patent_num
int64 3.93M
10.2M
| claim_num1
int64 1
519
| claim_num2
int64 2
520
| sentence1
stringlengths 40
15.9k
| sentence2
stringlengths 88
20k
| label
float64 0.5
0.99
|
|---|---|---|---|---|---|
9,873,428 | 13 | 14 | 13. The method of claim 9 , further comprising: receiving, by the controller, two or more image streams from two or more cameras mounted to the autonomous vehicle; identifying, by the controller, a set of potential obstacles among the plurality of vehicle images; evaluating, by the controller, possible collisions between the autonomous vehicle and the set of potential obstacles and the sound source; and activating, by the controller, at least one of a steering actuator, an accelerator actuator, and a brake actuator of the autonomous vehicle effective to avoid collisions with the set of potential obstacles. | 13. The method of claim 9 , further comprising: receiving, by the controller, two or more image streams from two or more cameras mounted to the autonomous vehicle; identifying, by the controller, a set of potential obstacles among the plurality of vehicle images; evaluating, by the controller, possible collisions between the autonomous vehicle and the set of potential obstacles and the sound source; and activating, by the controller, at least one of a steering actuator, an accelerator actuator, and a brake actuator of the autonomous vehicle effective to avoid collisions with the set of potential obstacles. 14. The method of claim 13 , wherein the sound source is not in a field of view of any of the two or more cameras. | 0.5 |
9,639,516 | 1 | 5 | 1. An express spreadsheet visualization system configured for the simultaneous viewing of spreadsheet information and visualization information of a model, comprising; a. a processor; b. a display device; c. an input interface configured for use by the user; and d. a system memory comprising; i. a visualization application configured to show the visualization information for the model to a user on the display device; ii. a spreadsheet application configured to show the spreadsheet information of the model to the user on the display device, wherein the spreadsheet application is configured to allow, through the input interface, the user to make changes to components of the spreadsheet information, the spread sheet application comprising: an input manager module to monitor and interpret the changes made to the spreadsheet information by the user; and a synchronization module configured to pass along the changes made to the spreadsheet information to the visualization application, wherein the visualization application and the spreadsheet application are configured to show the visualization information and the spreadsheet information of the model on the display device simultaneously, wherein the spreadsheet application and the visualization application are configured to work with one another to synchronize the visualization information and the spreadsheet information in real-time, wherein the spreadsheet application is configured to continuously monitor the spreadsheet information and notify the spreadsheet application of changes made to the spreadsheet information; and wherein the input manager module is further configured to identify a global unique identifier of each component for which the user has changed, wherein the synchronization module is further configured to pass along the global unique identifiers to the visualization application, and wherein the visualization application is further configured to make changes to the corresponding components that match the global unique identifiers. | 1. An express spreadsheet visualization system configured for the simultaneous viewing of spreadsheet information and visualization information of a model, comprising; a. a processor; b. a display device; c. an input interface configured for use by the user; and d. a system memory comprising; i. a visualization application configured to show the visualization information for the model to a user on the display device; ii. a spreadsheet application configured to show the spreadsheet information of the model to the user on the display device, wherein the spreadsheet application is configured to allow, through the input interface, the user to make changes to components of the spreadsheet information, the spread sheet application comprising: an input manager module to monitor and interpret the changes made to the spreadsheet information by the user; and a synchronization module configured to pass along the changes made to the spreadsheet information to the visualization application, wherein the visualization application and the spreadsheet application are configured to show the visualization information and the spreadsheet information of the model on the display device simultaneously, wherein the spreadsheet application and the visualization application are configured to work with one another to synchronize the visualization information and the spreadsheet information in real-time, wherein the spreadsheet application is configured to continuously monitor the spreadsheet information and notify the spreadsheet application of changes made to the spreadsheet information; and wherein the input manager module is further configured to identify a global unique identifier of each component for which the user has changed, wherein the synchronization module is further configured to pass along the global unique identifiers to the visualization application, and wherein the visualization application is further configured to make changes to the corresponding components that match the global unique identifiers. 5. The express spreadsheet visualization system of claim 1 , wherein the spreadsheet application and the visualization application each further comprise a file handler module configured to transfer the spreadsheet information and the visualization information to the system memory. | 0.5 |
