when executed on one or more processors, cause the one or more processors to perform operations comprising: receiving an input query transmitted over a network; determining an input query pattern associated with the input query; accessing a library comprising one or more query patterns associated with one or more predefined search task categories by one or more transition probabilities, each of the one or more predefined search task categories associated with a task to be accomplished; classifying the input query pattern into one or more search tasks according to the one or more predefined search task categories, the classifying based at least in part on a lookup of the input query pattern in the library and identifying one or more tasks to be accomplished associated with the one or more predefined search task categories associated with a query pattern of the one or more query patterns similar to the input query pattern; and determining a search result based at least in part on the classified one or more search tasks, the search result based at least in part on the identified one or more tasks to be accomplished. 18. The one or more computer-readable storage media of claim 14 , wherein the input query is a search query transmitted over a network. | 0.871917 |
7,761,843 | 29 | 46 | 29. A tangible computer-readable medium having computer-executable instructions for implementing a method of creating computer code for a target programming language, the computer executable instructions comprising instructions for: defining a programming command as a predefined command sentence comprising at least one constant word and at least one enterable word, wherein the predefined command sentence comprises a structure other than a syntax of the target programming language and wherein the computer executable instructions for defining the programming command comprise instructions for: inserting a word into the programming command; deleting a word from the programming command; modifying a definition of a word of the programming command; and writing a translation procedure for the programming command; receiving data relating to an input value for the enterable word; and converting the predefined command sentence and the input value for the enterable word into a completed programming command. | 29. A tangible computer-readable medium having computer-executable instructions for implementing a method of creating computer code for a target programming language, the computer executable instructions comprising instructions for: defining a programming command as a predefined command sentence comprising at least one constant word and at least one enterable word, wherein the predefined command sentence comprises a structure other than a syntax of the target programming language and wherein the computer executable instructions for defining the programming command comprise instructions for: inserting a word into the programming command; deleting a word from the programming command; modifying a definition of a word of the programming command; and writing a translation procedure for the programming command; receiving data relating to an input value for the enterable word; and converting the predefined command sentence and the input value for the enterable word into a completed programming command. 46. The tangible computer-readable medium having computer-executable instructions of claim 29 wherein the enterable word comprises a variable word, and wherein the input value comprises an alphanumeric value. | 0.869511 |
7,609,848 | 12 | 14 | 12. A method for creating a watermark in a target digital document at different stages of the target document life cycle to be carried out by a computer adapted to perform the steps of: scanning the target document containing a target watermark; recovering the target watermark from the target document, the recovered target watermark indicating a template specification that describes how the target watermark was merged into the target document; generating an additional watermark according to a watermark specification; updating the template specification by adding the watermark specification and associating the additional watermark with the watermark specification in the template specification; and merging the generated additional watermark with the scanned target document. | 12. A method for creating a watermark in a target digital document at different stages of the target document life cycle to be carried out by a computer adapted to perform the steps of: scanning the target document containing a target watermark; recovering the target watermark from the target document, the recovered target watermark indicating a template specification that describes how the target watermark was merged into the target document; generating an additional watermark according to a watermark specification; updating the template specification by adding the watermark specification and associating the additional watermark with the watermark specification in the template specification; and merging the generated additional watermark with the scanned target document. 14. The method of claim 12 , wherein the additional watermark includes dynamic information pertaining to different stages of the life cycle of the target document. | 0.863712 |
8,024,338 | 2 | 3 | 2. The method of claim 1 , wherein the step of displaying a portion of the hierarchical subject guide includes displaying a user-selectable topic level indicator as well as a plurality of user-selectable sub-topic level indicators. | 2. The method of claim 1 , wherein the step of displaying a portion of the hierarchical subject guide includes displaying a user-selectable topic level indicator as well as a plurality of user-selectable sub-topic level indicators. 3. The method of claim 2 , further comprising displaying a first set of document identifiers associated with a first sub-topic level indicator when a user selects the first sub-topic indicator and displaying a second set of document identifiers associated with a second sub-topic level indicator when a user selects the second sub-topic level indicator. | 0.919443 |
8,898,137 | 1 | 5 | 1. A computer-implemented URL rescue process, said URL rescue process comprising: receiving, by a hosting site, a URL (Uniform Resource Locator) request from a user device, said URL request specifying a URL, the URL request including an address of the hosting site and a character string referencing a content location on the hosting web site; detecting that the URL is invalid by detecting that the content location is not found on the hosting site; in response to detecting that the URL is invalid, attempting, as a first URL rescue strategy, to repair the invalid URL through URL modification; determining that the attempt to repair the invalid URL through URL modification has failed to produce a modified URL that resolves to a valid content location on the hosting site; and executing an alternate URL rescue strategy that does not attempt to repair the invalid URL, said alternate URL rescue strategy comprising: extracting from the invalid URL a set of one or more search terms for executing one or more searches, wherein extracting the set of one or more search terms comprises using delimiters in the invalid URL to identify a substring of the invalid URL, and comparing the substring to entries in a selection list to determine whether to use the substring as a search term, said extracting resulting in a selection of less than all text of said character string for use in executing the one or more searches; executing one or more searches of content of the hosting site using the one or more extracted search terms, wherein executing the one or more searches comprises applying the one or more search terms to an index of the content of the hosting site; incorporating results of the one or more searches into a page; and returning the page to the user device in response to the URL request; said URL rescue process implemented by a server system that comprises one or more computing devices. | 1. A computer-implemented URL rescue process, said URL rescue process comprising: receiving, by a hosting site, a URL (Uniform Resource Locator) request from a user device, said URL request specifying a URL, the URL request including an address of the hosting site and a character string referencing a content location on the hosting web site; detecting that the URL is invalid by detecting that the content location is not found on the hosting site; in response to detecting that the URL is invalid, attempting, as a first URL rescue strategy, to repair the invalid URL through URL modification; determining that the attempt to repair the invalid URL through URL modification has failed to produce a modified URL that resolves to a valid content location on the hosting site; and executing an alternate URL rescue strategy that does not attempt to repair the invalid URL, said alternate URL rescue strategy comprising: extracting from the invalid URL a set of one or more search terms for executing one or more searches, wherein extracting the set of one or more search terms comprises using delimiters in the invalid URL to identify a substring of the invalid URL, and comparing the substring to entries in a selection list to determine whether to use the substring as a search term, said extracting resulting in a selection of less than all text of said character string for use in executing the one or more searches; executing one or more searches of content of the hosting site using the one or more extracted search terms, wherein executing the one or more searches comprises applying the one or more search terms to an index of the content of the hosting site; incorporating results of the one or more searches into a page; and returning the page to the user device in response to the URL request; said URL rescue process implemented by a server system that comprises one or more computing devices. 5. The URL rescue process of claim 1 , wherein the selection list includes a blacklist. | 0.922182 |
9,489,446 | 1 | 3 | 1. A computer-implemented method for generating a training set for use during document review, comprising: assigning classification codes to a set of documents; receiving further classification codes assigned to the same set of documents; comparing the classification code for at least one document with the further classification code for that document; determining whether a disagreement exists between the assigned classification code and the further classification code for at least one document; identifying those documents with disagreeing classification codes as training set candidates; applying a stop threshold to the training set candidates, wherein the stop threshold comprises one of a percentage of disagreement, a number of documents with disagreeing classifications, and a zero-defect test; and designating the training set candidates as a training set when the stop threshold is satisfied. | 1. A computer-implemented method for generating a training set for use during document review, comprising: assigning classification codes to a set of documents; receiving further classification codes assigned to the same set of documents; comparing the classification code for at least one document with the further classification code for that document; determining whether a disagreement exists between the assigned classification code and the further classification code for at least one document; identifying those documents with disagreeing classification codes as training set candidates; applying a stop threshold to the training set candidates, wherein the stop threshold comprises one of a percentage of disagreement, a number of documents with disagreeing classifications, and a zero-defect test; and designating the training set candidates as a training set when the stop threshold is satisfied. 3. A method according to claim 1 , wherein the classification codes are assigned by a machine and the further classification codes are assigned by a reviewer. | 0.818807 |
9,203,881 | 8 | 10 | 8. A method for converting entity mentions in a feed item to online social environments specific identifiers for cross posting on heterogeneous online social environments, the method including: receiving a feed item that includes an entity mention; resolving the entity mention using a lookup to find tag formats specific to heterogeneous online social environments; encoding the tag formats in different instances of the feed item; and automatically cross posting to the heterogeneous online social environments associated with the entity mention using the tag formats compatible with the heterogeneous online social environments. | 8. A method for converting entity mentions in a feed item to online social environments specific identifiers for cross posting on heterogeneous online social environments, the method including: receiving a feed item that includes an entity mention; resolving the entity mention using a lookup to find tag formats specific to heterogeneous online social environments; encoding the tag formats in different instances of the feed item; and automatically cross posting to the heterogeneous online social environments associated with the entity mention using the tag formats compatible with the heterogeneous online social environments. 10. The method of claim 8 , further including looking up a database that is automatically populated by a web crawler. | 0.900171 |
9,208,149 | 6 | 10 | 6. A machine translation method comprising: translating an original sentence which is a character string of a first language into a forward-translated sentence which is a character string of a second language; acquiring, by translating an original word in the original sentence corresponding to a first forward-translated word in the forward-translated sentence, at least one second forward-translated word different from the first forward-translated word, to obtain candidate words including the first forward-translated word and the at least one second forward-translated word; calculating a fluency for each of the candidate words, the fluency indicating naturalness of the forward-translated sentence if each of the candidate words is replaced with the first forward-translated word; obtaining at least one reverse-translated word for each of the candidate words by reverse-translating each candidate word into the first language; calculating a semantic similarity between the original word and each reverse-translated word; and selecting a corrected forward-translated word to be replaced with the first forward-translated word from among the candidate words based on the semantic similarity and fluency. | 6. A machine translation method comprising: translating an original sentence which is a character string of a first language into a forward-translated sentence which is a character string of a second language; acquiring, by translating an original word in the original sentence corresponding to a first forward-translated word in the forward-translated sentence, at least one second forward-translated word different from the first forward-translated word, to obtain candidate words including the first forward-translated word and the at least one second forward-translated word; calculating a fluency for each of the candidate words, the fluency indicating naturalness of the forward-translated sentence if each of the candidate words is replaced with the first forward-translated word; obtaining at least one reverse-translated word for each of the candidate words by reverse-translating each candidate word into the first language; calculating a semantic similarity between the original word and each reverse-translated word; and selecting a corrected forward-translated word to be replaced with the first forward-translated word from among the candidate words based on the semantic similarity and fluency. 10. The method according to claim 6 , wherein the obtaining the at least one reverse-translated word acquires, from external device, at least one word obtained by translating each of the candidate words into the first language and adds the acquired the at least one word as the reverse-translated words. | 0.688912 |
8,819,078 | 1 | 11 | 1. A method of event processing for graph structured data, comprising: storing graph-structured data in a store, the graph-structured data including a plurality of vertex, edge, and/or property graph elements; defining a first graph view of a characteristic of vertex, edge, and/or property graph elements; determining a subgraph as a subset of the plurality of vertex, edge, and/or property graph elements that have the characteristic of vertex, edge, and/or property graph elements defined by the first graph view; and processing the vertex, edge, and/or property graph elements of the subgraph responsive to a predefined event that occurs on at least one of the vertex, edge, and/or property graph elements of the subgraph. | 1. A method of event processing for graph structured data, comprising: storing graph-structured data in a store, the graph-structured data including a plurality of vertex, edge, and/or property graph elements; defining a first graph view of a characteristic of vertex, edge, and/or property graph elements; determining a subgraph as a subset of the plurality of vertex, edge, and/or property graph elements that have the characteristic of vertex, edge, and/or property graph elements defined by the first graph view; and processing the vertex, edge, and/or property graph elements of the subgraph responsive to a predefined event that occurs on at least one of the vertex, edge, and/or property graph elements of the subgraph. 11. The method of claim 1 , wherein defining the first graph view includes storing the graph view as an object with pointers to all vertex, edge, and/or property graph elements of the subset of the plurality of graph elements that have the characteristic of vertex, edge, and/or property graph elements defined by the first graph view. | 0.777556 |
9,158,655 | 1 | 2 | 1. An apparatus for assessing standards compliance during computer development, comprising: an interface operable to receive a first selection from a user, the first selection comprising a design assessment ruleset to be used for evaluating a computer change, the design assessment ruleset comprising one or more design assessment rules, each design assessment rule associated with a condition that determines whether the design assessment rule is evaluated, the condition indicating whether the assessment rule is associated with one or more of a pilot project and a completed project; a memory operable to store the design assessment ruleset; and a processor communicatively coupled to the interface and the memory, the processor operable to: determine whether the computer change is associated with a pilot project; communicate to the user a design evaluation question relating to each design assessment rule whose associated condition indicates that the assessment rule is associated with a pilot project, an answer to the design evaluation question indicating an extent to which the computer change complies with the design assessment rule, the answer indicating one of compliance, noncompliance, or partial compliance; and determine one or more design scores based on the answer to each design evaluation question. | 1. An apparatus for assessing standards compliance during computer development, comprising: an interface operable to receive a first selection from a user, the first selection comprising a design assessment ruleset to be used for evaluating a computer change, the design assessment ruleset comprising one or more design assessment rules, each design assessment rule associated with a condition that determines whether the design assessment rule is evaluated, the condition indicating whether the assessment rule is associated with one or more of a pilot project and a completed project; a memory operable to store the design assessment ruleset; and a processor communicatively coupled to the interface and the memory, the processor operable to: determine whether the computer change is associated with a pilot project; communicate to the user a design evaluation question relating to each design assessment rule whose associated condition indicates that the assessment rule is associated with a pilot project, an answer to the design evaluation question indicating an extent to which the computer change complies with the design assessment rule, the answer indicating one of compliance, noncompliance, or partial compliance; and determine one or more design scores based on the answer to each design evaluation question. 2. The apparatus of claim 1 , wherein the processor is further operable to determine whether to permit building of the computer change based at least on the one or more design scores. | 0.927092 |
4,882,759 | 8 | 9 | 8. The method of claim 7 wherein step (b) includes the step of: (k) storing a context for a phonetic model based on the phonetic model occurring therebefore and the phonetic model occurring thereafter. | 8. The method of claim 7 wherein step (b) includes the step of: (k) storing a context for a phonetic model based on the phonetic model occurring therebefore and the phonetic model occurring thereafter. 9. The method of claim 8 wherein step (h) further includes the steps of: (l) determining whether a new word phonetic model has the same preceding phonetic model and the same succeeding phonetic model as a known word phonetic model; and (m) representing the word piece corresponding to the new word phonetic model by the string of fenemic models for the known word phonetic model. | 0.900837 |
4,151,659 | 1 | 10 | 1. A portable machine for teaching students to read comprising: keyboard means having manually actuable switches at least some of which individually correspond to separate grammatical characteristics, display means for providing a visual display of words by generating images of words internally transmitted thereto in electronically encoded form, indicators of correctness and incorrectness, word set defining means for storing a multiplicity of words in electronically encoded form, and for assembling said words in sets wherein said sets are separately selected in response to separate actuation of said switch means corresponding to grammatical characteristics, random selection means connected to said word set defining means for selecting words at random from among words in a selected set and for transmitting words so selected to said display means for generation of an image containing at least one such word for visual observation, means for internally associating a particular one of said manually actuable switches with each of said displayed words, comparison means for sensing actuation of a manually selected switch and for ascertaining whether said selected switch is the switch internally associated with said image, and for providing an output indicative of the comparison results to said indicators of correctness and incorrectness. | 1. A portable machine for teaching students to read comprising: keyboard means having manually actuable switches at least some of which individually correspond to separate grammatical characteristics, display means for providing a visual display of words by generating images of words internally transmitted thereto in electronically encoded form, indicators of correctness and incorrectness, word set defining means for storing a multiplicity of words in electronically encoded form, and for assembling said words in sets wherein said sets are separately selected in response to separate actuation of said switch means corresponding to grammatical characteristics, random selection means connected to said word set defining means for selecting words at random from among words in a selected set and for transmitting words so selected to said display means for generation of an image containing at least one such word for visual observation, means for internally associating a particular one of said manually actuable switches with each of said displayed words, comparison means for sensing actuation of a manually selected switch and for ascertaining whether said selected switch is the switch internally associated with said image, and for providing an output indicative of the comparison results to said indicators of correctness and incorrectness. 10. Apparatus according to claim 1 further characterized in that at least one set of words is assembled having a considerable number of words with S and Z sounds. | 0.891856 |
8,880,430 | 1 | 9 | 1. A computer-implemented method for identifying an on-line bank account utilized for business purposes, the method being performed by a computer and comprising: the computer parsing data of an on-line bank account into a plurality of segments; the computer applying a first set of rules to individual segments and a second set of rules to groups of multiple segments; and the computer determining whether the on-line bank account is a business account or utilized for business purposes based at least in part upon respective scores generated by application of respective first and second sets of rules. | 1. A computer-implemented method for identifying an on-line bank account utilized for business purposes, the method being performed by a computer and comprising: the computer parsing data of an on-line bank account into a plurality of segments; the computer applying a first set of rules to individual segments and a second set of rules to groups of multiple segments; and the computer determining whether the on-line bank account is a business account or utilized for business purposes based at least in part upon respective scores generated by application of respective first and second sets of rules. 9. The method of claim 1 , the on-line bank account data comprising: a name on the on-line bank account and a transaction history of the on-line bank account, the name being parsed into a first plurality of segments, and the transaction history being parsed into a second plurality of segments; and the computer applying a first set of rules to each of the first plurality of segments and the second plurality of segments. | 0.501182 |