7,634,401 | 8 | 9 | 8. A speech recognition method, comprising: a step for starting input of speech made by a user in response to a user's operation; a step for determining whether a head portion of a speech waveform power exceeds a predetermined threshold value; a step for setting pronunciation information for recognizing the input speech of which the beginning is not missing in a case where the head portion of the speech waveform power does not exceed the predetermined threshold value, and setting pronunciation information for recognizing the input speech of which the beginning is missing in a case where the head portion of the speech waveform power exceeds the predetermined threshold value; and a step for recognizing the input speech using the set pronunciation information. | 8. A speech recognition method, comprising: a step for starting input of speech made by a user in response to a user's operation; a step for determining whether a head portion of a speech waveform power exceeds a predetermined threshold value; a step for setting pronunciation information for recognizing the input speech of which the beginning is not missing in a case where the head portion of the speech waveform power does not exceed the predetermined threshold value, and setting pronunciation information for recognizing the input speech of which the beginning is missing in a case where the head portion of the speech waveform power exceeds the predetermined threshold value; and a step for recognizing the input speech using the set pronunciation information. 9. A computer-readable medium having stored thereon a control program for causing a computer to perform the speech recognition method according to claim 8 . | 0.5 |
8,099,752 | 13 | 26 | 13. A digital television receiver that receives non-real time (NRT) content, comprising in combination: an Internet protocol (IP) receiver that receives a stream of IP packets and selects an IP subnet containing a packet stream selected as an output packet stream; the output packet stream containing non-real time content, a FLUTE file description table (FDT) and NRT metadata; an electronic storage medium; a demultiplexer that demultiplexes the IP packet stream to produce: NRT content that is stored in the electronic storage medium, the FLUTE FDT, and NRT metadata; an FDT parser that parses the FDT to produce NRT file metadata from the FLUTE FDT; and an NRT metadata parser that produces NRT service metadata and NRT content metadata; where the NRT metadata is stored in an NRT-IT table that has a content ID that indexes table entries in the FLUTE FDT. | 13. A digital television receiver that receives non-real time (NRT) content, comprising in combination: an Internet protocol (IP) receiver that receives a stream of IP packets and selects an IP subnet containing a packet stream selected as an output packet stream; the output packet stream containing non-real time content, a FLUTE file description table (FDT) and NRT metadata; an electronic storage medium; a demultiplexer that demultiplexes the IP packet stream to produce: NRT content that is stored in the electronic storage medium, the FLUTE FDT, and NRT metadata; an FDT parser that parses the FDT to produce NRT file metadata from the FLUTE FDT; and an NRT metadata parser that produces NRT service metadata and NRT content metadata; where the NRT metadata is stored in an NRT-IT table that has a content ID that indexes table entries in the FLUTE FDT. 26. The NRT receiver according to claim 13 , wherein the NRT content description text is stored in a Text Fragment Table (TFT). | 0.86518 |
8,527,496 | 1 | 7 | 1. A method comprising: receiving a query comprising a term from a user; determining a user identifier of the user and a term identifier associated with the term; gathering post identifiers and user identifiers that are associated with the term identifier in a user-term index comprising time-ordered database shards of records, where the user-term index is organized by a plurality of user identifiers associated with a plurality of users of a social networking system and a plurality of term identifiers associated with a plurality of terms used by the plurality of users; retrieving posts from an index based upon the gathered post identifiers for presentation to the user; determining user identifiers of connections of the user; and selecting partitions that are associated with the user identifiers of the connections of the user from among a plurality of partitions of the user-term index, wherein gathering post identifiers comprises gathering post identifiers that are associated with the term identifier in the selected partitions of the user-term index. | 1. A method comprising: receiving a query comprising a term from a user; determining a user identifier of the user and a term identifier associated with the term; gathering post identifiers and user identifiers that are associated with the term identifier in a user-term index comprising time-ordered database shards of records, where the user-term index is organized by a plurality of user identifiers associated with a plurality of users of a social networking system and a plurality of term identifiers associated with a plurality of terms used by the plurality of users; retrieving posts from an index based upon the gathered post identifiers for presentation to the user; determining user identifiers of connections of the user; and selecting partitions that are associated with the user identifiers of the connections of the user from among a plurality of partitions of the user-term index, wherein gathering post identifiers comprises gathering post identifiers that are associated with the term identifier in the selected partitions of the user-term index. 7. The method of claim 1 , wherein the connections comprise other users of the social networking system that are connected to the user. | 0.86168 |
9,529,904 | 11 | 12 | 11. A computer program product for updating ontology when a set of evidences and a set of constraints are given as inputs, the computer program product comprising: one or more computer-readable storage media and program instructions stored on the one or more computer-readable storage media, the program instructions comprising: program instructions to categorize one or more new concepts included in a set of evidences into one of three sets, a) a definitely relevant set, b) a possibly relevant set, and c) an irrelevant set, wherein i) concepts included in the definitely relevant set meet or exceed a first categorization threshold, ii) concepts included in the irrelevant set are below a second categorization threshold, and iii) concepts included in the possibly relevant set are (a) below the first categorization threshold and (b) meet or exceed the second categorization threshold; program instructions to add a categorized new concept included in the definitely relevant set to an first ontology; program instructions to add a categorized new concept included in the possibly relevant set to a residual ontology; program instructions to match one or more new concepts included in the set of evidences to an old concept included in the first ontology or to an old concept included in the residual ontology, wherein an old concept existed as part of the first ontology or the residual ontology before the respective addition of the new concepts to the first ontology or the residual ontology; program instructions to determine to increase an associated confidence measure of the old concept, included in the first ontology or the residual ontology, based at least in part, on the matching; program instructions to determine to expand the first ontology or the residual ontology by respectively exchanging one or more old concepts between the first ontology and the residual ontology; and program instructions to remove one or more old concepts from the first ontology or the residual ontology based, at least in part, on a set of constraints, wherein the constraints dictate size and performance requirements of the first ontology. | 11. A computer program product for updating ontology when a set of evidences and a set of constraints are given as inputs, the computer program product comprising: one or more computer-readable storage media and program instructions stored on the one or more computer-readable storage media, the program instructions comprising: program instructions to categorize one or more new concepts included in a set of evidences into one of three sets, a) a definitely relevant set, b) a possibly relevant set, and c) an irrelevant set, wherein i) concepts included in the definitely relevant set meet or exceed a first categorization threshold, ii) concepts included in the irrelevant set are below a second categorization threshold, and iii) concepts included in the possibly relevant set are (a) below the first categorization threshold and (b) meet or exceed the second categorization threshold; program instructions to add a categorized new concept included in the definitely relevant set to an first ontology; program instructions to add a categorized new concept included in the possibly relevant set to a residual ontology; program instructions to match one or more new concepts included in the set of evidences to an old concept included in the first ontology or to an old concept included in the residual ontology, wherein an old concept existed as part of the first ontology or the residual ontology before the respective addition of the new concepts to the first ontology or the residual ontology; program instructions to determine to increase an associated confidence measure of the old concept, included in the first ontology or the residual ontology, based at least in part, on the matching; program instructions to determine to expand the first ontology or the residual ontology by respectively exchanging one or more old concepts between the first ontology and the residual ontology; and program instructions to remove one or more old concepts from the first ontology or the residual ontology based, at least in part, on a set of constraints, wherein the constraints dictate size and performance requirements of the first ontology. 12. The computer program product of claim 11 , the program instructions further comprising: program instructions to determine a confidence measure associated with one or more concepts included in the set of evidences, wherein is used as a factor; and program instructions to determine whether to add or remove a given concept included in the first ontology or the residual ontology based at least in part, on the confidence measure associated with that concept. | 0.615192 |
9,846,696 | 8 | 9 | 8. The method of claim 1 , wherein the different media types include at least one of image, sound, text, speech, or content metadata. | 8. The method of claim 1 , wherein the different media types include at least one of image, sound, text, speech, or content metadata. 9. The method of claim 8 , wherein the content metadata includes at least one of: geographic positioning data, user generated text associated with the feature, or other user generated tags. | 0.5 |
9,244,968 | 2 | 4 | 2. The method of claim 1 wherein step (d) is performed by analyzing a prevalence of selected content from said first electronic document within a first domain. | 2. The method of claim 1 wherein step (d) is performed by analyzing a prevalence of selected content from said first electronic document within a first domain. 4. The method of claim 2 wherein said first domain is an index of a search engine. | 0.768362 |