8,122,026 | 15 | 21 | 15. A computer readable storage medium storing one or more programs configured for execution by a computer, the one or more programs comprising instructions to: identify a first set of documents based on a first model and a first set of features, wherein the first model includes a first set of rules specifying at least one combination of features from the first set of features that are sufficient for identifying a document referring to an entity, each document of the first set of documents comprising a sufficient number of features in common with the first set of features to identify a document referring to the entity according to the first model; determine a second model based on the features of the first set of documents, wherein the second model includes a second set of rules specifying at least one combination of features from the first set of documents that are sufficient for identifying a document referring to the entity; identify a second set of documents based on the second model and the first set of features, each document of the second set of documents comprising a sufficient number of features in common with the first set of features to identify a document referring to the entity according to the second model; identify a second set of features based on the second set of documents; determine if the second set of features are associated with the entity; and responsive to determining that the second set of features are associated with the entity, identifying a third set of documents based on a third model and the second set of features, each of the third set of documents comprising a sufficient number of features in common with the second set of features to identify a document referring to the entity according to the third model. | 15. A computer readable storage medium storing one or more programs configured for execution by a computer, the one or more programs comprising instructions to: identify a first set of documents based on a first model and a first set of features, wherein the first model includes a first set of rules specifying at least one combination of features from the first set of features that are sufficient for identifying a document referring to an entity, each document of the first set of documents comprising a sufficient number of features in common with the first set of features to identify a document referring to the entity according to the first model; determine a second model based on the features of the first set of documents, wherein the second model includes a second set of rules specifying at least one combination of features from the first set of documents that are sufficient for identifying a document referring to the entity; identify a second set of documents based on the second model and the first set of features, each document of the second set of documents comprising a sufficient number of features in common with the first set of features to identify a document referring to the entity according to the second model; identify a second set of features based on the second set of documents; determine if the second set of features are associated with the entity; and responsive to determining that the second set of features are associated with the entity, identifying a third set of documents based on a third model and the second set of features, each of the third set of documents comprising a sufficient number of features in common with the second set of features to identify a document referring to the entity according to the third model. 21. The computer readable storage medium of claim 15 , further comprising instructions to: store at least one feature of the second set of features as a fact in a fact repository. | 0.688153 |
9,460,708 | 1 | 2 | 1. A system, comprising: a language model generated from language model seed text, the language model seed text comprising first entries that correctly utilize a first word and second entries that correctly utilize a second word, wherein the second word shares a pronunciation with the first word and has a different spelling than the first word; a dictionary of available data substitutions, the available data substitutions including a substitution of the second word for the first word; a transducer configured to process speech recognition data utilizing the language model and the dictionary, wherein, to process the speech recognition data, the transducer is further configured to: establish probabilities including a first probability of a first alternative that replaces an occurrence of the first word in the speech recognition data with the second word, and a second probability of a second alternative that leaves the occurrence of the first word in the speech recognition data without modification, the probabilities being established based on a third word that appears in sequence with the occurrence of the first word in the speech recognition data; and when the first probability exceeds the second probability, applying the first alternative by replacing the occurrence of the first word in the speech recognition data with the second word that shares the pronunciation with the first word and has a different spelling than the first word; and a computing device configured to execute at least the transducer. | 1. A system, comprising: a language model generated from language model seed text, the language model seed text comprising first entries that correctly utilize a first word and second entries that correctly utilize a second word, wherein the second word shares a pronunciation with the first word and has a different spelling than the first word; a dictionary of available data substitutions, the available data substitutions including a substitution of the second word for the first word; a transducer configured to process speech recognition data utilizing the language model and the dictionary, wherein, to process the speech recognition data, the transducer is further configured to: establish probabilities including a first probability of a first alternative that replaces an occurrence of the first word in the speech recognition data with the second word, and a second probability of a second alternative that leaves the occurrence of the first word in the speech recognition data without modification, the probabilities being established based on a third word that appears in sequence with the occurrence of the first word in the speech recognition data; and when the first probability exceeds the second probability, applying the first alternative by replacing the occurrence of the first word in the speech recognition data with the second word that shares the pronunciation with the first word and has a different spelling than the first word; and a computing device configured to execute at least the transducer. 2. The system of claim 1 , further comprising an error model that is accessible by the transducer and wherein the error model includes: available letter substitutions including an individual available letter substitution that matches a letter substitution misspelling of a third word in the speech recognition data, wherein the letter substitution misspelling substitutes a second letter for a first letter that is present in a corrected form of the third word, available letter deletions including an individual available letter deletion that matches a letter deletion misspelling of the third word in the speech recognition data, wherein the letter deletion misspelling is identical to the corrected form of the third word except for a single missing letter that is present in the corrected form of the third word and not the letter deletion misspelling, and available letter additions including an individual available letter addition that matches a letter addition misspelling of the third word in the speech recognition data, wherein the letter addition misspelling is identical to the corrected form of the third word except for a single additional letter that is present in the letter addition misspelling but not the corrected form of the third word. | 0.500397 |
7,895,144 | 1 | 8 | 1. A risk assessment method comprising: receiving, by an inference engine within a computing system, first sensor cohort data associated with a first cohort, said first cohort located within a pre/post security area within an airport; receiving, by said inference engine, first group technology inferences associated with said first cohort; generating, by said inference engine, first risk cohort inferences, said generating said first risk cohort inferences based on said first group technology inferences and said first sensor cohort data; receiving, by said inference engine, first inference data generated by said inference engine, said first inference data comprising a first plurality of inferences associated with said first cohort and a security perimeter surrounding said airport; receiving, by said inference engine, second inference data generated by said inference engine, said second inference data comprising a second of plurality of inferences associated with said first cohort and said pre/post security area within said airport; generating, by said inference engine, third inference data, said third inference data comprising a third plurality of inferences associated with said first cohort and said pre/post security area within said airport, wherein said generating said third inference data is based on said first risk cohort inferences, said first inference data, and said second inference data; generating, by said inference engine based on said third inference data, a first associated risk level score for said first cohort; and storing, by said computing system, said third inference data and said first associated risk level score. | 1. A risk assessment method comprising: receiving, by an inference engine within a computing system, first sensor cohort data associated with a first cohort, said first cohort located within a pre/post security area within an airport; receiving, by said inference engine, first group technology inferences associated with said first cohort; generating, by said inference engine, first risk cohort inferences, said generating said first risk cohort inferences based on said first group technology inferences and said first sensor cohort data; receiving, by said inference engine, first inference data generated by said inference engine, said first inference data comprising a first plurality of inferences associated with said first cohort and a security perimeter surrounding said airport; receiving, by said inference engine, second inference data generated by said inference engine, said second inference data comprising a second of plurality of inferences associated with said first cohort and said pre/post security area within said airport; generating, by said inference engine, third inference data, said third inference data comprising a third plurality of inferences associated with said first cohort and said pre/post security area within said airport, wherein said generating said third inference data is based on said first risk cohort inferences, said first inference data, and said second inference data; generating, by said inference engine based on said third inference data, a first associated risk level score for said first cohort; and storing, by said computing system, said third inference data and said first associated risk level score. 8. The method of claim 1 , wherein said first sensor cohort data comprises data selected from the group consisting of audio sensor data, video sensor data, biometrical sensor data, olfactory sensor data, and sensory/actuator data. | 0.92595 |
6,061,063 | 7 | 14 | 7. The computer product of claim 6, further including computer readable code configured to cause a computer to displace said m-1 fields within said display region a distance h in a second direction when a second button is clicked. | 7. The computer product of claim 6, further including computer readable code configured to cause a computer to displace said m-1 fields within said display region a distance h in a second direction when a second button is clicked. 14. The computer product of claim 7, wherein said second button is disabled when said last field of said list of n fields is displayed within said display region. | 0.947539 |
9,697,239 | 2 | 3 | 2. The system of claim 1 , wherein the operations further comprise differentiating the extended model tokens into one or more of class tokens, cluster tokens, relation tokens, derived tokens, and combinations thereof. | 2. The system of claim 1 , wherein the operations further comprise differentiating the extended model tokens into one or more of class tokens, cluster tokens, relation tokens, derived tokens, and combinations thereof. 3. The system of claim 2 , wherein a relation token references: one or more connections amongst instance tokens; a source token being one of a class token and a cluster token, the source token representing a source of the connection; and a target token being one of a class token and a cluster token, the target token representing a target of the connection. | 0.913233 |
8,756,058 | 8 | 10 | 8. A speech recognition result output method, comprising: a word speech recognition step of converting input speech to a recognition result word sequence by using a predetermined word dictionary for recognition, the word dictionary including word entries arranged in a predetermined order; a syllable recognition step of converting the input speech to a recognition result syllable sequence; a segment determination step of determining a segment corresponding to an unknown word in the converted recognition result word sequence; and an output step of obtaining a partial syllable sequence corresponding to the determined segment from the recognition result syllable sequence, determining an ordered position of the partial syllable sequence in the word dictionary on the assumption that the partial syllable sequence exists in the word dictionary, and displaying on a display device one or more word entries in the vicinity of the ordered position in the word dictionary, together with the recognition result word sequence, wherein in the output step, the recognition result word sequence is displayed after the recognition result word sequence is replaced with the partial syllable sequence with respect to the segment determined by the segment determination step, and in the output step, the segment corresponding to the unknown word is highlighted. | 8. A speech recognition result output method, comprising: a word speech recognition step of converting input speech to a recognition result word sequence by using a predetermined word dictionary for recognition, the word dictionary including word entries arranged in a predetermined order; a syllable recognition step of converting the input speech to a recognition result syllable sequence; a segment determination step of determining a segment corresponding to an unknown word in the converted recognition result word sequence; and an output step of obtaining a partial syllable sequence corresponding to the determined segment from the recognition result syllable sequence, determining an ordered position of the partial syllable sequence in the word dictionary on the assumption that the partial syllable sequence exists in the word dictionary, and displaying on a display device one or more word entries in the vicinity of the ordered position in the word dictionary, together with the recognition result word sequence, wherein in the output step, the recognition result word sequence is displayed after the recognition result word sequence is replaced with the partial syllable sequence with respect to the segment determined by the segment determination step, and in the output step, the segment corresponding to the unknown word is highlighted. 10. The speech recognition result output method according to claim 8 , wherein the predetermined order is the Japanese syllabary order. | 0.852298 |
7,630,974 | 28 | 29 | 28. The method of claim 25 , further comprising: determining said preferred language associated with said request. | 28. The method of claim 25 , further comprising: determining said preferred language associated with said request. 29. The method of claim 28 , wherein: determining said preferred language includes determining said preferred language from at least one of a Uniform Resource Locator (URL) associated with said request, a hypertext transfer protocol (HTTP) header variable associated with said request, an identity profile associated with said request, and a cookie associated with said request. | 0.919023 |
8,996,555 | 10 | 11 | 10. A non-transitory computer readable storage medium as in claim 8 wherein the answering system comprises a search plugin configured to: recognize certain semantics from the parse graph; and create the plurality of structured queries from the certain semantics. | 10. A non-transitory computer readable storage medium as in claim 8 wherein the answering system comprises a search plugin configured to: recognize certain semantics from the parse graph; and create the plurality of structured queries from the certain semantics. 11. A non-transitory computer readable storage medium as in claim 10 wherein the parse graph is captured in Resource Description Framework (RDF) form by the search plugin, and the plurality of structured queries are created in the SparQL query language. | 0.900079 |
9,984,687 | 8 | 11 | 8. A method for driving an electronic device, the method comprising: pre-storing Query and Answer (Q/A) set, the stored Q/A set includes a plurality of Q/As; acquiring a speech query associated with a query uttered by a user; identifying at least one Q/A of the plurality of Q/As based on criteria; generating a query list including candidate queries of the identified at least one Q/A, the candidate queries having the same or similar semantic as a semantic of the acquired speech query; providing the query list for display; and performing an operation related to a candidate query selected from the query list provided for display, wherein the criteria include a time and a place of the query being uttered by the user, and at least one of user situation information, a user profile, or a past service usage pattern. | 8. A method for driving an electronic device, the method comprising: pre-storing Query and Answer (Q/A) set, the stored Q/A set includes a plurality of Q/As; acquiring a speech query associated with a query uttered by a user; identifying at least one Q/A of the plurality of Q/As based on criteria; generating a query list including candidate queries of the identified at least one Q/A, the candidate queries having the same or similar semantic as a semantic of the acquired speech query; providing the query list for display; and performing an operation related to a candidate query selected from the query list provided for display, wherein the criteria include a time and a place of the query being uttered by the user, and at least one of user situation information, a user profile, or a past service usage pattern. 11. The method of claim 8 , further comprising: generating semantic information related to a semantic of the acquired speech query, wherein the candidate queries having the same or similar semantic as the acquired speech are those having same semantic information. | 0.69515 |
7,977,562 | 15 | 17 | 15. The at least one computer storage medium of claim 12 , wherein synthesizing the lyrics with the melody comprises: breaking down words in the lyrics into sub-phonemic units; converting the sub-phonemic units into a sequence of contextual labels; and determining a matching contextual parametric model for each contextual label, wherein the sequence of contextual parametric models is comprised of the matching contextual model for each contextual label. | 15. The at least one computer storage medium of claim 12 , wherein synthesizing the lyrics with the melody comprises: breaking down words in the lyrics into sub-phonemic units; converting the sub-phonemic units into a sequence of contextual labels; and determining a matching contextual parametric model for each contextual label, wherein the sequence of contextual parametric models is comprised of the matching contextual model for each contextual label. 17. The at least one computer storage medium of claim 15 , wherein the matching contextual parametric model for each contextual label is a Hidden Markov Model (HMM). | 0.890584 |
8,090,722 | 13 | 14 | 13. The system of claim 12 , the first level document being related to the base document as one or more of, an attached document, an attaching document, a referenced document, and a referencing document. | 13. The system of claim 12 , the first level document being related to the base document as one or more of, an attached document, an attaching document, a referenced document, and a referencing document. 14. The system of claim 13 , where the relevance logic is to determine the relevance of the virtual document to the query using a term frequency value, an inverse document frequency value, and a nearness value. | 0.951253 |
8,996,377 | 1 | 5 | 1. A method for blending recorded speech with text-to-speech (TTS) for specific domains, comprising: receiving input text; identifying a domain from the input text; determining a static part from the input text that has previously been recorded and stored within a data store, wherein determining the static part comprises detecting the static part based on recorded units for the identified domain; determining a dynamic part from the input text; and blending the static part with the dynamic part within a TTS engine. | 1. A method for blending recorded speech with text-to-speech (TTS) for specific domains, comprising: receiving input text; identifying a domain from the input text; determining a static part from the input text that has previously been recorded and stored within a data store, wherein determining the static part comprises detecting the static part based on recorded units for the identified domain; determining a dynamic part from the input text; and blending the static part with the dynamic part within a TTS engine. 5. The method of claim 1 , further comprising attempting to maintain a prosody of the static part in the dynamic part output by a TTS synthesizer. | 0.900137 |
9,418,058 | 1 | 9 | 1. A social media issue processing device, comprising: a processor that executes program code stored on computer readable storage medium, the program code comprising: question analyzing program code that analyzes a question type related with a question input based on a question pattern dictionary which is stored in advance; social media analyzing program code that performs issue period recognition for the question, question type based analysis, question based summary creation, and question based reliability calculation; and report creating program code that creates a summary in accordance with at least one of correlation between the question type and the question based summary, correlation between the issue period and a question type based analysis result, and correlation between the reliability and the question based summary. | 1. A social media issue processing device, comprising: a processor that executes program code stored on computer readable storage medium, the program code comprising: question analyzing program code that analyzes a question type related with a question input based on a question pattern dictionary which is stored in advance; social media analyzing program code that performs issue period recognition for the question, question type based analysis, question based summary creation, and question based reliability calculation; and report creating program code that creates a summary in accordance with at least one of correlation between the question type and the question based summary, correlation between the issue period and a question type based analysis result, and correlation between the reliability and the question based summary. 9. The device of claim 1 , further comprising: display program code or input program code that supports an input function to request at least one of the issue period selection and the question analysis result correction. | 0.869359 |
7,644,071 | 1 | 2 | 1. A computer implemented method for searching for a keyword within a document, the method comprising: receiving a keyword search term and a view area size for the document, wherein the view area size is a desired amount of context expressed in characters, lines of text or sentences; identifying a plurality of occurrences of the search term within the document, and the view area size surrounding each of the plurality of occurrences of the search term to form a plurality of view area sizes; and displaying an edited view of the document, the edited view comprising a sequence of the plurality of view area sizes, wherein each of the plurality of view area sizes are separated by one of a plurality of interactive bifurcated boundary lines, each of the plurality of interactive bifurcated boundary lines having a conceal/reveal indicator to form a plurality of conceal/reveal indicators, wherein each of the conceal/reveal indicators is a graphical indication of an amount of the document that is concealed between sequential view areas of the plurality of view area sizes. | 1. A computer implemented method for searching for a keyword within a document, the method comprising: receiving a keyword search term and a view area size for the document, wherein the view area size is a desired amount of context expressed in characters, lines of text or sentences; identifying a plurality of occurrences of the search term within the document, and the view area size surrounding each of the plurality of occurrences of the search term to form a plurality of view area sizes; and displaying an edited view of the document, the edited view comprising a sequence of the plurality of view area sizes, wherein each of the plurality of view area sizes are separated by one of a plurality of interactive bifurcated boundary lines, each of the plurality of interactive bifurcated boundary lines having a conceal/reveal indicator to form a plurality of conceal/reveal indicators, wherein each of the conceal/reveal indicators is a graphical indication of an amount of the document that is concealed between sequential view areas of the plurality of view area sizes. 2. The computer implemented method of claim 1 , further comprising: detecting an interaction with an upper boundary of a first bifurcated boundary line of the plurality of interactive bifurcated boundary lines, wherein the upper boundary of the first bifurcated boundary line is associated with a first view area size of the plurality of view area sizes; and responsive to detecting an interaction with the upper boundary of the first bifurcated boundary line, resizing the first view area size; updating the reveal/conceal indicator associated with the first bifurcated boundary line to show an updated graphical indication of an amount of the document that is concealed between the first view area size and a second view area size of the plurality of view area sizes; detecting an interaction with a lower boundary of the first bifurcated boundary line of the plurality of interactive bifurcated boundary lines, wherein the lower boundary of the first boundary line is associated with the second view area size of the plurality of view area sizes; and responsive to detecting an interaction with the lower boundary of the first bifurcated boundary line, resizing the second view area size. | 0.500419 |