9,984,072 | 9 | 13 | 9. A method for providing translated content, the method comprising: acquiring, by a device, content to be sent to a plurality of mail accounts, the content comprising text included in a text body, and the plurality of mail accounts being electronic mail accounts; displaying, on a display of the device, a plurality of translation option icons corresponding to the plurality of mail accounts, respectively; generating a plurality of inquiries corresponding to the plurality of mail accounts, respectively, based on information estimated as receiver-related information included in the text of the text body of the content; acquiring, based on an input to a corresponding translation option icon for each of the plurality of mail accounts, translation option information indicating whether to perform translation for the plurality of mail accounts; acquiring, based on the plurality of inquiries corresponding to the plurality of mail accounts, respectively, translation language information indicating a translation language for the plurality of mail accounts; determining, by the device, whether to perform the translation, and a translation language for each of the plurality of mail accounts, based on the translation option information and the translation language information for the plurality of mail accounts; and transmitting, by the device, a request for translating the content to a server, based on the determining, wherein the content comprises a first Web page address linking to a first Web page comprising first text in a language corresponding to the device, the request comprises instructions instructing the server to convert the first Web page address to a second Web page address linking to a second Web page comprising second text in a translation language corresponding to one of the plurality of mail accounts, and the second Web page corresponds to the first Web page. | 9. A method for providing translated content, the method comprising: acquiring, by a device, content to be sent to a plurality of mail accounts, the content comprising text included in a text body, and the plurality of mail accounts being electronic mail accounts; displaying, on a display of the device, a plurality of translation option icons corresponding to the plurality of mail accounts, respectively; generating a plurality of inquiries corresponding to the plurality of mail accounts, respectively, based on information estimated as receiver-related information included in the text of the text body of the content; acquiring, based on an input to a corresponding translation option icon for each of the plurality of mail accounts, translation option information indicating whether to perform translation for the plurality of mail accounts; acquiring, based on the plurality of inquiries corresponding to the plurality of mail accounts, respectively, translation language information indicating a translation language for the plurality of mail accounts; determining, by the device, whether to perform the translation, and a translation language for each of the plurality of mail accounts, based on the translation option information and the translation language information for the plurality of mail accounts; and transmitting, by the device, a request for translating the content to a server, based on the determining, wherein the content comprises a first Web page address linking to a first Web page comprising first text in a language corresponding to the device, the request comprises instructions instructing the server to convert the first Web page address to a second Web page address linking to a second Web page comprising second text in a translation language corresponding to one of the plurality of mail accounts, and the second Web page corresponds to the first Web page. 13. The method of claim 9 , further comprising displaying, on the display of the device, an object into which identification information is to be input. | 0.752443 |
9,244,901 | 1 | 8 | 1. A computer implemented method for tagging Natural language application comprising: analyzing utterances using one or more rules; assigning a tag to the analyzed utterances based on the one or more rules by a tagging server; and generating at least a report based on the one or more rules to include a unique utterance for each tag, the unique utterance displayed in a descending order. | 1. A computer implemented method for tagging Natural language application comprising: analyzing utterances using one or more rules; assigning a tag to the analyzed utterances based on the one or more rules by a tagging server; and generating at least a report based on the one or more rules to include a unique utterance for each tag, the unique utterance displayed in a descending order. 8. The method according to claim 1 , further comprising building a semantic interpretation grammar based on the tagged utterances. | 0.588608 |
7,734,459 | 41 | 44 | 41. A computer-implemented method of associating dependency structures from two different languages stored on a tangible computer readable medium, wherein the dependency structures comprise nodes organized in a hierarchical parent/child structure, the computer-implemented method comprising: aligning nodes of the dependency structures with correspondences on the tangible medium with a computer as a function of a set of rules comprising at least two different rules where aligned nodes are determined based on the parent/child structure, and wherein aligning does not require beginning with either a top or bottom node of the hierarchical parent/child structure of the dependency structures, and wherein an order of aligning nodes is based on linguistic relevance, beginning with aligning nodes having more linguistic relevance than aligning nodes having less linguistic relevance; and providing an output from the computer indicative of the alignment of the dependency structures. | 41. A computer-implemented method of associating dependency structures from two different languages stored on a tangible computer readable medium, wherein the dependency structures comprise nodes organized in a hierarchical parent/child structure, the computer-implemented method comprising: aligning nodes of the dependency structures with correspondences on the tangible medium with a computer as a function of a set of rules comprising at least two different rules where aligned nodes are determined based on the parent/child structure, and wherein aligning does not require beginning with either a top or bottom node of the hierarchical parent/child structure of the dependency structures, and wherein an order of aligning nodes is based on linguistic relevance, beginning with aligning nodes having more linguistic relevance than aligning nodes having less linguistic relevance; and providing an output from the computer indicative of the alignment of the dependency structures. 44. The computer-implemented method of claim 41 wherein one rule of the set of rules comprises aligning a pair of parent nodes, one from each dependency structure having a tentative correspondence to each other, if each child node of each respective parent node is already aligned to a child of the other parent node. | 0.698095 |
10,049,141 | 1 | 6 | 1. A method of generating a dashboard that includes multiple panels from a declarative representation of queries, widgets, bindings and facets, the method including: accessing one or more configuration files that represent interaction among queries, widgets, bindings and facet property settings to generate multiple panels of a dashboard; parsing key-value properties in the configuration files to identify queries, wherein each query specifies a value or range of values of at least one dimension and, when processed, returns a data set that includes at least one dimension and at least one measure; providing a plurality of visualization widgets, wherein each visualization widget, when invoked on a computer including a processor, accepts as input the dimension returned in the data set as an independent variable and the measure as a dependent variable; generates a specified chart or graph from the dimension and the measure to form a first view of segments in the specified chart or graph, each segment of the segments having a starting position in the first view and each segment in the specified chart or graph illustrating a first measurement of data from the data set with respect to a first particular constraint; invokes a tweener to morph each of the segments to second view from the first view by morphing each of the segments from the starting position in the first view to multiple intermediate positions and then to an ending position in the second view, each segment in the second view of the specified chart or graph illustrating a second measurement of data from the data set with respect to a second particular constraint; and invokes the tweener to morph to a third view from the second view by morphing each of the segments from the ending position in the second view to multiple intermediate positions and then to an ending position in the third view; parsing key-value properties in the configuration files to identify panels, wherein each panel declares a binding that links an associated visualization widget to an associated query; wherein each panel declares the specified chart or graph into which the data set returned by the associated query will be rendered by the associated visualization widget; and wherein at least some of the panels set the facet property, wherein the facet property links operation of data filtering controls among the panels, whereby selection of a data filter control in one panel causes the selected data filter to be applied to additional panels that have the facet property set; and generating data representing a dashboard and the panels based at least in part on the configuration files. | 1. A method of generating a dashboard that includes multiple panels from a declarative representation of queries, widgets, bindings and facets, the method including: accessing one or more configuration files that represent interaction among queries, widgets, bindings and facet property settings to generate multiple panels of a dashboard; parsing key-value properties in the configuration files to identify queries, wherein each query specifies a value or range of values of at least one dimension and, when processed, returns a data set that includes at least one dimension and at least one measure; providing a plurality of visualization widgets, wherein each visualization widget, when invoked on a computer including a processor, accepts as input the dimension returned in the data set as an independent variable and the measure as a dependent variable; generates a specified chart or graph from the dimension and the measure to form a first view of segments in the specified chart or graph, each segment of the segments having a starting position in the first view and each segment in the specified chart or graph illustrating a first measurement of data from the data set with respect to a first particular constraint; invokes a tweener to morph each of the segments to second view from the first view by morphing each of the segments from the starting position in the first view to multiple intermediate positions and then to an ending position in the second view, each segment in the second view of the specified chart or graph illustrating a second measurement of data from the data set with respect to a second particular constraint; and invokes the tweener to morph to a third view from the second view by morphing each of the segments from the ending position in the second view to multiple intermediate positions and then to an ending position in the third view; parsing key-value properties in the configuration files to identify panels, wherein each panel declares a binding that links an associated visualization widget to an associated query; wherein each panel declares the specified chart or graph into which the data set returned by the associated query will be rendered by the associated visualization widget; and wherein at least some of the panels set the facet property, wherein the facet property links operation of data filtering controls among the panels, whereby selection of a data filter control in one panel causes the selected data filter to be applied to additional panels that have the facet property set; and generating data representing a dashboard and the panels based at least in part on the configuration files. 6. The method of claim 1 , wherein at least some of the panels are declared by six to ten key-value property pairs. | 0.899476 |