9,245,052 | 15 | 17 | 15. A system comprising: at least one computing device; a processor included in the at least one computing device; memory coupled to the processor; an input device; and a video adapter via which the at least one computing device is configured to substantially simultaneously provide, in response to receiving, via the input device, one or more characters that form at least a portion of a query string that does not include an explicit submission of the portion of the query string, both query results and query refinement options, where the one or more received characters match at least one pattern that indicates one or more characters followed by a space character. | 15. A system comprising: at least one computing device; a processor included in the at least one computing device; memory coupled to the processor; an input device; and a video adapter via which the at least one computing device is configured to substantially simultaneously provide, in response to receiving, via the input device, one or more characters that form at least a portion of a query string that does not include an explicit submission of the portion of the query string, both query results and query refinement options, where the one or more received characters match at least one pattern that indicates one or more characters followed by a space character. 17. The system of claim 15 where the at least one pattern further indicates the received one or more characters followed by a time delay before any additional characters are received. | 0.593333 |
9,468,852 | 1 | 12 | 1. A method comprising: capturing, using one or more computing devices, information about a user; wherein capturing actions includes at least one of: determining which type of items are purchased online for the user; determining which type of computer programs are downloaded for or used by the user; determining topics reflected in electronic communications of associated with the user; determining topics reflected in items purchased online for the user; determining to which online social communities the user belongs; determining interests reflected in comments associated with the user in online social applications; determining persons to whom the user is socially connected in online social applications; capturing how an avatar of the user interacts with one or more other characters in an online environment; randomly inserting questions that relate to play personality or intrinsic motivators with other questions presented within an online game environment; capturing one or more images of the user as the user engages in an activity; detecting eye movement of the user as the user engages in an activity; detecting pupil size changes of the user as the user engages in an activity; or monitoring how the user makes purchases at or interacts with virtual stores or venues; wherein capturing information about the user further comprises presenting an online assessment that includes a series of questions designed to assess at least one of: a1) which play type, of a plurality of play types, satisfies the user's need for play; or b1) intrinsic motivators of the user; based, at least in part, on the information about the user that is captured using the one or more computing devices, automatically determining at least one of: a2) a play personality of the user, including: estimating a degree to which each of the plurality of play types satisfies the user's need for play; and determining that one or more particular play types, of the plurality of play types, best satisfy the user's need for play; wherein the plurality of play types includes two or more of: Object, Pretend, Social, Rough and Tumble, Body, Exploratory, Celebratory, Competitive, Ritual, Narrative, Fantasy or Games/Gaming; or b2) intrinsic motivators of the user; storing, in a computer-readable medium, data that reflects at least one of: a3) the play personality of the user; or b3) intrinsic motivators of the user; and wherein the method is performed by one or more computing devices. | 1. A method comprising: capturing, using one or more computing devices, information about a user; wherein capturing actions includes at least one of: determining which type of items are purchased online for the user; determining which type of computer programs are downloaded for or used by the user; determining topics reflected in electronic communications of associated with the user; determining topics reflected in items purchased online for the user; determining to which online social communities the user belongs; determining interests reflected in comments associated with the user in online social applications; determining persons to whom the user is socially connected in online social applications; capturing how an avatar of the user interacts with one or more other characters in an online environment; randomly inserting questions that relate to play personality or intrinsic motivators with other questions presented within an online game environment; capturing one or more images of the user as the user engages in an activity; detecting eye movement of the user as the user engages in an activity; detecting pupil size changes of the user as the user engages in an activity; or monitoring how the user makes purchases at or interacts with virtual stores or venues; wherein capturing information about the user further comprises presenting an online assessment that includes a series of questions designed to assess at least one of: a1) which play type, of a plurality of play types, satisfies the user's need for play; or b1) intrinsic motivators of the user; based, at least in part, on the information about the user that is captured using the one or more computing devices, automatically determining at least one of: a2) a play personality of the user, including: estimating a degree to which each of the plurality of play types satisfies the user's need for play; and determining that one or more particular play types, of the plurality of play types, best satisfy the user's need for play; wherein the plurality of play types includes two or more of: Object, Pretend, Social, Rough and Tumble, Body, Exploratory, Celebratory, Competitive, Ritual, Narrative, Fantasy or Games/Gaming; or b2) intrinsic motivators of the user; storing, in a computer-readable medium, data that reflects at least one of: a3) the play personality of the user; or b3) intrinsic motivators of the user; and wherein the method is performed by one or more computing devices. 12. The method of claim 1 wherein: capturing information about the user includes determining interests reflected in comments made in association with the user in online social applications; and automatically determining is based, at least in part, on interests reflected in comments made by the user in online social applications. | 0.501511 |
8,407,805 | 5 | 6 | 5. The method of claim 1 , wherein the step (f) further comprises determining the clearance level of the recipient of the email message by applying a set of rules. | 5. The method of claim 1 , wherein the step (f) further comprises determining the clearance level of the recipient of the email message by applying a set of rules. 6. The method of claim 5 , further comprising: introducing the set of rules for mapping a category of the recipient of the email message to the clearance level of the recipient, the category characterizing a company and a group within the company to which the recipient belongs; and determining the clearance level of the recipient by using the set of rules. | 0.920196 |
10,162,512 | 1 | 5 | 1. A method of controlling a function of a wearable electronic device, comprising: detecting a predetermined gesture by a motion sensor of the wearable electronic device, for activating a voice recognition function of the wearable electronic device, wherein the wearable electronic device is worn on a wrist of a user; driving a timer for calculating a time period from an instance when the predetermined gesture is detected; recognizing eyeballs of the user through one or more cameras of the wearable electronic device; activating a touch screen and the voice recognition function when the time period calculated by the driven timer exceeds a predetermined time period and the eyeballs of the user are recognized; displaying, on the touch screen, a screen of the activated voice recognition function for receiving a voice input to control the voice recognition function of the wearable device; receiving the voice input through the activated voice recognition function; determining whether one or more functions executable by the received voice input include a user's personal information of the wearable electronic device; if the one or more functions executable based on the voice input do not include the user's personal information, analyzing the voice input by using the activated voice recognition function, and executing a function that does not include the user's personal information, based on the voice input; and if the one or more functions executable based on the voice input include the user's personal information of the wearable electronic device, outputting, through the activated touch screen, a message indicating that the one or more functions including the user's personal information are not executable, wherein the output message includes a function list of one or more functions that are executable by at least one voice input different from the received voice input and do not include the user's personal information. | 1. A method of controlling a function of a wearable electronic device, comprising: detecting a predetermined gesture by a motion sensor of the wearable electronic device, for activating a voice recognition function of the wearable electronic device, wherein the wearable electronic device is worn on a wrist of a user; driving a timer for calculating a time period from an instance when the predetermined gesture is detected; recognizing eyeballs of the user through one or more cameras of the wearable electronic device; activating a touch screen and the voice recognition function when the time period calculated by the driven timer exceeds a predetermined time period and the eyeballs of the user are recognized; displaying, on the touch screen, a screen of the activated voice recognition function for receiving a voice input to control the voice recognition function of the wearable device; receiving the voice input through the activated voice recognition function; determining whether one or more functions executable by the received voice input include a user's personal information of the wearable electronic device; if the one or more functions executable based on the voice input do not include the user's personal information, analyzing the voice input by using the activated voice recognition function, and executing a function that does not include the user's personal information, based on the voice input; and if the one or more functions executable based on the voice input include the user's personal information of the wearable electronic device, outputting, through the activated touch screen, a message indicating that the one or more functions including the user's personal information are not executable, wherein the output message includes a function list of one or more functions that are executable by at least one voice input different from the received voice input and do not include the user's personal information. 5. The method of claim 1 , further comprising: if a command is not input for a predetermined time after the voice recognition function is executed, switching the executed function to be in an inactive state. | 0.682515 |
8,001,139 | 5 | 7 | 5. The method of claim 1 wherein the first node represents a linguistic word, the second node represents a visual word, and the association score indicates a degree of association between the linguistic word and the visual word. | 5. The method of claim 1 wherein the first node represents a linguistic word, the second node represents a visual word, and the association score indicates a degree of association between the linguistic word and the visual word. 7. The method of claim 5 wherein the association score indicates how strongly the visual word is associated with any image in a set of images described by the linguistic word. | 0.974849 |
9,854,399 | 1 | 3 | 1. A proximity service (ProSe) information transmission method, comprising: receiving, by the receiving ProSe entity, a code message sent by the monitoring terminal, wherein the code message is configured to instruct the receiving ProSe entity to acquire a code word, the code word is allocated to the first terminal by a first ProSe entity, and the first ProSe entity is the ProSe entity in the HPLMN of the first terminal; acquiring, by the receiving ProSe entity, the code word; sending, by the receiving ProSe entity, a monitoring message to the monitoring terminal, wherein the monitoring message carries the code word; receiving, by a receiving ProSe entity at a 3GPP network layer in a home public land mobile network (HPLMN), a first message sent by a monitoring terminal, wherein the first message is configured to instruct the receiving ProSe entity to acquire an application user identity that is allocated to a first application user by a first application server, the first application user is a user of a first application of a first terminal, the first application server is an application server of the first application, and the receiving ProSe entity is a ProSe entity in the HPLMN; acquiring, by the receiving ProSe entity, the application user identity via the HPLMN; and sending, by the receiving ProSe entity at the 3GPP network layer in the HPLMN, a second message to the monitoring terminal, wherein the second message carries the application user identity, and the application user identity is configured to indicate the first application user discovered by the monitoring terminal in the HPLMN, wherein the second message further carries a first application identity, the first application identity is an application identity of the first application, and the first application identity is configured to indicate the first application corresponding to the application user identity. | 1. A proximity service (ProSe) information transmission method, comprising: receiving, by the receiving ProSe entity, a code message sent by the monitoring terminal, wherein the code message is configured to instruct the receiving ProSe entity to acquire a code word, the code word is allocated to the first terminal by a first ProSe entity, and the first ProSe entity is the ProSe entity in the HPLMN of the first terminal; acquiring, by the receiving ProSe entity, the code word; sending, by the receiving ProSe entity, a monitoring message to the monitoring terminal, wherein the monitoring message carries the code word; receiving, by a receiving ProSe entity at a 3GPP network layer in a home public land mobile network (HPLMN), a first message sent by a monitoring terminal, wherein the first message is configured to instruct the receiving ProSe entity to acquire an application user identity that is allocated to a first application user by a first application server, the first application user is a user of a first application of a first terminal, the first application server is an application server of the first application, and the receiving ProSe entity is a ProSe entity in the HPLMN; acquiring, by the receiving ProSe entity, the application user identity via the HPLMN; and sending, by the receiving ProSe entity at the 3GPP network layer in the HPLMN, a second message to the monitoring terminal, wherein the second message carries the application user identity, and the application user identity is configured to indicate the first application user discovered by the monitoring terminal in the HPLMN, wherein the second message further carries a first application identity, the first application identity is an application identity of the first application, and the first application identity is configured to indicate the first application corresponding to the application user identity. 3. The method according to claim 1 , wherein the code message carries a third identity and a first application identity, the third identity is a temporary terminal identity that is allocated to the monitoring terminal by the receiving ProSe entity, and the first application identity is an application identity of the first application; and the acquiring, by the receiving ProSe entity, the code word comprises: sending, by the receiving ProSe entity, a seventh message to the first application server according to the first application identity, wherein the seventh message carries the third identity; receiving, by the receiving ProSe entity, an eighth message sent by the first application server, wherein the eighth message carries a first identity, and the first identity is a temporary terminal identity that is allocated to the first terminal by the first ProSe entity, wherein the temporary terminal identity uniquely determines the monitoring terminal in an evolved packet core network (EPC); sending, by the receiving ProSe entity, a ninth message to the first ProSe entity, wherein the ninth message carries the first identity; and receiving, by the receiving ProSe entity, a tenth message sent by the first ProSe entity, wherein the tenth message carries the code word. | 0.587838 |
9,519,870 | 16 | 17 | 16. A computerized system comprising: one or more processors; and a plurality of components that include computer-executable instructions that are executed by the one or more processors, the components including: a model building component that trains a classifier model for an entity class using positive sample entities that belong to the entity class and negative sample entities that do not belong to the entity class, the model building component also using clicked URLs, search result URLs, and attributes from an entity graph as features of the positive sample entities and negative sample entities to train the classifier model; and a weighting component that employs the classifier model to weight entities in a candidate dictionary to provide weightings for the entities from the candidate dictionary. | 16. A computerized system comprising: one or more processors; and a plurality of components that include computer-executable instructions that are executed by the one or more processors, the components including: a model building component that trains a classifier model for an entity class using positive sample entities that belong to the entity class and negative sample entities that do not belong to the entity class, the model building component also using clicked URLs, search result URLs, and attributes from an entity graph as features of the positive sample entities and negative sample entities to train the classifier model; and a weighting component that employs the classifier model to weight entities in a candidate dictionary to provide weightings for the entities from the candidate dictionary. 17. The system of claim 16 , wherein the positive sample entities are from a seed list and the negative sample entities are from a background entity list, and wherein the seed list and background entity list are generated based on information from at least one selected from the following: an existing entity graph and training data from a spoken language understanding system. | 0.706386 |
8,825,828 | 11 | 17 | 11. A computer for implementing notifications, the computer comprising: a memory for storing data defining notification operations, the data defining notification operations comprising a hierarchy of Uniform Resource Identifiers (URIs) and Extensible Markup Language (XML) document schema defining XML documents; an interface for receiving a notification command comprising an URI identifying a notification resource and a Hypertext Transfer Protocol (HTTP) GET method; and a processor for: determining a notification operation based on the data defining notification operations stored in the memory and the notification command received; obtaining a list of notifications indicated in the notification; and returning a NotificationList XML document. | 11. A computer for implementing notifications, the computer comprising: a memory for storing data defining notification operations, the data defining notification operations comprising a hierarchy of Uniform Resource Identifiers (URIs) and Extensible Markup Language (XML) document schema defining XML documents; an interface for receiving a notification command comprising an URI identifying a notification resource and a Hypertext Transfer Protocol (HTTP) GET method; and a processor for: determining a notification operation based on the data defining notification operations stored in the memory and the notification command received; obtaining a list of notifications indicated in the notification; and returning a NotificationList XML document. 17. The computer according to claim 11 , wherein the interface is further operable to receive a second notification command comprising an HTTP GET method and an URI identifying a notification resource associated with a notification ID, and the processor is further operable to execute a second notification operation comprising obtaining properties of a notification described in the URI and returning a Notification XML document. | 0.559426 |
7,499,915 | 24 | 25 | 24. The method of claim 1 wherein: the entry for a given node includes path data that corresponds to a path, through the structure of the XML document that contains the given node, to the given node; and the method further comprises building a secondary index for accessing entries in said index based on said path data. | 24. The method of claim 1 wherein: the entry for a given node includes path data that corresponds to a path, through the structure of the XML document that contains the given node, to the given node; and the method further comprises building a secondary index for accessing entries in said index based on said path data. 25. A computer-readable storage medium storing one or more sequences of instructions which, when executed by one or more processors, causes the one or more processors to perform the method recited in claim 24 . | 0.959096 |
8,682,668 | 3 | 4 | 3. The language model score look-ahead value imparting device according to claim 2 , wherein the smoothing language model score look-ahead value calculation unit obtains the language model score look-ahead value at each phoneme in the word based on the number of phonemes from a word head to the phoneme. | 3. The language model score look-ahead value imparting device according to claim 2 , wherein the smoothing language model score look-ahead value calculation unit obtains the language model score look-ahead value at each phoneme in the word based on the number of phonemes from a word head to the phoneme. 4. The language model score look-ahead value imparting device according to claim 3 , wherein the smoothing language model score look-ahead value calculation unit obtains a language model score look-ahead value which is within a threshold value of the language model score look-ahead value set based on the number of phonemes from a word head to a phoneme. | 0.829981 |
7,747,607 | 7 | 15 | 7. The method of claim 1 , wherein: the one or more other queries are other queries that satisfy a threshold degree of affinity to said particular query, wherein, for each of the other queries, a degree of affinity comprises a ratio of (a) a third number of sessions that include both said particular query and the other query to (b) a fourth number of sessions that include either said particular query or the other query but not both; and forming a splits dictionary entry for said string based on said affinity set by removing from the affinity set any queries that do not represent sub-strings of the string. | 7. The method of claim 1 , wherein: the one or more other queries are other queries that satisfy a threshold degree of affinity to said particular query, wherein, for each of the other queries, a degree of affinity comprises a ratio of (a) a third number of sessions that include both said particular query and the other query to (b) a fourth number of sessions that include either said particular query or the other query but not both; and forming a splits dictionary entry for said string based on said affinity set by removing from the affinity set any queries that do not represent sub-strings of the string. 15. The method of claim 7 wherein the threshold degree of affinity is greater than one. | 0.970165 |