10,088,562 | 7 | 8 | 7. A semantic sensing system comprising: a first sensing entity and a second sensing entity; a semantic engine coupled with the first sensing entity and the second sensing entity; a memory associated with the semantic engine, the memory storing a plurality of semantics; the memory further storing a plurality of rules comprising: a first semantic inference rule associating data from the first sensing entity with a first semantic; a second semantic inference rule associating data from the second sensing entity with a second semantic; a third semantic inference rule for a composite semantic indicating that the composite semantic is a combination of the first and the second semantic; a plurality of access control rules, each of the plurality of access control rules associating at least one time interval and at least one semantic from among the plurality of semantics stored in the memory with an access control action; the semantic engine being configured to interpret the plurality of rules; the semantic engine further being configured to perform the access control action by: inferring the first semantic based on an input from the first sensing entity at a first time and the first semantic inference rule; inferring the second semantic based on an input from the second sensing entity at a second time and the second semantic inference rule, wherein the second semantic is further inferred based on an external input; inferring the composite semantic based on the first and the second semantics and the third semantic inference rule; identifying a first access control rule from among the plurality of access control rules applicable to the composite semantic and performing the access control action associated with the first access control rule. | 7. A semantic sensing system comprising: a first sensing entity and a second sensing entity; a semantic engine coupled with the first sensing entity and the second sensing entity; a memory associated with the semantic engine, the memory storing a plurality of semantics; the memory further storing a plurality of rules comprising: a first semantic inference rule associating data from the first sensing entity with a first semantic; a second semantic inference rule associating data from the second sensing entity with a second semantic; a third semantic inference rule for a composite semantic indicating that the composite semantic is a combination of the first and the second semantic; a plurality of access control rules, each of the plurality of access control rules associating at least one time interval and at least one semantic from among the plurality of semantics stored in the memory with an access control action; the semantic engine being configured to interpret the plurality of rules; the semantic engine further being configured to perform the access control action by: inferring the first semantic based on an input from the first sensing entity at a first time and the first semantic inference rule; inferring the second semantic based on an input from the second sensing entity at a second time and the second semantic inference rule, wherein the second semantic is further inferred based on an external input; inferring the composite semantic based on the first and the second semantics and the third semantic inference rule; identifying a first access control rule from among the plurality of access control rules applicable to the composite semantic and performing the access control action associated with the first access control rule. 8. The system of claim 7 , wherein at least one of the first semantic inference rule and the second semantic inference rule comprises a pattern. | 0.741007 |
10,002,196 | 1 | 7 | 1. A method of searching a collection of electronic documents, the method comprising: replacing a set of synonymous terms appearing in a paragraph with a set of standardized paragraph terms, wherein each standardized paragraph term has an associated term weight; generating standardized search terms in response to a search query; generating paragraph scores for paragraphs of a document based at least in part on the associated weights of standardized paragraph terms that match one or more of the standardized search terms; determining overall document scores for the electronic documents based at least in part on a combination of the paragraph scores; and determining a set of matching documents, wherein the set of matching documents is ordered using the overall document scores. | 1. A method of searching a collection of electronic documents, the method comprising: replacing a set of synonymous terms appearing in a paragraph with a set of standardized paragraph terms, wherein each standardized paragraph term has an associated term weight; generating standardized search terms in response to a search query; generating paragraph scores for paragraphs of a document based at least in part on the associated weights of standardized paragraph terms that match one or more of the standardized search terms; determining overall document scores for the electronic documents based at least in part on a combination of the paragraph scores; and determining a set of matching documents, wherein the set of matching documents is ordered using the overall document scores. 7. The method of claim 1 , further comprising: retrieving a text of a matching document in response to receiving a selection of the matching document; and providing the text to a display device. | 0.686084 |