8,341,252 | 15 | 16 | 15. A method for registering a domain, comprising: receiving a request to register an Internationalized Domain Name (IDN); determining a language category of the request; identifying corresponding code points of variants within the language category for one or more code points in the request; converting each of the one or more code points into a representative code point chosen from among the corresponding code points by applying a deterministic algorithm to a value of each of the corresponding code points; determining, by the computer processor, a generalized variant of the IDN based on the converted code points; comparing a portion of the generalized variant to a stored database of registered IDNs; and determining, by the computer processor, whether to allow registration of the IDN based on whether the portion of the generalized variant matches a portion of a registered IDN; wherein, if the IDN is not allowed to register, the comparing step is repeated with another portion of the generalized variant. | 15. A method for registering a domain, comprising: receiving a request to register an Internationalized Domain Name (IDN); determining a language category of the request; identifying corresponding code points of variants within the language category for one or more code points in the request; converting each of the one or more code points into a representative code point chosen from among the corresponding code points by applying a deterministic algorithm to a value of each of the corresponding code points; determining, by the computer processor, a generalized variant of the IDN based on the converted code points; comparing a portion of the generalized variant to a stored database of registered IDNs; and determining, by the computer processor, whether to allow registration of the IDN based on whether the portion of the generalized variant matches a portion of a registered IDN; wherein, if the IDN is not allowed to register, the comparing step is repeated with another portion of the generalized variant. 16. The method of claim 15 , wherein the generalized variant includes a representative code point for each of the code points of the request. | 0.813492 |
9,009,172 | 1 | 2 | 1. A method for comparing a first XML document with a second XML document comprising: a) parsing, by a computing device, an XML event from the first XML document or the second XML document when an XML event indicator is set, wherein the parsed XML event comprises a start element, the tag value element, and an end element; b) storing, by the computing device, the parsed XML event and storing data associated with a tag value element of the parsed XML event as a node in a first data structure or a second data structure, when the parsed XML event is from the first XML document or the second XML document, respectively, the storing further comprising storing, a tag name, a set of tag attributes, and values of the set of tag attributes of the parsed XML event in in the first data structure or the second data structure when the parsed XML event is the start element parsed from the first XML document or the second XML document; c) comparing, by the computing device, the stored node of the parsed XML event with one or more nodes stored in the first data structure or the second data structure, based on the parsed XML event and a plurality of parameters wherein the comparing further comprises comparing the tag name of the stored node of the parsed XML event with the tag name of a node stored in the second data structure or the first data structure, when the parsed XML event is the start element from the first XML document or the second XML document and wherein setting a node comparison indicator of the stored node of the parsed XML event, the node comparison indicator of the node, and the a comparison indicator to ‘TagMatch’, when the tag name of the stored node of the parsed XML event is equal to the tag name of the node and setting the node comparison indicator of the stored node of the parsed XML event, the node comparison indicator of the node, the status comparison indicator to “TagMismatch”, and the node mismatch indicator to ‘True’, when the taq name of the stored node of the parsed XML event differs from the tag name of the node; d) outputting, by the computing device, a comparison result, based on the parsed XML event and the plurality of parameters, when a node in the one or more nodes stored in the first data structure or the second data structure is a comparable stored node of the stored node of the parsed XML event; e) deleting, by the computing device, the compared stored nodes from the first data structure and the second data structure, based on the parsed XML event and the plurality of parameters, when the compared stored nodes are outputted in the comparison result; f) setting, by the computing device, the XML event indicator to the first XML document or the second XML document on processing the step of parsing, the step of storing, the step of comparing, the step of outputting a comparison result, or the step of deleting, based on the plurality of parameters, performing the step (f), whereby the XML event indicator is set to the first XML document; and g) repeating steps (a) through (e) or step (f) in each iteration, until the first XML document and the second XML document are parsed completely. | 1. A method for comparing a first XML document with a second XML document comprising: a) parsing, by a computing device, an XML event from the first XML document or the second XML document when an XML event indicator is set, wherein the parsed XML event comprises a start element, the tag value element, and an end element; b) storing, by the computing device, the parsed XML event and storing data associated with a tag value element of the parsed XML event as a node in a first data structure or a second data structure, when the parsed XML event is from the first XML document or the second XML document, respectively, the storing further comprising storing, a tag name, a set of tag attributes, and values of the set of tag attributes of the parsed XML event in in the first data structure or the second data structure when the parsed XML event is the start element parsed from the first XML document or the second XML document; c) comparing, by the computing device, the stored node of the parsed XML event with one or more nodes stored in the first data structure or the second data structure, based on the parsed XML event and a plurality of parameters wherein the comparing further comprises comparing the tag name of the stored node of the parsed XML event with the tag name of a node stored in the second data structure or the first data structure, when the parsed XML event is the start element from the first XML document or the second XML document and wherein setting a node comparison indicator of the stored node of the parsed XML event, the node comparison indicator of the node, and the a comparison indicator to ‘TagMatch’, when the tag name of the stored node of the parsed XML event is equal to the tag name of the node and setting the node comparison indicator of the stored node of the parsed XML event, the node comparison indicator of the node, the status comparison indicator to “TagMismatch”, and the node mismatch indicator to ‘True’, when the taq name of the stored node of the parsed XML event differs from the tag name of the node; d) outputting, by the computing device, a comparison result, based on the parsed XML event and the plurality of parameters, when a node in the one or more nodes stored in the first data structure or the second data structure is a comparable stored node of the stored node of the parsed XML event; e) deleting, by the computing device, the compared stored nodes from the first data structure and the second data structure, based on the parsed XML event and the plurality of parameters, when the compared stored nodes are outputted in the comparison result; f) setting, by the computing device, the XML event indicator to the first XML document or the second XML document on processing the step of parsing, the step of storing, the step of comparing, the step of outputting a comparison result, or the step of deleting, based on the plurality of parameters, performing the step (f), whereby the XML event indicator is set to the first XML document; and g) repeating steps (a) through (e) or step (f) in each iteration, until the first XML document and the second XML document are parsed completely. 2. The method of claim 1 , wherein the compared stored nodes are stored in a previous iteration and a current iteration and compared in the previous iteration and the current iteration. | 0.935179 |
9,304,980 | 1 | 2 | 1. A computer-implemented method for identifying software component versions on a file system the method comprising: scanning, by a computer, a plurality of target files to obtain content-based file identifiers; comparing, by the computer, each content-based file identifier to identifiers in a reference library to determine a plurality of candidate versions of software components, each candidate version corresponding to at least one of the content-based file identifiers; identifying a subset of candidate versions from the plurality of candidate versions as present on the file system by, for each candidate version in the plurality of candidate versions: determining, using the reference library, whether the content-based file identifiers uniquely identify the candidate version, a content-based file identifier uniquely identifying the candidate version responsive to the content-based file identifier matching an identifier in the reference library that is associated with the candidate version but is not associated with other candidate versions; and responsive to the content-based file identifiers uniquely identifying the candidate version, determining that the candidate version is present on the file system; and producing a report including the subset of candidate versions identified as present on the file system. | 1. A computer-implemented method for identifying software component versions on a file system the method comprising: scanning, by a computer, a plurality of target files to obtain content-based file identifiers; comparing, by the computer, each content-based file identifier to identifiers in a reference library to determine a plurality of candidate versions of software components, each candidate version corresponding to at least one of the content-based file identifiers; identifying a subset of candidate versions from the plurality of candidate versions as present on the file system by, for each candidate version in the plurality of candidate versions: determining, using the reference library, whether the content-based file identifiers uniquely identify the candidate version, a content-based file identifier uniquely identifying the candidate version responsive to the content-based file identifier matching an identifier in the reference library that is associated with the candidate version but is not associated with other candidate versions; and responsive to the content-based file identifiers uniquely identifying the candidate version, determining that the candidate version is present on the file system; and producing a report including the subset of candidate versions identified as present on the file system. 2. The method of claim 1 , wherein the content based file identifiers comprise MD5 digests. | 0.927548 |
9,706,040 | 7 | 8 | 7. The method as recited in claim 1 , further comprising the step of replaying said text message, said selected avatar, said selected avatar state of mind, said selected avatar state of mind intensity level, and said indication for receiver interaction. | 7. The method as recited in claim 1 , further comprising the step of replaying said text message, said selected avatar, said selected avatar state of mind, said selected avatar state of mind intensity level, and said indication for receiver interaction. 8. The method as recited in claim 7 , further comprising the step of communicating said replayed text message, said selected avatar, said selected avatar state of mind, said selected avatar state of mind intensity level, and said indication for receiver interaction, to another receiver for viewing, wherein said selected avatar is displayed at certain portions said text message. | 0.956462 |
9,904,949 | 14 | 15 | 14. A non-transitory machine readable storage medium having computer readable program code embedded therein for providing product recommendations, comprising: a context item processor to receive and track context items as a product page is navigated; a plurality of similarities datasets comprising products with a similarity to a product on the product page, each of the plurality of similarities datasets being derived from a separate source; a data store selection module to select one of the plurality of similarities datasets as a source of product recommendations based on the context items, wherein the one of the plurality of similarities datasets is selected using a machine learning model that yields patterns representative of an underlying mechanism based on the context items as input for the machine model, and wherein the plurality of similarities datasets are prepared for the product page, and the machine learning model selects the one of the plurality of similarities datasets based on a fallback strategy where one of the plurality of similarity datasets is a new similarities dataset, wherein the fallback strategy is learned by the machine learning model using logged data regarding the plurality of similarities datasets used for past recommendations; a logging module to log the similarities dataset selected by the machine learning model for use in making future recommendations; a ranking module to rank products in the selected one of the plurality of similarities datasets based on the context items; and a content page module to provide the product recommendations for display based on the context items. | 14. A non-transitory machine readable storage medium having computer readable program code embedded therein for providing product recommendations, comprising: a context item processor to receive and track context items as a product page is navigated; a plurality of similarities datasets comprising products with a similarity to a product on the product page, each of the plurality of similarities datasets being derived from a separate source; a data store selection module to select one of the plurality of similarities datasets as a source of product recommendations based on the context items, wherein the one of the plurality of similarities datasets is selected using a machine learning model that yields patterns representative of an underlying mechanism based on the context items as input for the machine model, and wherein the plurality of similarities datasets are prepared for the product page, and the machine learning model selects the one of the plurality of similarities datasets based on a fallback strategy where one of the plurality of similarity datasets is a new similarities dataset, wherein the fallback strategy is learned by the machine learning model using logged data regarding the plurality of similarities datasets used for past recommendations; a logging module to log the similarities dataset selected by the machine learning model for use in making future recommendations; a ranking module to rank products in the selected one of the plurality of similarities datasets based on the context items; and a content page module to provide the product recommendations for display based on the context items. 15. The system of claim 14 , wherein the plurality of similarities datasets include a purchase similarities dataset and a session similarities dataset. | 0.709615 |
9,171,068 | 1 | 9 | 1. A method of providing personalized content recommendations using a computer comprising: providing a user interface on a computing device that monitors a user's information; generating a content signature that includes semantically salient content elements from the monitored user's information, a network address to the monitored user's information that the semantically salient content is based upon, and a plurality of densities of topics embodied in the salient content elements; storing a personal behavioral profile in a memory of the computing device, the memory stored with user's preferences based on a plurality of monitored past behaviors and a collaborative filtering; filtering items in an incoming information stream with the personal behavioral profile and the content signature and responsive to a document ranking based in part on a plurality of phrase inhibitors and a plurality of saturation topic phrases; and receiving only those items of the incoming information stream that match and is responsive to the document ranking and the personal behavioral profile for a certain information consumption mode of the user. | 1. A method of providing personalized content recommendations using a computer comprising: providing a user interface on a computing device that monitors a user's information; generating a content signature that includes semantically salient content elements from the monitored user's information, a network address to the monitored user's information that the semantically salient content is based upon, and a plurality of densities of topics embodied in the salient content elements; storing a personal behavioral profile in a memory of the computing device, the memory stored with user's preferences based on a plurality of monitored past behaviors and a collaborative filtering; filtering items in an incoming information stream with the personal behavioral profile and the content signature and responsive to a document ranking based in part on a plurality of phrase inhibitors and a plurality of saturation topic phrases; and receiving only those items of the incoming information stream that match and is responsive to the document ranking and the personal behavioral profile for a certain information consumption mode of the user. 9. The method of claim 1 further comprising extracting a content signature in response to inferring word level user dwell times. | 0.821229 |
9,858,349 | 1 | 3 | 1. A method comprising: analyzing contents of a document to identify a plurality of document elements that collectively constitute the contents of the document, and storing, for each of the identified document elements, a database entry having a unique respective document-element identifier; creating a plurality of anchors dispersed throughout the document by storing, for each of a plurality of anchor locations, a respective database entry comprising a unique anchor identifier; storing a document view that represents the document as an ordered list of the document-element identifiers of the identified document elements, and listing at least some of the anchor identifiers in the document view interspersed with or nested within the document-element identifiers; and in response to selection of a portion of the document, generating a referencing address uniquely identifying the selected portion, the referencing address comprising one or more anchor identifiers of one or more respective anchors associated with the selected portion. | 1. A method comprising: analyzing contents of a document to identify a plurality of document elements that collectively constitute the contents of the document, and storing, for each of the identified document elements, a database entry having a unique respective document-element identifier; creating a plurality of anchors dispersed throughout the document by storing, for each of a plurality of anchor locations, a respective database entry comprising a unique anchor identifier; storing a document view that represents the document as an ordered list of the document-element identifiers of the identified document elements, and listing at least some of the anchor identifiers in the document view interspersed with or nested within the document-element identifiers; and in response to selection of a portion of the document, generating a referencing address uniquely identifying the selected portion, the referencing address comprising one or more anchor identifiers of one or more respective anchors associated with the selected portion. 3. The method of claim 1 , wherein the plurality of anchors are created at breakpoints placed at one or more of the end of sentences, the end of paragraphs, or punctuation marks within the document. | 0.836364 |
10,011,285 | 16 | 17 | 16. A method for displaying a pictorial language executed by a processing circuitry and comprising: receiving output from a plurality of sensors; receiving a driver model from a driver preferences database, the driver model predicting driver action in a plurality of driving situations based on a safety margin defined in the driver preferences; and displaying a pictorial language via a human-machine interface in response to the output from the plurality of sensors and the driver model, wherein the pictorial language includes one or more of a noun, verb, first adverb, object classifier, and second adverb to construct a plan sentence. | 16. A method for displaying a pictorial language executed by a processing circuitry and comprising: receiving output from a plurality of sensors; receiving a driver model from a driver preferences database, the driver model predicting driver action in a plurality of driving situations based on a safety margin defined in the driver preferences; and displaying a pictorial language via a human-machine interface in response to the output from the plurality of sensors and the driver model, wherein the pictorial language includes one or more of a noun, verb, first adverb, object classifier, and second adverb to construct a plan sentence. 17. The method of claim 16 , further comprising: displaying the plan sentence; receiving a driver response sentence; determining, via the processing circuitry, if the driver sentence received does not match the plan sentence more than a predetermined number of times; changing the first adverb of the plan sentence in response to the driver sentence not matching the plan sentence more than the predetermined number of times; changing the noun of the plan sentence in response to the driver sentence not matching the plan sentence more than the predetermined number of times; and updating the driver model in response to one or more changes to the first adverb and the noun. | 0.573418 |
7,876,467 | 7 | 8 | 7. The system of claim 1 , wherein said indicating data comprises digital ink including a plurality of pen positions. | 7. The system of claim 1 , wherein said indicating data comprises digital ink including a plurality of pen positions. 8. The system of claim 7 , wherein said computer system is configured for interpreting said digital ink as a handwritten annotation of said document. | 0.951623 |
4,695,977 | 11 | 12 | 11. The method of claim 10 wherein said step of deactivating comprises the steps of: generating a deactivation signal; finding by said computer system's execution of said finite state machine program routine a fourth table within said present state's set of the tables associated with said deactivation signal; determining a fifth group of program instructions to be executed by said computer system from said fourth table; executing a fourth operation in said process by said computer system's execution of said first program instructions of said fifth group of program instructions; allowing the continuation of processing said deactivation signal by said computer system's execution of a second program instruction of said fifth group of program instructions; and purging said first script of program instructions by said computer system's execution of said finite state machine program routine upon the allowance of said continuation of processing of said deactivation signal. | 11. The method of claim 10 wherein said step of deactivating comprises the steps of: generating a deactivation signal; finding by said computer system's execution of said finite state machine program routine a fourth table within said present state's set of the tables associated with said deactivation signal; determining a fifth group of program instructions to be executed by said computer system from said fourth table; executing a fourth operation in said process by said computer system's execution of said first program instructions of said fifth group of program instructions; allowing the continuation of processing said deactivation signal by said computer system's execution of a second program instruction of said fifth group of program instructions; and purging said first script of program instructions by said computer system's execution of said finite state machine program routine upon the allowance of said continuation of processing of said deactivation signal. 12. The method of claim 11 wherein said step of purging comprises the steps of: identifiying each set of said identification tables by said computer system's execution of said finite state machine program routine; checking by said computer system's execution of said finite state machine program routine each table within the identified set of identification tables for the occurrence of a reference to a group of instructions of said first set of program scripts; and removing by said computer system's execution of said finite state machine program routine the reference of the identified group of instructions of said first set of program scripts from each of said identified set of tables. | 0.903401 |
7,650,348 | 18 | 20 | 18. The method of claim 14 , wherein the step of integrating comprises the steps of: determining whether any of the identified words occur in the existing word list; and assigning a resolved weighting to identified words that occur in the existing word list. | 18. The method of claim 14 , wherein the step of integrating comprises the steps of: determining whether any of the identified words occur in the existing word list; and assigning a resolved weighting to identified words that occur in the existing word list. 20. The method of claim 18 , wherein the resolved weighting is based on the weighting of the identified word and the predefined weighting of the identified word in the existing word list. | 0.918837 |