7,765,178 | 12 | 13 | 12. In a computerized search system in which queries are submitted by users who receive, in response, a list of documents selected from a corpus of documents wherein the list comprises documents deemed responsive to a user's query, an apparatus for determining relevance of the documents comprising: a search engine configured to obtain the query from a user; a Search Auto Categorizer (SAC) configured to determine initial probabilities that at least one leaf category of a taxonomy contains documents relevant to the query, at least one of the initial probabilities being non-zero, wherein the at least one leaf category contains indexed documents predetermined to be related to one another and the initial probabilities are numeric values; and a Search Logic Unit (SLU) configured to determine a relevance of documents matching the query in each leaf category having non-zero initial probability, and determine a relevance of documents to the query based on the initial probabilities of the at least one leaf category generated by the SAC and the relevance of the documents matching the query, wherein the SLU is configured to determine the relevance of documents to the query by: for each particular leaf category containing a particular document, determining a weighted relevance value by multiplying a determined relevance of the particular document matching the query by the initial probability that the particular leaf category includes relevant documents; generating undated probabilities that the nodes of the taxonomy contain relevant documents by weighting each of the relevance of documents to the query, wherein weights used to generate the updated probabilities decay monotonically with the probability that a document matching the query resides in the Particular node; determining an updated relevance of documents to the query based on the updated probabilities and the probability that a document matching the query resides in the particular node; and summing the weighted relevance values to determine the relevance of the particular document to the query. | 12. In a computerized search system in which queries are submitted by users who receive, in response, a list of documents selected from a corpus of documents wherein the list comprises documents deemed responsive to a user's query, an apparatus for determining relevance of the documents comprising: a search engine configured to obtain the query from a user; a Search Auto Categorizer (SAC) configured to determine initial probabilities that at least one leaf category of a taxonomy contains documents relevant to the query, at least one of the initial probabilities being non-zero, wherein the at least one leaf category contains indexed documents predetermined to be related to one another and the initial probabilities are numeric values; and a Search Logic Unit (SLU) configured to determine a relevance of documents matching the query in each leaf category having non-zero initial probability, and determine a relevance of documents to the query based on the initial probabilities of the at least one leaf category generated by the SAC and the relevance of the documents matching the query, wherein the SLU is configured to determine the relevance of documents to the query by: for each particular leaf category containing a particular document, determining a weighted relevance value by multiplying a determined relevance of the particular document matching the query by the initial probability that the particular leaf category includes relevant documents; generating undated probabilities that the nodes of the taxonomy contain relevant documents by weighting each of the relevance of documents to the query, wherein weights used to generate the updated probabilities decay monotonically with the probability that a document matching the query resides in the Particular node; determining an updated relevance of documents to the query based on the updated probabilities and the probability that a document matching the query resides in the particular node; and summing the weighted relevance values to determine the relevance of the particular document to the query. 13. The apparatus of claim 12 , wherein the SAC is further configured to determine for each leaf category, a probability that the documents relevant to the query reside in the leaf category. | 0.733894 |
4,797,929 | 10 | 13 | 10. A method for generating a measure of similarity for speech information in a speech recognition system, wherein the information is represented by a sequence of frames, said speech recognition system being capable of comparing a given input frame set to a template, said method comprising the steps of: combining contiguous acoustically similar frames of a previous frame set into representative frames to form a reduced template; comparing frames of said given frame set to said representative frames of said reduced template by accumulating a set of distance measures for each representative frame, each said set having a total number of accumulated distance measures corresponding to the number of frames combined in each said representative frame; and determining a measure of similarity between said given frame set and said template based on said accumulated distance measures. | 10. A method for generating a measure of similarity for speech information in a speech recognition system, wherein the information is represented by a sequence of frames, said speech recognition system being capable of comparing a given input frame set to a template, said method comprising the steps of: combining contiguous acoustically similar frames of a previous frame set into representative frames to form a reduced template; comparing frames of said given frame set to said representative frames of said reduced template by accumulating a set of distance measures for each representative frame, each said set having a total number of accumulated distance measures corresponding to the number of frames combined in each said representative frame; and determining a measure of similarity between said given frame set and said template based on said accumulated distance measures. 13. The method of claim 10, wherein comparing further includes the step of sequentially accumulating similarity measures corresponding to two representative frames, two said representative frames being separated by at least one other said representative frame, but without accumulating a similarity measure from said other representative frame. | 0.515493 |