7,580,945 | 5 | 7 | 5. A method in a computing device for determining scores of documents with links between documents, the method comprising: generating transition probabilities of transitioning between pairs of source and target documents based on a determination of information available through each target document of a source document by factoring in information content of the target documents; calculating by the computing device scores of the documents based on the stationary probability of the generated transition probabilities; and storing the calculated scores of the documents wherein transition probabilities are generated according to the following: P ij ( N ) = d j ( N - 1 ) 1 { ( i , j ) ∈ l ( G ) } ∑ ( i , k ) ∈ l ( G ) d k ( N - 1 ) where P ij (N) represents the transition probability of transitioning from document i to document j based on a look-ahead distance of N−1 and d j (N−1) represents the count of links from document j that is a look-ahead distance of N−1 from document i. | 5. A method in a computing device for determining scores of documents with links between documents, the method comprising: generating transition probabilities of transitioning between pairs of source and target documents based on a determination of information available through each target document of a source document by factoring in information content of the target documents; calculating by the computing device scores of the documents based on the stationary probability of the generated transition probabilities; and storing the calculated scores of the documents wherein transition probabilities are generated according to the following: P ij ( N ) = d j ( N - 1 ) 1 { ( i , j ) ∈ l ( G ) } ∑ ( i , k ) ∈ l ( G ) d k ( N - 1 ) where P ij (N) represents the transition probability of transitioning from document i to document j based on a look-ahead distance of N−1 and d j (N−1) represents the count of links from document j that is a look-ahead distance of N−1 from document i. 7. The method of claim 5 wherein the transition probabilities are represented as a transition probability matrix and the calculating of the score is performed by identifying the principal eigenvector of the transition probability matrix. | 0.757669 |
9,621,649 | 1 | 3 | 1. A platform independent XML virtual machine that accepts application process code written in an XML programming language as input and implements a method that causes the application process code to be executed on a computing device, the method comprising: defining a set of runtime objects for executing an application including an instance object configured to couple operational meaning to the execution of the application process code; providing low-level constructs (“operation handlers”) configured to execute operations indicated by the application process code; and executing operation handlers of the application process code using a stateless process object and an instance object separate from the stateless process object; wherein the instance object is provided to the operation handlers to provide access to and persist changes to the application's runtime state while the stateless process object executes the operation handlers indicated by the application process code. | 1. A platform independent XML virtual machine that accepts application process code written in an XML programming language as input and implements a method that causes the application process code to be executed on a computing device, the method comprising: defining a set of runtime objects for executing an application including an instance object configured to couple operational meaning to the execution of the application process code; providing low-level constructs (“operation handlers”) configured to execute operations indicated by the application process code; and executing operation handlers of the application process code using a stateless process object and an instance object separate from the stateless process object; wherein the instance object is provided to the operation handlers to provide access to and persist changes to the application's runtime state while the stateless process object executes the operation handlers indicated by the application process code. 3. The XML virtual machine as recited in claim 1 , wherein executing operation handlers of the application process code using a stateless process object and an instance object includes providing a trigger event bus configured to supply the instance object to the stateless process object for use during execution of the operation handlers. | 0.563144 |
10,140,991 | 1 | 6 | 1. A method performed by one or more computers, the method comprising: receiving, by the one or more computers, a request from a client device for media content, the request including at least a portion of a first media item or a URL corresponding to the first media item, the first media item including speech of a person; based on the data indicating the first media item, selecting, by the one or more computers, one or more other media items based on one or more representations of acoustic characteristics of the one or more other media items, wherein the one or more representations of acoustic characteristics of the one or more other media items comprise, for each of the one or more other media items, a speaker representation that includes (i) an i-vector or d-vector generated from the other media item, or (ii) a hash of an i-vector or d-vector generated from the other media item; wherein each of the one or more other media items is selected based on a comparison of (i) an i-vector, d-vector or hash determined from speech in the first media item with (ii) the speaker representation for the other media item, wherein: each of the selected one or more other media items is different from the first media item; each of the selected one or more other media items includes speech of the same person whose speech is included in the first media item; and each of the selected one or more other media items is determined, based on the acoustic characteristics of the media item, to include speech demonstrating speaker characteristics that have at least a threshold level of similarity with speaker characteristics determined from speech in the first media item; generating, by the one or more computers, data indicating the selected one or more other media items that are each different from the first media item and that each include speech of the same person whose speech is included in the first media item; and providing, by the one or more computers and to the client device, a response to the request that includes the data indicating the selected one or more other media items that are each different from the first media item and that each include speech of the same person whose speech is included in the first media item. | 1. A method performed by one or more computers, the method comprising: receiving, by the one or more computers, a request from a client device for media content, the request including at least a portion of a first media item or a URL corresponding to the first media item, the first media item including speech of a person; based on the data indicating the first media item, selecting, by the one or more computers, one or more other media items based on one or more representations of acoustic characteristics of the one or more other media items, wherein the one or more representations of acoustic characteristics of the one or more other media items comprise, for each of the one or more other media items, a speaker representation that includes (i) an i-vector or d-vector generated from the other media item, or (ii) a hash of an i-vector or d-vector generated from the other media item; wherein each of the one or more other media items is selected based on a comparison of (i) an i-vector, d-vector or hash determined from speech in the first media item with (ii) the speaker representation for the other media item, wherein: each of the selected one or more other media items is different from the first media item; each of the selected one or more other media items includes speech of the same person whose speech is included in the first media item; and each of the selected one or more other media items is determined, based on the acoustic characteristics of the media item, to include speech demonstrating speaker characteristics that have at least a threshold level of similarity with speaker characteristics determined from speech in the first media item; generating, by the one or more computers, data indicating the selected one or more other media items that are each different from the first media item and that each include speech of the same person whose speech is included in the first media item; and providing, by the one or more computers and to the client device, a response to the request that includes the data indicating the selected one or more other media items that are each different from the first media item and that each include speech of the same person whose speech is included in the first media item. 6. The method of claim 1 , wherein generating the data indicating the selected one or more other media items comprises generating one or more search results that each include a link to a media item that is available on the Internet and that includes speech of the person. | 0.723469 |
9,338,121 | 13 | 16 | 13. A method for sending an electronic communication within a message campaign to a plurality of recipients, the method comprising: receiving, at a server, a recipient list based upon one or more selected attributes of potential recipients within a predetermined context assigned to a first user, wherein an attribute of the one or more selected attributes comprises an online social influence rating, wherein the predetermined context is selected from a plurality of contexts stored in a database, wherein each context stored in the database comprises a set of attributes of potential recipients made available to one or more users, wherein the recipient list is selected based upon a random sample from the potential recipients within the predetermined context that have the online social influence rating, and wherein the first user is restricted from viewing attributes of potential recipients outside the predetermined context; generating electronic messages within a message campaign; sending the electronic messages to recipients on the recipient list; in response to sending the electronic messages to the recipients on the recipient list, generating a new context comprising a new set of attributes, the new set of attributes comprising the attributes of the predetermined context other than the selected one or more attributes; and automatically assigning the new context to the first user, wherein the new context restricts the first user from accessing attributes of potential recipients other than the new set of attributes, wherein the new set of attributes comprises at least one attribute linked to a particular potential recipient, and wherein substantially all attributes of the new set of attributes that are linked to the particular potential recipient maintain the privacy of the particular potential recipient. | 13. A method for sending an electronic communication within a message campaign to a plurality of recipients, the method comprising: receiving, at a server, a recipient list based upon one or more selected attributes of potential recipients within a predetermined context assigned to a first user, wherein an attribute of the one or more selected attributes comprises an online social influence rating, wherein the predetermined context is selected from a plurality of contexts stored in a database, wherein each context stored in the database comprises a set of attributes of potential recipients made available to one or more users, wherein the recipient list is selected based upon a random sample from the potential recipients within the predetermined context that have the online social influence rating, and wherein the first user is restricted from viewing attributes of potential recipients outside the predetermined context; generating electronic messages within a message campaign; sending the electronic messages to recipients on the recipient list; in response to sending the electronic messages to the recipients on the recipient list, generating a new context comprising a new set of attributes, the new set of attributes comprising the attributes of the predetermined context other than the selected one or more attributes; and automatically assigning the new context to the first user, wherein the new context restricts the first user from accessing attributes of potential recipients other than the new set of attributes, wherein the new set of attributes comprises at least one attribute linked to a particular potential recipient, and wherein substantially all attributes of the new set of attributes that are linked to the particular potential recipient maintain the privacy of the particular potential recipient. 16. The method of claim 13 , wherein the one or more attributes comprise an attribute of a social media communication. | 0.907668 |
8,667,412 | 7 | 8 | 7. The method of claim 1 , wherein the key mapping includes a plurality of characters associated with an alphabet corresponding to the language identifier. | 7. The method of claim 1 , wherein the key mapping includes a plurality of characters associated with an alphabet corresponding to the language identifier. 8. The method of claim 7 , further comprising: receiving user input selecting a control included in the virtual input device; and entering a character associated with the selected control in the selected data entry field. | 0.936385 |
9,607,101 | 6 | 7 | 6. The medium of claim 5 , the instructions causing additional operations comprising: performing a second search using a second search query generated using the updated first tokenized search suggestion; displaying results of the second search; and generating additional search suggestions from an additional text input received at the text input field. | 6. The medium of claim 5 , the instructions causing additional operations comprising: performing a second search using a second search query generated using the updated first tokenized search suggestion; displaying results of the second search; and generating additional search suggestions from an additional text input received at the text input field. 7. The medium of claim 6 , the instructions causing additional operations comprising: receiving a selection of a second selected suggestion from the additional search suggestions; generating a second tokenized search suggestion using the second selected suggestion and a second default suggestion scope; and generating a third search query using the updated first tokenized search suggestion and the second tokenized search suggestion. | 0.849168 |
9,773,052 | 11 | 13 | 11. A system comprising: one or more processors; and a non-transitory computer-readable medium including instructions that, when executed by the one or more processors, cause the one or more processors to perform operations including: receive a request for a snapshot of a document at a previous point in time; identify events associated with the document prior to the previous point in time, wherein the events include every event associated with a change in the document prior to the previous point in time, and wherein an event of the events includes a deletion of at least a portion of the document, wherein each event of the events is separately stored in an archive, wherein the archive receives a first set of one or more events from a first communication modality and a second set of one or more events from a second communication modality, and wherein the archive processes each of the first set of one or more events and each of the second set of one or more events to a common format; and generate the snapshot by performing each event of the events, wherein the snapshot represents the document at the previous point in time. | 11. A system comprising: one or more processors; and a non-transitory computer-readable medium including instructions that, when executed by the one or more processors, cause the one or more processors to perform operations including: receive a request for a snapshot of a document at a previous point in time; identify events associated with the document prior to the previous point in time, wherein the events include every event associated with a change in the document prior to the previous point in time, and wherein an event of the events includes a deletion of at least a portion of the document, wherein each event of the events is separately stored in an archive, wherein the archive receives a first set of one or more events from a first communication modality and a second set of one or more events from a second communication modality, and wherein the archive processes each of the first set of one or more events and each of the second set of one or more events to a common format; and generate the snapshot by performing each event of the events, wherein the snapshot represents the document at the previous point in time. 13. The system of claim 11 , wherein the first communication modality includes electronic mail, instant messaging, chat, web collaboration, video conferencing, voice telephone, social networks, or any combination thereof. | 0.502252 |
8,700,593 | 18 | 24 | 18. A search system for determining whether an input string of characters matches a regular expression comprising a number of sub-expressions, the search system comprising: a result pipeline extending through the search system; a deterministic finite automaton (DFA) engine to store first sub-expressions that comprise strings, the DFA engine having an input to receive the input string of characters from a data pipeline, and having an output to provide a first token onto the result pipeline in response to a match with one of the first sub-expressions; a non-deterministic finite automaton (NFA) engine to store second sub-expressions having selected quantified character classes, the NFA engine having an input to selectively receive the input string of characters from the data pipeline, and having an output to provide a second token onto the result pipeline in response to a match with one of the second sub-expressions; and a token stitcher, having an input to receive the tokens from the result pipeline, to combine the tokens to generate a latch signal indicating whether the input string of characters matches the regular expression, wherein the regular expression comprises an unbounded sub-expression, and the unbounded sub-expression is delegated to the token stitcher for processing without utilizing resources of the DFA or NFA engine and wherein the DFA and NFA engines and the token stitcher are implemented by at least one processor-based computing device. | 18. A search system for determining whether an input string of characters matches a regular expression comprising a number of sub-expressions, the search system comprising: a result pipeline extending through the search system; a deterministic finite automaton (DFA) engine to store first sub-expressions that comprise strings, the DFA engine having an input to receive the input string of characters from a data pipeline, and having an output to provide a first token onto the result pipeline in response to a match with one of the first sub-expressions; a non-deterministic finite automaton (NFA) engine to store second sub-expressions having selected quantified character classes, the NFA engine having an input to selectively receive the input string of characters from the data pipeline, and having an output to provide a second token onto the result pipeline in response to a match with one of the second sub-expressions; and a token stitcher, having an input to receive the tokens from the result pipeline, to combine the tokens to generate a latch signal indicating whether the input string of characters matches the regular expression, wherein the regular expression comprises an unbounded sub-expression, and the unbounded sub-expression is delegated to the token stitcher for processing without utilizing resources of the DFA or NFA engine and wherein the DFA and NFA engines and the token stitcher are implemented by at least one processor-based computing device. 24. The search system of claim 18 , wherein the DFA engine is configured to provide a match result onto the result pipeline if the first sub-expression is matched by the input string of characters and the regular expression consists of the first sub-expression. | 0.748555 |
9,552,281 | 12 | 18 | 12. A system, comprising: an interface to a data store storing at least one script file; and a processor, communicating with the data store via the interface, the processor being configured to— access, via a test environment, a plurality of script files, in a plurality of scripting languages, coded to perform a set of test operations, wherein the plurality of script files are accessed concurrently; invoke a set of object-oriented handlers based on the set of test operations contained in the plurality of script files, wherein invoking the set of object-oriented handlers comprises: loading each scripting language of the plurality of scripting languages; interfacing an object-oriented handler of the set of object-oriented handlers into each scripting language of the plurality of scripting languages, wherein interfacing the object-oriented handler of the set of object-oriented handlers into each scripting language of the plurality of scripting languages comprises creating a variable in the scripting language and loading a class from the object-oriented handler; bringing each object-oriented handler of the set of object-oriented handlers in scope; establishing a handler object context for each object-oriented handler of the set of object-oriented handlers; and loading classes required by one or more of the set of object-oriented handlers; initiate the set of test operations using the set of object-oriented handlers; determine that the set of test operations are complete; and bring each object-oriented handler of the set of object-oriented handlers out of scope by suspending each object-oriented handler in response to determining that the set of test operations are complete. | 12. A system, comprising: an interface to a data store storing at least one script file; and a processor, communicating with the data store via the interface, the processor being configured to— access, via a test environment, a plurality of script files, in a plurality of scripting languages, coded to perform a set of test operations, wherein the plurality of script files are accessed concurrently; invoke a set of object-oriented handlers based on the set of test operations contained in the plurality of script files, wherein invoking the set of object-oriented handlers comprises: loading each scripting language of the plurality of scripting languages; interfacing an object-oriented handler of the set of object-oriented handlers into each scripting language of the plurality of scripting languages, wherein interfacing the object-oriented handler of the set of object-oriented handlers into each scripting language of the plurality of scripting languages comprises creating a variable in the scripting language and loading a class from the object-oriented handler; bringing each object-oriented handler of the set of object-oriented handlers in scope; establishing a handler object context for each object-oriented handler of the set of object-oriented handlers; and loading classes required by one or more of the set of object-oriented handlers; initiate the set of test operations using the set of object-oriented handlers; determine that the set of test operations are complete; and bring each object-oriented handler of the set of object-oriented handlers out of scope by suspending each object-oriented handler in response to determining that the set of test operations are complete. 18. The system of claim 12 , wherein the set of object-oriented handlers comprises a set of Java® programming language-based handler objects. | 0.585294 |
8,060,820 | 13 | 17 | 13. A computer program stored on a computer readable storage medium, comprising: program means for copying, using a first application, a portion less than the whole document for real-time review at the first and second clients; program means for formatting, using the first computer application, the copied portion for real-time review; program means for providing the copied portion to an application for processing data of a second type at the first client, the application being an instant messaging application, and wherein the providing the copied portion to the second computer application is initiated through a menu component of the first computer application; program means for identifying a second client for performing the real-time review of the copied portion; program means for sending the copied portion via the instant messaging application to the identified second client; program means for receiving comments about the copied portion back from the identified second client, the comments conforming to the instant messaging application's protocol and wherein the receiving comments back from the identified second client comprises receiving text suggested as a replacement for the copied portion; and program means for interpreting the comments about the copied portion. | 13. A computer program stored on a computer readable storage medium, comprising: program means for copying, using a first application, a portion less than the whole document for real-time review at the first and second clients; program means for formatting, using the first computer application, the copied portion for real-time review; program means for providing the copied portion to an application for processing data of a second type at the first client, the application being an instant messaging application, and wherein the providing the copied portion to the second computer application is initiated through a menu component of the first computer application; program means for identifying a second client for performing the real-time review of the copied portion; program means for sending the copied portion via the instant messaging application to the identified second client; program means for receiving comments about the copied portion back from the identified second client, the comments conforming to the instant messaging application's protocol and wherein the receiving comments back from the identified second client comprises receiving text suggested as a replacement for the copied portion; and program means for interpreting the comments about the copied portion. 17. The computer program according to claim 13 , wherein the program means for interpreting the comments comprises: program means for parsing the comments to identify the suggested text; program means for extracting the suggested text; and program means for replacing the copied portion with the suggested text. | 0.659737 |
9,055,148 | 11 | 15 | 11. The method of claim 9 , further comprising: after said anchor is built, using positive hits generated during anchor building in connection with edit-distance to obtain a temporary categorization of a team/department. | 11. The method of claim 9 , further comprising: after said anchor is built, using positive hits generated during anchor building in connection with edit-distance to obtain a temporary categorization of a team/department. 15. The method of claim 11 , further comprising: after said anchor is built, categorizing said chat data into said team/department names. | 0.942145 |
7,533,013 | 15 | 18 | 15. A computer-implemented machine translation decoding method comprising: receiving as input a text segment in a source language to be translated into a target language; generating an initial translation by the computer as an initial current target language translation; estimating a probability of correctness of the initial translation by the computer, the probability based on alignment links between words and phrases in the source language and words and phrases in the target language; applying one or more modification operators by the computer to the initial current target language translation to generate one or more modified target language translations; estimating a probability of correctness of the one or more modified target language translations, the probability based on alignment links between words and phrases in the source language and words and phrases in the target language; determining by the computer whether one or more of the modified target language translations represents an improved translation in comparison with the initial current target language translation by comparing the estimated probability of correctness of the initial translation with the estimated probability of correctness of the one or more modified target language translations; iteratively modifying the current target language translation of the source language text segment based on the determination; and repeating said applying, said determining and said iteratively modifying until occurrence of a termination condition. | 15. A computer-implemented machine translation decoding method comprising: receiving as input a text segment in a source language to be translated into a target language; generating an initial translation by the computer as an initial current target language translation; estimating a probability of correctness of the initial translation by the computer, the probability based on alignment links between words and phrases in the source language and words and phrases in the target language; applying one or more modification operators by the computer to the initial current target language translation to generate one or more modified target language translations; estimating a probability of correctness of the one or more modified target language translations, the probability based on alignment links between words and phrases in the source language and words and phrases in the target language; determining by the computer whether one or more of the modified target language translations represents an improved translation in comparison with the initial current target language translation by comparing the estimated probability of correctness of the initial translation with the estimated probability of correctness of the one or more modified target language translations; iteratively modifying the current target language translation of the source language text segment based on the determination; and repeating said applying, said determining and said iteratively modifying until occurrence of a termination condition. 18. The method of claim 15 wherein the source language text segment comprises a clause, a sentence, a paragraph, or a treatise. | 0.960681 |
9,659,045 | 26 | 27 | 26. The one or more non-transitory storage media of claim 16 , wherein the sequences of instructions include instructions, that, when executed by said one or more computing devices, cause: storing said plurality of hierarchical data objects in a column of a certain database table of a DBMS; receiving a DDL statement requesting to create an index on said column; in response to receiving said DDL statement: creating said hierarchy-value index; and modifying said hierarchy-value index in response to database statements specifying modification to said column. | 26. The one or more non-transitory storage media of claim 16 , wherein the sequences of instructions include instructions, that, when executed by said one or more computing devices, cause: storing said plurality of hierarchical data objects in a column of a certain database table of a DBMS; receiving a DDL statement requesting to create an index on said column; in response to receiving said DDL statement: creating said hierarchy-value index; and modifying said hierarchy-value index in response to database statements specifying modification to said column. 27. The one or more non-transitory storage media of claim 26 , wherein said hierarchy-value index includes a particular database table that includes: a column that holds tokens; and a binary object column that stores posting lists. | 0.929573 |
7,694,222 | 1 | 2 | 1. A method of providing document specific instructions during a document's composition, the method executing on a computer system, the method comprising: obtaining, at the computer system, a document type indication of a document; providing an interface element to select one of a plurality of document sub-types, including at least three of an document informative sub-type, a persuasive sub-type, a compare and contrast sub-type, and a creative sub-type; obtaining, at the computer system, a document sub-type selection in accordance with said interface element; providing a document specific preparation guide in accordance with said document type indication and said document sub-type selection; providing a document specific documentation guide in accordance with said document type indication and said document sub-type selection; providing a document specific grammar guide in accordance with said document type indication and said document sub-type selection; obtaining, at the computer system, a user response to at least one of said provided guides; modifying said document in accordance with said user response; obtaining, at the computer system, a citation format type indication of the document; storing in a memory of the computer system a plurality of structured bibliographic sources; obtaining, at the computer system, a selection of at least one from said plurality of bibliographic sources; obtaining, at the computer system, a location reference within said selected bibliographic source; and using a processor, inserting into the document, in accordance with said citation format type indication, a citation associated with said selected bibliographic source and said location reference. | 1. A method of providing document specific instructions during a document's composition, the method executing on a computer system, the method comprising: obtaining, at the computer system, a document type indication of a document; providing an interface element to select one of a plurality of document sub-types, including at least three of an document informative sub-type, a persuasive sub-type, a compare and contrast sub-type, and a creative sub-type; obtaining, at the computer system, a document sub-type selection in accordance with said interface element; providing a document specific preparation guide in accordance with said document type indication and said document sub-type selection; providing a document specific documentation guide in accordance with said document type indication and said document sub-type selection; providing a document specific grammar guide in accordance with said document type indication and said document sub-type selection; obtaining, at the computer system, a user response to at least one of said provided guides; modifying said document in accordance with said user response; obtaining, at the computer system, a citation format type indication of the document; storing in a memory of the computer system a plurality of structured bibliographic sources; obtaining, at the computer system, a selection of at least one from said plurality of bibliographic sources; obtaining, at the computer system, a location reference within said selected bibliographic source; and using a processor, inserting into the document, in accordance with said citation format type indication, a citation associated with said selected bibliographic source and said location reference. 2. The method of claim 1 , wherein said preparation guide comprises research instructions. | 0.852459 |
8,719,176 | 1 | 4 | 1. A computer-implemented method, said computer-implemented method comprising the following: providing for receiving at a first computer, via a computer network, first information, said first information comprising at least (i) first user account information, (ii) first word or phrase information, or (iii) first approval or disapproval information; providing for storing at least some of said first information in a first database; providing for associating a first item with a first status, said first item comprising or relating to said first information; providing for associating a second item with said first status; providing for determining a first measure of community approval, said providing for determining said first measure of community approval comprising at least providing for counting a first plurality of submissions, said first plurality of submissions pertaining to said first item; providing for comparing said first measure of community approval to a second measure of community approval; providing for associating said first item with a second status, said second status being different from said first status, the associating said first item with said second status being performed at least partly according to an outcome of said providing for comparing said first measure of community approval to said second measure of community approval; providing for comparing a third measure of community approval to a fourth measure of community approval; providing for removing a third item from said second status, the removing said third item from said second status being performed at least partly according to an outcome of said providing for comparing said third measure of community approval to said fourth measure of community approval; and providing a plurality of real-time newsfeeds or tickers, said plurality of real-time newsfeeds or tickers comprising at least a first real-time newsfeed or ticker, said first real-time newsfeed or ticker comprising at least a first news item, said first news item being related to said first information. | 1. A computer-implemented method, said computer-implemented method comprising the following: providing for receiving at a first computer, via a computer network, first information, said first information comprising at least (i) first user account information, (ii) first word or phrase information, or (iii) first approval or disapproval information; providing for storing at least some of said first information in a first database; providing for associating a first item with a first status, said first item comprising or relating to said first information; providing for associating a second item with said first status; providing for determining a first measure of community approval, said providing for determining said first measure of community approval comprising at least providing for counting a first plurality of submissions, said first plurality of submissions pertaining to said first item; providing for comparing said first measure of community approval to a second measure of community approval; providing for associating said first item with a second status, said second status being different from said first status, the associating said first item with said second status being performed at least partly according to an outcome of said providing for comparing said first measure of community approval to said second measure of community approval; providing for comparing a third measure of community approval to a fourth measure of community approval; providing for removing a third item from said second status, the removing said third item from said second status being performed at least partly according to an outcome of said providing for comparing said third measure of community approval to said fourth measure of community approval; and providing a plurality of real-time newsfeeds or tickers, said plurality of real-time newsfeeds or tickers comprising at least a first real-time newsfeed or ticker, said first real-time newsfeed or ticker comprising at least a first news item, said first news item being related to said first information. 4. The method in claim 1 additionally comprising the following: providing a first search engine, said providing said first search engine comprising at least receiving a first query and providing at least a first plurality of URLs in response to said first query; and providing a first financial service, said first financial service being selected from the group consisting of: (i) processing of a first user-to-user payment; (ii) processing of a first user-to-user sale; (iii) processing of a first charitable donation or contribution; and (iv) indicating of a first financial condition of a user. | 0.615681 |
8,332,231 | 1 | 5 | 1. A computer-implemented method for operating an interactive response system comprising: receiving data representing a multi-utterance transaction with a person, the data having multiple elements, a subset of the elements including sensitive customer data; portioning the multi-utterance transaction into discrete, logical utterance units; automatically presenting the utterance units in perceptible form through a routing device and an analyst interface to each of a number of intent analysts, the utterance units being distributed so that no intent analyst is ever exposed to more than one of said subset of the elements including sensitive customer data; accepting intent input from each intent analyst through the respective analyst user interface, where the intent input characterizes the intent analyst's interpretation of the person's intent expressed in the utterance unit; and using a processor, automatically communicating a message to the person, in perceptible form and in substantially real time relative to the receiving step, the message being automatically selected from among a predetermined set of possible messages as a function of the intent input accepted from the intent analysts. | 1. A computer-implemented method for operating an interactive response system comprising: receiving data representing a multi-utterance transaction with a person, the data having multiple elements, a subset of the elements including sensitive customer data; portioning the multi-utterance transaction into discrete, logical utterance units; automatically presenting the utterance units in perceptible form through a routing device and an analyst interface to each of a number of intent analysts, the utterance units being distributed so that no intent analyst is ever exposed to more than one of said subset of the elements including sensitive customer data; accepting intent input from each intent analyst through the respective analyst user interface, where the intent input characterizes the intent analyst's interpretation of the person's intent expressed in the utterance unit; and using a processor, automatically communicating a message to the person, in perceptible form and in substantially real time relative to the receiving step, the message being automatically selected from among a predetermined set of possible messages as a function of the intent input accepted from the intent analysts. 5. The method of claim 1 , wherein the number of intent analysts is at least two. | 0.939732 |
9,886,703 | 19 | 21 | 19. A method performed by one or more computer systems coupled to a packet-based network, comprising: receiving an advertisement (ad) request from the packet-based network, the ad request including a location indicator, the location indicator having first location components; deriving one or more second location components from one or more of the first location components and by consulting information stored in a storage device accessible by the one or more computer systems; forming one or more location component groups each including a respective subset of one or more location components selected from the first location components and from the one or more second location components, the respective subset of one or more location components being determined to be consistent with each other; and generating a spatial representation of the location of the mobile device based on the ad request, the spatial representation including one or more probable geographical areas, each of the one or more probable geographical areas corresponding to a respective one of the one or more location component groups. | 19. A method performed by one or more computer systems coupled to a packet-based network, comprising: receiving an advertisement (ad) request from the packet-based network, the ad request including a location indicator, the location indicator having first location components; deriving one or more second location components from one or more of the first location components and by consulting information stored in a storage device accessible by the one or more computer systems; forming one or more location component groups each including a respective subset of one or more location components selected from the first location components and from the one or more second location components, the respective subset of one or more location components being determined to be consistent with each other; and generating a spatial representation of the location of the mobile device based on the ad request, the spatial representation including one or more probable geographical areas, each of the one or more probable geographical areas corresponding to a respective one of the one or more location component groups. 21. The method of claim 19 , wherein the ad request further includes context information including one or more of a time stamp, a user identifier, and a publisher name, the method further comprising determining a weight associated with a respective one of the one or more probable geographical areas using the context information. | 0.843898 |
8,489,628 | 52 | 53 | 52. A system comprising: one or more memory devices configured store executable instructions; and one or more processors configured to execute the stored instructions to cause the system to: receive a query from a user; identify a multiple word phrase in the query; identify a phrase extension of the identified phrase, wherein the phrase extension of the identified phrase is a sequence of words that begins with the identified phrase but is longer than the identified phrase, and wherein the identified phrase predicts the phrase extension based on a measure of an actual co-occurrence rate of the phrase extension and the identified phrase exceeding an expected co-occurrence rate of the phrase extension and the identified phrase in the document collection; and suggest the phrase extension to the user to use in the query. | 52. A system comprising: one or more memory devices configured store executable instructions; and one or more processors configured to execute the stored instructions to cause the system to: receive a query from a user; identify a multiple word phrase in the query; identify a phrase extension of the identified phrase, wherein the phrase extension of the identified phrase is a sequence of words that begins with the identified phrase but is longer than the identified phrase, and wherein the identified phrase predicts the phrase extension based on a measure of an actual co-occurrence rate of the phrase extension and the identified phrase exceeding an expected co-occurrence rate of the phrase extension and the identified phrase in the document collection; and suggest the phrase extension to the user to use in the query. 53. The system of claim 52 , wherein the identified phrase is an incomplete phrase, wherein phrases predicted by the incomplete phrase in the document collection include only phrase extensions of the incomplete phrase. | 0.865598 |
9,378,284 | 16 | 18 | 16. A portable device, comprising: a memory that stores executable instructions; and a processor coupled to the memory, wherein the processor, responsive to executing the instructions, facilitates performance of operations, comprising: receiving, prior to entering a screensaver mode of operation, a search criteria identifying a requested subject matter, wherein the search criteria is based on a last search query to a web-based search engine; entering the screens aver mode of operation; in response to entering the screensaver mode of operation, automatically transmitting to a search engine the search criteria identifying the requested subject matter to a web browser; receiving from the web browser a set of uniform resource locators in response to the transmitting of the search criteria identifying the requested subject matter; sequentially presenting image content referenced by uniform resource locators of the set of uniform resource locators during the screensaver mode of operation; presenting, during the screensaver mode, a user-selectable region comprising a selectable graphical element superimposed on a graphical interface for presenting a first image; causing a defined action responsive to selection of the selectable graphical element, wherein the defined action causes a generation of an e-mail message having an attachment with content referenced by a link associated with the first image; and ceasing, during the screensaver mode of operation, the presenting of the image content at the first image responsive to an input signal received via an input component of the portable device, wherein the first image is associated with a first uniform resource locator of the set of uniform resource locators. | 16. A portable device, comprising: a memory that stores executable instructions; and a processor coupled to the memory, wherein the processor, responsive to executing the instructions, facilitates performance of operations, comprising: receiving, prior to entering a screensaver mode of operation, a search criteria identifying a requested subject matter, wherein the search criteria is based on a last search query to a web-based search engine; entering the screens aver mode of operation; in response to entering the screensaver mode of operation, automatically transmitting to a search engine the search criteria identifying the requested subject matter to a web browser; receiving from the web browser a set of uniform resource locators in response to the transmitting of the search criteria identifying the requested subject matter; sequentially presenting image content referenced by uniform resource locators of the set of uniform resource locators during the screensaver mode of operation; presenting, during the screensaver mode, a user-selectable region comprising a selectable graphical element superimposed on a graphical interface for presenting a first image; causing a defined action responsive to selection of the selectable graphical element, wherein the defined action causes a generation of an e-mail message having an attachment with content referenced by a link associated with the first image; and ceasing, during the screensaver mode of operation, the presenting of the image content at the first image responsive to an input signal received via an input component of the portable device, wherein the first image is associated with a first uniform resource locator of the set of uniform resource locators. 18. The portable device of claim 16 , wherein the user-selectable region comprising a selectable graphical element comprises a preference interface associated with the screensaver mode. | 0.926354 |
7,788,254 | 1 | 4 | 1. A method of analyzing web pages, comprising: accessing a plurality of web pages; generating a plurality of different graphical representations of the web pages, each graphical representation having nodes that represent the web pages and links between the nodes, the nodes in each of the graphical representations representing a same set of the web pages as represented in other of the graphical representations, and the links in each graphical representation representing different relationships between the nodes in each graphical representation from the other graphical representations; generating a model that models a random walk through all of the different graphical representations; receiving training pages, wherein each of a plurality of training nodes, in the graphical representations, corresponding to a training page has a target function value indicative of a label for the corresponding training page, the label indicating the corresponding training page belongs to one of a plurality of different groups; generating a classifier based on the model, based on classifier function values of nodes in the graphical representations, and based on the target function values of the training pages; and grouping the web pages into groups with the classifier. | 1. A method of analyzing web pages, comprising: accessing a plurality of web pages; generating a plurality of different graphical representations of the web pages, each graphical representation having nodes that represent the web pages and links between the nodes, the nodes in each of the graphical representations representing a same set of the web pages as represented in other of the graphical representations, and the links in each graphical representation representing different relationships between the nodes in each graphical representation from the other graphical representations; generating a model that models a random walk through all of the different graphical representations; receiving training pages, wherein each of a plurality of training nodes, in the graphical representations, corresponding to a training page has a target function value indicative of a label for the corresponding training page, the label indicating the corresponding training page belongs to one of a plurality of different groups; generating a classifier based on the model, based on classifier function values of nodes in the graphical representations, and based on the target function values of the training pages; and grouping the web pages into groups with the classifier. 4. The method of claim 1 wherein generating a plurality of graphical representations comprises: generating a first graphical representation having the links between the nodes being representative of hyperlinks between the web pages. | 0.609428 |
4,051,459 | 7 | 10 | 7. In combination in an automated text recording and editing system, keyboard means for generating serial encoded characters, text storing means, stored program controlled central processor means for assembling said serial characters into plural character records, means for storing said assembled records in said text storing means, memory means coupled to said processor means, said memory means including a first program storing portion and a second variable storing portion, said second memory portion being of read-write construction, edit command storing means, means for storing batched edit commands serially entered at said keyboard means in said edit storing means, first compiler means for marking said character records in said text storing means in accordance with the contents of said edit command storing means, and second compiler means for producing plural revised character records in accordance with and responsive to the stored contents of said marked character records in said text storing means and the stored contents of said edit command storing means. | 7. In combination in an automated text recording and editing system, keyboard means for generating serial encoded characters, text storing means, stored program controlled central processor means for assembling said serial characters into plural character records, means for storing said assembled records in said text storing means, memory means coupled to said processor means, said memory means including a first program storing portion and a second variable storing portion, said second memory portion being of read-write construction, edit command storing means, means for storing batched edit commands serially entered at said keyboard means in said edit storing means, first compiler means for marking said character records in said text storing means in accordance with the contents of said edit command storing means, and second compiler means for producing plural revised character records in accordance with and responsive to the stored contents of said marked character records in said text storing means and the stored contents of said edit command storing means. 10. A combination as in claim 7, wherein said text storing means comprise parallel tracks of a ferromagnetic tape. | 0.957871 |
9,910,888 | 10 | 14 | 10. A system comprising: a memory; and a processing device, operatively coupled with the memory, to execute instructions, the processing device to: receive a map-reduce job written in a first map-reduce language, wherein the map-reduce job is to be performed in parallel on a plurality of nodes of a plurality of clusters, wherein the first map-reduce language is a general map-reduce language that describes functions supported by multiple map-reduce frameworks but is not specific to any of the multiple map-reduce frameworks; select one or more clusters from the plurality of clusters to run the map-reduce job, wherein the selected one or more clusters of the plurality of clusters operate a different map-reduce framework from other clusters of the plurality of clusters; identify a second map-reduce language associated with the selected one or more clusters; convert the first map-reduce language of the map-reduce job into the second map-reduce language; and cause the map-reduce job in the second map-reduce language to be run on the plurality of nodes of the selected one or more clusters. | 10. A system comprising: a memory; and a processing device, operatively coupled with the memory, to execute instructions, the processing device to: receive a map-reduce job written in a first map-reduce language, wherein the map-reduce job is to be performed in parallel on a plurality of nodes of a plurality of clusters, wherein the first map-reduce language is a general map-reduce language that describes functions supported by multiple map-reduce frameworks but is not specific to any of the multiple map-reduce frameworks; select one or more clusters from the plurality of clusters to run the map-reduce job, wherein the selected one or more clusters of the plurality of clusters operate a different map-reduce framework from other clusters of the plurality of clusters; identify a second map-reduce language associated with the selected one or more clusters; convert the first map-reduce language of the map-reduce job into the second map-reduce language; and cause the map-reduce job in the second map-reduce language to be run on the plurality of nodes of the selected one or more clusters. 14. The system of claim 10 , wherein to select the one or more clusters from the plurality of clusters to run the map-reduce job, the processing device further to: cause data to be processed by the map-reduce job to be available to the selected one or more clusters. | 0.671605 |
8,977,629 | 1 | 4 | 1. A method comprising: accessing an image that corresponds to a description of an item depicted in the image; determining an image quality score of the image that depicts the item and corresponds to the description of the item, the determining of the image quality score of the image including segmenting the image to identify a foreground of the image and a background of the image and determining a brightness difference between the segmented background of the image and the segmented foreground of the image, the determined image quality score representing a degree of clarity with which the image shows the item in the segmented foreground of the image, the determining of the image quality score being performed by a processor of a machine based on an analysis of the image that depicts the item; receiving a request for search results of which at least some pertain to the description of the item depicted in the image; and presenting a search result that is referential of the image of the item based on the image quality score of the image and in response to the request for the search results of which at least some pertain to the description of the item depicted in the image, the presenting of the search result being performed by a processor of a machine. | 1. A method comprising: accessing an image that corresponds to a description of an item depicted in the image; determining an image quality score of the image that depicts the item and corresponds to the description of the item, the determining of the image quality score of the image including segmenting the image to identify a foreground of the image and a background of the image and determining a brightness difference between the segmented background of the image and the segmented foreground of the image, the determined image quality score representing a degree of clarity with which the image shows the item in the segmented foreground of the image, the determining of the image quality score being performed by a processor of a machine based on an analysis of the image that depicts the item; receiving a request for search results of which at least some pertain to the description of the item depicted in the image; and presenting a search result that is referential of the image of the item based on the image quality score of the image and in response to the request for the search results of which at least some pertain to the description of the item depicted in the image, the presenting of the search result being performed by a processor of a machine. 4. The method of claim 1 , wherein: the determining of the image quality score based on the analysis of the image includes quantizing color pixel values from the foreground of the image that depicts the item into a color histogram. | 0.85324 |
7,516,126 | 1 | 3 | 1. A computer-implemented method utilizing a processor comprising: grouping single fields of a multiple-field source in a computer memory into a plurality of multiple-field keys (MFKs) of a search target, each MFK of the search target having single fields that correspond to single fields in one of a plurality of multiple-field vectors (MFVs) of entries in a data structure; utilizing the processor to generate a set of queries based, at least in part, on the MFKs, wherein each query includes one or more of the MFKs and wherein each query has a different MFK as a lead MFK; using a query to determine whether the non-wildcard values in the MFVs of an entry match the non-wildcard values in corresponding MFKs of the search target; and: if an entry has non-wildcard values in the MFVs that match the corresponding non-wildcard values in the MFKs, then performing an operation associated with the matching entry; or if no entry has non-wildcard values in the MFVs that match the corresponding non-wildcard values in the MFKs, then using the queries to determine whether the entry has non-wildcard values in a MFV that match the non-wildcard values in a corresponding lead MFK, and whether remaining MFVs of the entry match corresponding remaining MFKs based on matching the non-wildcard values and wildcard values. | 1. A computer-implemented method utilizing a processor comprising: grouping single fields of a multiple-field source in a computer memory into a plurality of multiple-field keys (MFKs) of a search target, each MFK of the search target having single fields that correspond to single fields in one of a plurality of multiple-field vectors (MFVs) of entries in a data structure; utilizing the processor to generate a set of queries based, at least in part, on the MFKs, wherein each query includes one or more of the MFKs and wherein each query has a different MFK as a lead MFK; using a query to determine whether the non-wildcard values in the MFVs of an entry match the non-wildcard values in corresponding MFKs of the search target; and: if an entry has non-wildcard values in the MFVs that match the corresponding non-wildcard values in the MFKs, then performing an operation associated with the matching entry; or if no entry has non-wildcard values in the MFVs that match the corresponding non-wildcard values in the MFKs, then using the queries to determine whether the entry has non-wildcard values in a MFV that match the non-wildcard values in a corresponding lead MFK, and whether remaining MFVs of the entry match corresponding remaining MFKs based on matching the non-wildcard values and wildcard values. 3. The method of claim 1 , further comprising arranging the entries of the data structure so that the MFVs that have non-wildcard values are placed at the end of the entry. | 0.847518 |
7,680,890 | 1 | 5 | 1. A computer-implemented method comprising: receiving a first classification value associated with an e-mail message that is an output of a first classification tool and a second classification value associated with said e-mail message that is an output of a second classification tool, said first classification value and said second classification value indicative of whether said e-mail message is spam; and generating a single, aggregated classification value for said e-mail message by combining said first classification value and said second classification value using a fuzzy logic-based voting mechanism, wherein said first classification value and said second classification value represent probabilities P 1 and P 2 , respectively, and said single, aggregated classification value represents a combined probability P combined , and wherein said fuzzy logic-based voting mechanism includes a voting formula comprising:
P combined =( P 1 ×P 2 )/(( P 1 ×P 2 )+(1− P 1 )(1− P 2 )). | 1. A computer-implemented method comprising: receiving a first classification value associated with an e-mail message that is an output of a first classification tool and a second classification value associated with said e-mail message that is an output of a second classification tool, said first classification value and said second classification value indicative of whether said e-mail message is spam; and generating a single, aggregated classification value for said e-mail message by combining said first classification value and said second classification value using a fuzzy logic-based voting mechanism, wherein said first classification value and said second classification value represent probabilities P 1 and P 2 , respectively, and said single, aggregated classification value represents a combined probability P combined , and wherein said fuzzy logic-based voting mechanism includes a voting formula comprising:
P combined =( P 1 ×P 2 )/(( P 1 ×P 2 )+(1− P 1 )(1− P 2 )). 5. The method of claim 1 , wherein said generating of said single, aggregated classification value further comprises tuning said first classification value and said second classification value based on historical effectiveness data. | 0.892791 |
8,707,172 | 8 | 9 | 8. The computer readable storage medium of claim 7 , wherein the target program includes a resource bundle annotation defining a name for a resource bundle to which the text string of the user interface element annotation may be extracted, wherein obtaining the displayed text string comprises: extracting the text string from the user interface element annotation by executing the internationalize method, and wherein the executable program code of the computer readable storage medium comprises: computer readable code configured as an annotation processor to process source code of the target program, including processing the user interface element annotation and resource bundle annotation, and create the resource bundle responsive to processing the user interface element annotation and resource bundle annotation. | 8. The computer readable storage medium of claim 7 , wherein the target program includes a resource bundle annotation defining a name for a resource bundle to which the text string of the user interface element annotation may be extracted, wherein obtaining the displayed text string comprises: extracting the text string from the user interface element annotation by executing the internationalize method, and wherein the executable program code of the computer readable storage medium comprises: computer readable code configured as an annotation processor to process source code of the target program, including processing the user interface element annotation and resource bundle annotation, and create the resource bundle responsive to processing the user interface element annotation and resource bundle annotation. 9. The computer readable storage medium of claim 8 , wherein the resource bundle annotation is configured in the non-executable annotation syntax and included with the resource bundle name therein, in the target computer program, and wherein processing the user interface element annotation and resource bundle annotation comprises: extracting the text string from the user interface element annotation, extracting the resource bundle name from the resource bundle annotation and storing the text string in computer readable storage media as a resource bundle having the extracted resource bundle name. | 0.803268 |
9,602,310 | 1 | 7 | 1. A message correlation system comprising: a message manager and processor that determine whether there are two or more outstanding queries to which a response from a user to any of the outstanding queries that includes no context would render the response uncorrelatable to one of the outstanding queries; and a keyword management module that correlates a keyword associated with a response to an outstanding query, wherein, when there is a conflict where the keyword is used in at least two of the two or more outstanding queries or the keyword is not used in the two or more outstanding queries, a supplemental query is sent to the user requesting clarification. | 1. A message correlation system comprising: a message manager and processor that determine whether there are two or more outstanding queries to which a response from a user to any of the outstanding queries that includes no context would render the response uncorrelatable to one of the outstanding queries; and a keyword management module that correlates a keyword associated with a response to an outstanding query, wherein, when there is a conflict where the keyword is used in at least two of the two or more outstanding queries or the keyword is not used in the two or more outstanding queries, a supplemental query is sent to the user requesting clarification. 7. The system of claim 1 , wherein the message manager further filters non-query based messages from context correlation mapping. | 0.708145 |
7,711,573 | 375 | 376 | 375. The method of claim 333 , further comprising: receiving the job description; storing the job description in the resume database; and sending a portion of the result set to a recruiter, wherein the result set includes at least one matching resume from the resume database, each said at least one matching resume satisfying the job description. | 375. The method of claim 333 , further comprising: receiving the job description; storing the job description in the resume database; and sending a portion of the result set to a recruiter, wherein the result set includes at least one matching resume from the resume database, each said at least one matching resume satisfying the job description. 376. The method of claim 375 , wherein the required term of experience is rounded up to a unit of time. | 0.979267 |
5,466,159 | 3 | 6 | 3. The system of claim 1 wherein the verification means further comprises collaboration means for providing an electronic communication link between the first and second test resolver terminals and, if the first and second test scores contain the discrepancy based on the predefined criteria, for signalling the first and second test resolvers to interactively determine and enter a joint score using the electronic communication link, the verification means further comprising recording means for recording the joint score. | 3. The system of claim 1 wherein the verification means further comprises collaboration means for providing an electronic communication link between the first and second test resolver terminals and, if the first and second test scores contain the discrepancy based on the predefined criteria, for signalling the first and second test resolvers to interactively determine and enter a joint score using the electronic communication link, the verification means further comprising recording means for recording the joint score. 6. The system of claim 3 wherein the verification means further comprises means for transmitting the test questions to a third test resolver terminal to be scored by a third test resolver, if the first and second test resolvers do not enter the joint score within a predetermined time period. | 0.837778 |
9,405,375 | 10 | 13 | 10. An apparatus comprising: at least one recording device configured to record a gesture object in a plurality of data objects over time; and a processor configured to: determine at least one set of gesture angles using the plurality of recorded data objects, wherein each of the gesture angles in the at least one set of gesture angles comprises an angle measurement between two positions of the gesture object, the two positions recorded in successive data objects of the plurality of recorded data objects, wherein the at least one set of gesture angles further comprises a first subset of gesture angles and a second subset of gesture angles; determine a first histogram representing a frequency of angles based on the first subset of gesture angles and a second histogram representing a frequency of angles based on the second subset of gesture angles; recognize a gesture based on comparing the first histogram and the second histogram to a respective first model histogram and second model histogram, each model histogram representing a frequency of angles of a subdivision of gestures of a gesture model; and modify a behavior of the device in response to the recognizing the gesture. | 10. An apparatus comprising: at least one recording device configured to record a gesture object in a plurality of data objects over time; and a processor configured to: determine at least one set of gesture angles using the plurality of recorded data objects, wherein each of the gesture angles in the at least one set of gesture angles comprises an angle measurement between two positions of the gesture object, the two positions recorded in successive data objects of the plurality of recorded data objects, wherein the at least one set of gesture angles further comprises a first subset of gesture angles and a second subset of gesture angles; determine a first histogram representing a frequency of angles based on the first subset of gesture angles and a second histogram representing a frequency of angles based on the second subset of gesture angles; recognize a gesture based on comparing the first histogram and the second histogram to a respective first model histogram and second model histogram, each model histogram representing a frequency of angles of a subdivision of gestures of a gesture model; and modify a behavior of the device in response to the recognizing the gesture. 13. The apparatus of claim 10 , wherein the at least one set of gesture angles further comprises a third subset of gesture angles, and wherein the third subset of gesture angles comprises a subdivision of the second subset of gesture angles. | 0.723624 |
9,990,386 | 31 | 42 | 31. One or more non-transitory computer-readable storage media, storing software instructions, which when executed by one or more processors cause performance of: creating two or more sets of field searchable, time stamped event records from raw data stored in at least one data store, wherein each set of field searchable, time stamped event records among the two or more sets of field searchable, time stamped event records comprises field searchable, time stamped event records having time stamps that fall within a time range, the time range different than time ranges associated with other sets of field searchable, time stamped event records among the two or more sets of field searchable, time stamped event records, wherein each field searchable, time stamped event record in the two or more sets of field searchable, time stamped event records includes a portion of the raw data and is associated with a time stamp derived from the raw data, wherein the raw data reflects activity in an information technology environment; generating a summarization table for each set of field searchable, time stamped event records in the two or more sets of field searchable, time stamped event records that: identifies one or more field values, wherein a field value comprises a value that appears in an associated field in one or more field searchable, time stamped event records in the set of field searchable, time stamped event records; and for each field value, includes a posting value to the one or more field searchable, time stamped event records in the set of field searchable, time stamped event records that contain the field value for the associated field; storing the summarization table for each set of field searchable, time stamped event records among the two or more sets of time stamped field searchable event records; selecting a stored summarization table based on a received query that includes search criteria for evaluating field values for one or more fields; using the search criteria to evaluate field values for one or more fields in the selected summarization table to generate a query result; and wherein the query result reflects an aspect of activity in the information technology environment. | 31. One or more non-transitory computer-readable storage media, storing software instructions, which when executed by one or more processors cause performance of: creating two or more sets of field searchable, time stamped event records from raw data stored in at least one data store, wherein each set of field searchable, time stamped event records among the two or more sets of field searchable, time stamped event records comprises field searchable, time stamped event records having time stamps that fall within a time range, the time range different than time ranges associated with other sets of field searchable, time stamped event records among the two or more sets of field searchable, time stamped event records, wherein each field searchable, time stamped event record in the two or more sets of field searchable, time stamped event records includes a portion of the raw data and is associated with a time stamp derived from the raw data, wherein the raw data reflects activity in an information technology environment; generating a summarization table for each set of field searchable, time stamped event records in the two or more sets of field searchable, time stamped event records that: identifies one or more field values, wherein a field value comprises a value that appears in an associated field in one or more field searchable, time stamped event records in the set of field searchable, time stamped event records; and for each field value, includes a posting value to the one or more field searchable, time stamped event records in the set of field searchable, time stamped event records that contain the field value for the associated field; storing the summarization table for each set of field searchable, time stamped event records among the two or more sets of time stamped field searchable event records; selecting a stored summarization table based on a received query that includes search criteria for evaluating field values for one or more fields; using the search criteria to evaluate field values for one or more fields in the selected summarization table to generate a query result; and wherein the query result reflects an aspect of activity in the information technology environment. 42. The one or more non-transitory computer-readable storage media of claim 31 , wherein the raw data includes log data. | 0.912281 |
9,395,907 | 1 | 9 | 1. An apparatus comprising at least one processor and at least one memory including computer program code, the at least one memory and the computer program code configured to, with the processor, cause the apparatus to: cause display of a) a content package comprising a first content segment from a first content source, and b) a plurality of other content packages each comprising respective displayed content segments from the first content source; receive an indication of a first gesture input in a positional relationship to the first content segment; identify a second content segment relating to the first content segment from a second content source; in response to the indication of the first gesture input, gradually adapt the content package, causing display of at least a portion of the second content segment; receive a second indication, wherein the second indication is a gradual extension of the first input gesture; and in response to the second indication, cause the content package to be further gradually adapted to reveal at least an additional portion of the second content segment, wherein the gradual adaption of the content package is performed simultaneously and proportionately gradually to the gradual extension of the first input gesture such that the first content segment from the first content source is gradually hidden and the second content segment from the second content source is gradually further revealed from beneath the first content segment, and while the gradual adaptation of the content package is performed, the respective displayed content segments from the first content source remain displayed. | 1. An apparatus comprising at least one processor and at least one memory including computer program code, the at least one memory and the computer program code configured to, with the processor, cause the apparatus to: cause display of a) a content package comprising a first content segment from a first content source, and b) a plurality of other content packages each comprising respective displayed content segments from the first content source; receive an indication of a first gesture input in a positional relationship to the first content segment; identify a second content segment relating to the first content segment from a second content source; in response to the indication of the first gesture input, gradually adapt the content package, causing display of at least a portion of the second content segment; receive a second indication, wherein the second indication is a gradual extension of the first input gesture; and in response to the second indication, cause the content package to be further gradually adapted to reveal at least an additional portion of the second content segment, wherein the gradual adaption of the content package is performed simultaneously and proportionately gradually to the gradual extension of the first input gesture such that the first content segment from the first content source is gradually hidden and the second content segment from the second content source is gradually further revealed from beneath the first content segment, and while the gradual adaptation of the content package is performed, the respective displayed content segments from the first content source remain displayed. 9. The apparatus of claim 1 , wherein the second content source is identified based on an update time. | 0.909414 |
9,058,382 | 16 | 19 | 16. A computer system for generating a feature from a hierarchy of documents, comprising: a memory storing computer-executable instructions for: accessing a hierarchical organization of the documents, the hierarchical organization specifying parent/child relations between documents, one of the documents being a root document of the hierarchical organization that has no parent document, some of the documents of the hierarchical organization being leaf documents that have no child documents, each document other than the root document and the leaf documents having both a parent document and a child document, the parent/child relations occurring when a parent document contains a reference to a child document; generating a feature for each of the documents in the hierarchical organization of documents; and for each document, generating an aggregate feature from the generated features of the documents to represent the feature for the document by combining the generated feature for that document with the generated aggregate features for child documents of that document to generate an aggregate feature for that document by summing the features for child documents of that document, dividing that sum by the number of child documents to generate a quotient, and multiply the quotient by a weighting factor so that each document in the hierarchical organization with a child document has an aggregate feature derived from the generated feature for that document and the aggregate features of the child documents of that document; and a processor for executing the computer-executable instructions stored in the memory. | 16. A computer system for generating a feature from a hierarchy of documents, comprising: a memory storing computer-executable instructions for: accessing a hierarchical organization of the documents, the hierarchical organization specifying parent/child relations between documents, one of the documents being a root document of the hierarchical organization that has no parent document, some of the documents of the hierarchical organization being leaf documents that have no child documents, each document other than the root document and the leaf documents having both a parent document and a child document, the parent/child relations occurring when a parent document contains a reference to a child document; generating a feature for each of the documents in the hierarchical organization of documents; and for each document, generating an aggregate feature from the generated features of the documents to represent the feature for the document by combining the generated feature for that document with the generated aggregate features for child documents of that document to generate an aggregate feature for that document by summing the features for child documents of that document, dividing that sum by the number of child documents to generate a quotient, and multiply the quotient by a weighting factor so that each document in the hierarchical organization with a child document has an aggregate feature derived from the generated feature for that document and the aggregate features of the child documents of that document; and a processor for executing the computer-executable instructions stored in the memory. 19. The computer system of claim 16 wherein the documents are web pages. | 0.889571 |
7,774,341 | 1 | 10 | 1. A user-interface method of selecting and presenting a collection of content items in which the presentation is ordered at least in part based on learning the preferred microgenres of content of the user as contained in content items selected by the user, the method comprising: providing access to a content system including a set of content items organized by genre information that characterizes the content items, wherein the genre information is specified by the content system, and wherein the set of content items contains microgenre metadata further characterizing the content items; receiving incremental input entered by the user for incrementally identifying desired content items; in response to the incremental input entered by the user, presenting a subset of content items to the user; receiving actions from the user selecting content items from the subset; analyzing the microgenre metadata within the selected content items to learn the preferred microgenres of the user; analyzing the date, day, and time of the user selection actions and analyzing at least one of the genre information and microgenre metadata of the selected content items to learn a periodicity of user selections of similar content items, wherein similarity is determined by comparing the at least one of the genre information and microgenre metadata of the selected content item with a previously selected content item, and wherein the periodicity indicates the amount of time between user selections of similar content items relative to a reference point; and associating the learned periodicity with the at least one of the genre information and microgenre metadata of the similar content items; in response to receiving subsequent incremental input entered by the user, selecting and ranking a collection of content items, wherein content items containing microgenre metadata matching more learned microgenre preferences of the user relative to other microgenre preferences are ranked more highly than other content items of the collection containing microgenre metadata matching less learned microgenre preferences of the user relative to other microgenre preferences, and wherein the selecting and presenting the collection of content items is further based on promoting the relevance of those content items characterized by genre information or containing microgenre metadata associated with periodicities matching the date, day, and time of the subsequent incremental input; and presenting the ranked collection of content items on a display device in an order reflecting the ranking of the content items. | 1. A user-interface method of selecting and presenting a collection of content items in which the presentation is ordered at least in part based on learning the preferred microgenres of content of the user as contained in content items selected by the user, the method comprising: providing access to a content system including a set of content items organized by genre information that characterizes the content items, wherein the genre information is specified by the content system, and wherein the set of content items contains microgenre metadata further characterizing the content items; receiving incremental input entered by the user for incrementally identifying desired content items; in response to the incremental input entered by the user, presenting a subset of content items to the user; receiving actions from the user selecting content items from the subset; analyzing the microgenre metadata within the selected content items to learn the preferred microgenres of the user; analyzing the date, day, and time of the user selection actions and analyzing at least one of the genre information and microgenre metadata of the selected content items to learn a periodicity of user selections of similar content items, wherein similarity is determined by comparing the at least one of the genre information and microgenre metadata of the selected content item with a previously selected content item, and wherein the periodicity indicates the amount of time between user selections of similar content items relative to a reference point; and associating the learned periodicity with the at least one of the genre information and microgenre metadata of the similar content items; in response to receiving subsequent incremental input entered by the user, selecting and ranking a collection of content items, wherein content items containing microgenre metadata matching more learned microgenre preferences of the user relative to other microgenre preferences are ranked more highly than other content items of the collection containing microgenre metadata matching less learned microgenre preferences of the user relative to other microgenre preferences, and wherein the selecting and presenting the collection of content items is further based on promoting the relevance of those content items characterized by genre information or containing microgenre metadata associated with periodicities matching the date, day, and time of the subsequent incremental input; and presenting the ranked collection of content items on a display device in an order reflecting the ranking of the content items. 10. The method of claim 1 , further comprising presenting the ordered collection of content items on a display constrained device. | 0.913564 |
8,914,358 | 1 | 4 | 1. A computer-implemented method, comprising: obtaining, in a server device, a plurality of first search results responsive to a first search query, the plurality of first search results being ranked in an order; obtaining one or more second search results responsive to a second search query, the second search query being related to the first search query; identifying a particular search result of the second search results; determining that the particular search result is related to the first search query and that the particular search result is highly ranked for the second search query; determining that the particular search result is not within a threshold number of highest-ranked first search results of the first search results responsive to the first search query; in response to determining that the particular search result is highly ranked for the second search query and the particular search result is not within a threshold number of highest-ranked first search results of the first search results responsive to the first search query, modifying the first search results by providing the particular search result within the threshold number of highest-ranked first search results of the first search results responsive to the first search query; and providing the modified plurality of first search results in response to the first search query. | 1. A computer-implemented method, comprising: obtaining, in a server device, a plurality of first search results responsive to a first search query, the plurality of first search results being ranked in an order; obtaining one or more second search results responsive to a second search query, the second search query being related to the first search query; identifying a particular search result of the second search results; determining that the particular search result is related to the first search query and that the particular search result is highly ranked for the second search query; determining that the particular search result is not within a threshold number of highest-ranked first search results of the first search results responsive to the first search query; in response to determining that the particular search result is highly ranked for the second search query and the particular search result is not within a threshold number of highest-ranked first search results of the first search results responsive to the first search query, modifying the first search results by providing the particular search result within the threshold number of highest-ranked first search results of the first search results responsive to the first search query; and providing the modified plurality of first search results in response to the first search query. 4. The method of claim 1 , further comprising: determining the second search query is related to the first search query based on one or more shared terms between the first search query and the second search query. | 0.806364 |
9,183,302 | 11 | 14 | 11. A computer-readable medium for storing instructions, the instructions comprising: one or more instructions that, when executed by a processor of a device, cause the processor to: receive information, the information including one or more of: technical computing environment (TCE) model information associated with one or more TCE models, problem information associated with one or more problems, or TCE tool information associated with one or more TCE tools, execute the one or more TCE models and the one or more TCE tools to determine behavior information associated with the one or more TCE models and the one or more TCE tools, store the behavior information in a repository, receive a query including a plurality of query elements, process the plurality of query elements to determine a plurality of processed query elements, a processed query element, of the plurality of processed query elements, including an identifier associated with a respective query element of the plurality of query elements, and utilize the behavior information stored in the repository for generating a result based on the plurality of processed query elements. | 11. A computer-readable medium for storing instructions, the instructions comprising: one or more instructions that, when executed by a processor of a device, cause the processor to: receive information, the information including one or more of: technical computing environment (TCE) model information associated with one or more TCE models, problem information associated with one or more problems, or TCE tool information associated with one or more TCE tools, execute the one or more TCE models and the one or more TCE tools to determine behavior information associated with the one or more TCE models and the one or more TCE tools, store the behavior information in a repository, receive a query including a plurality of query elements, process the plurality of query elements to determine a plurality of processed query elements, a processed query element, of the plurality of processed query elements, including an identifier associated with a respective query element of the plurality of query elements, and utilize the behavior information stored in the repository for generating a result based on the plurality of processed query elements. 14. The computer-readable medium of claim 11 , where the instructions further comprise: one or more instructions that, when executed by the processor, cause the processor to: provide for display a user interface associated with problem information, receive the problem information via the user interface, and store the problem information in the repository. | 0.749649 |
9,300,682 | 13 | 17 | 13. A system for analyzing executable content within at least one network of an enterprise, comprising: a plurality of collection agents disposed within one or more networks of an enterprise and executable by one or more hardware processors of one or more devices within the one or more networks, wherein each collection agent is configured to detect a presence of multiple instances of executable content within the enterprise; and a central analysis server remotely disposed from and in operative communication with the plurality of collection agents via the one or more networks, the central analysis server comprising: a collection engine, executable by a hardware processor of the central analysis server, that is configured to capture and store the multiple instances of executable content received from the plurality of collection agents; an extraction engine, executable by the hardware processor of the central analysis server, that is configured to extract one or more characteristics from each instance of the executable content; an analysis engine, executable by the hardware processor of the central analysis server, that is configured to: identify associations among the extracted characteristics; determine, based on the associations among the extracted characteristics, that a first portion of executable content is associated with a non-trusted entity; obtain a hash value for the first portion of executable content; and store the hash value and the associated extracted characteristics to create a non-trusted entity profile; and a first database that is configured to store the extracted characteristics, identified associations, and hash value, the first database being accessible by the hardware processor of the central analysis server and the plurality of collection agents such that each of the plurality of collection agents is operable to identify at least another portion of executable content associated with the non-trusted entity based on the hash value that has been recognized and presented in the database, wherein each of the plurality of collection agents is operable to transmit to the central analysis server an indication of notice indicative of a detection of the non-trusted entity at the corresponding collection agent, the indication comprising the hash value, location information but not a copy of the at least another portion of executable content to limit use of enterprise infrastructure resources and so as to update the non-trusted entity profile. | 13. A system for analyzing executable content within at least one network of an enterprise, comprising: a plurality of collection agents disposed within one or more networks of an enterprise and executable by one or more hardware processors of one or more devices within the one or more networks, wherein each collection agent is configured to detect a presence of multiple instances of executable content within the enterprise; and a central analysis server remotely disposed from and in operative communication with the plurality of collection agents via the one or more networks, the central analysis server comprising: a collection engine, executable by a hardware processor of the central analysis server, that is configured to capture and store the multiple instances of executable content received from the plurality of collection agents; an extraction engine, executable by the hardware processor of the central analysis server, that is configured to extract one or more characteristics from each instance of the executable content; an analysis engine, executable by the hardware processor of the central analysis server, that is configured to: identify associations among the extracted characteristics; determine, based on the associations among the extracted characteristics, that a first portion of executable content is associated with a non-trusted entity; obtain a hash value for the first portion of executable content; and store the hash value and the associated extracted characteristics to create a non-trusted entity profile; and a first database that is configured to store the extracted characteristics, identified associations, and hash value, the first database being accessible by the hardware processor of the central analysis server and the plurality of collection agents such that each of the plurality of collection agents is operable to identify at least another portion of executable content associated with the non-trusted entity based on the hash value that has been recognized and presented in the database, wherein each of the plurality of collection agents is operable to transmit to the central analysis server an indication of notice indicative of a detection of the non-trusted entity at the corresponding collection agent, the indication comprising the hash value, location information but not a copy of the at least another portion of executable content to limit use of enterprise infrastructure resources and so as to update the non-trusted entity profile. 17. The system of claim 13 , wherein the extracted characteristics are selected from the group consisting of: an author mark, a tool mark, a behavior, a pattern, and a text sequence. | 0.927548 |
8,924,923 | 10 | 12 | 10. A method of generating a multi-level test case in an apparatus of generating a multi-level test case from a unified modeling language (UML) sequence diagram (SD) based on a multiple condition control flow graph (MCCFG), the method comprising the steps of: model-converting, by a model converting unit, the unified modeling language (UML) sequence diagram (SD) according to a unified modeling language (UML) sequence diagram (SD) metamodel and a multiple condition control flow graph (MCCFG) metamodel to generate the multiple condition control flow graph (MCCFG); and converting, by a coverage criteria unit, the multiple condition control flow graph (MCCFG) into a plurality of test cases. | 10. A method of generating a multi-level test case in an apparatus of generating a multi-level test case from a unified modeling language (UML) sequence diagram (SD) based on a multiple condition control flow graph (MCCFG), the method comprising the steps of: model-converting, by a model converting unit, the unified modeling language (UML) sequence diagram (SD) according to a unified modeling language (UML) sequence diagram (SD) metamodel and a multiple condition control flow graph (MCCFG) metamodel to generate the multiple condition control flow graph (MCCFG); and converting, by a coverage criteria unit, the multiple condition control flow graph (MCCFG) into a plurality of test cases. 12. The method of claim 10 , wherein the unified modeling language (UML) sequence diagram (SD) metamodel includes at least one of Interaction, Lifeline, InteractionFragment, and Message. | 0.870112 |
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