8,214,242 | 11 | 13 | 11. A computer program product for signaling correspondence between a meeting agenda and a meeting discussion, the computer program product disposed upon a computer readable medium that comprises a recordable medium, the computer program product comprising computer program instructions capable of: receiving a meeting agenda specifying one or more topics for a meeting; analyzing, for each topic, one or more documents to identify topic keywords for that topic; receiving meeting discussions among participants for the meeting, wherein said receiving the meeting discussions among participants for the meeting comprises receiving voice utterances for the meeting of participants; generating a textual representation of the meeting discussions in a current meeting transcription; identifying a current topic for the meeting in dependence upon the meeting agenda; tracking a frequency at which the topic keywords for the current topic appear in the current meeting transcription; determining a correspondence indicator in dependence upon the tracked frequency at which the topic keywords for the current topic appear in the current meeting transcription, the correspondence indicator specifying the correspondence between the meeting agenda and the meeting discussion; and rendering the correspondence indicator to the participants of the meeting. | 11. A computer program product for signaling correspondence between a meeting agenda and a meeting discussion, the computer program product disposed upon a computer readable medium that comprises a recordable medium, the computer program product comprising computer program instructions capable of: receiving a meeting agenda specifying one or more topics for a meeting; analyzing, for each topic, one or more documents to identify topic keywords for that topic; receiving meeting discussions among participants for the meeting, wherein said receiving the meeting discussions among participants for the meeting comprises receiving voice utterances for the meeting of participants; generating a textual representation of the meeting discussions in a current meeting transcription; identifying a current topic for the meeting in dependence upon the meeting agenda; tracking a frequency at which the topic keywords for the current topic appear in the current meeting transcription; determining a correspondence indicator in dependence upon the tracked frequency at which the topic keywords for the current topic appear in the current meeting transcription, the correspondence indicator specifying the correspondence between the meeting agenda and the meeting discussion; and rendering the correspondence indicator to the participants of the meeting. 13. The computer program product of claim 11 further comprising computer program instructions capable of: identifying the topics of the meeting agenda that were discussed during the meeting; identifying the topics of the meeting agenda that were not discussed during the meeting; and generating a report that specifies the topics of the meeting agenda that were discussed during the meeting and the topics of the meeting agenda that were not discussed during the meeting. | 0.698464 |
8,027,990 | 1 | 4 | 1. A computer-implemented method comprising: receiving a query prefix from a user device; determining a user identifier based on the user device; identifying an associated user category that is associated with the user identifier; locating a node representing the query prefix in a query graph; locating descendent nodes of the located node, the descendent nodes representing queries, wherein: each descendent node has one or more user category specific frequency measures; each user category specific frequency measure for each located descendent node is associated with a user category; and each user category specific frequency measure for each located descendent node is based on a number of times that the query that is represented by the descendent node was received from users that belong to the associated user category; ranking the queries represented by the located descendent nodes based on a user category specific frequency measure associated with the identified user category; and sending the ranked queries to the user device. | 1. A computer-implemented method comprising: receiving a query prefix from a user device; determining a user identifier based on the user device; identifying an associated user category that is associated with the user identifier; locating a node representing the query prefix in a query graph; locating descendent nodes of the located node, the descendent nodes representing queries, wherein: each descendent node has one or more user category specific frequency measures; each user category specific frequency measure for each located descendent node is associated with a user category; and each user category specific frequency measure for each located descendent node is based on a number of times that the query that is represented by the descendent node was received from users that belong to the associated user category; ranking the queries represented by the located descendent nodes based on a user category specific frequency measure associated with the identified user category; and sending the ranked queries to the user device. 4. The method of claim 1 , wherein the query prefix is received from a browser executing at the user device. | 0.758929 |
9,449,054 | 10 | 16 | 10. A method for searching for content, the method comprising: receiving, using a hardware processor, a media search query for one or more media assets; identifying, using the hardware processor, a plurality of web search results from a corpus of web resources that are responsive to the media search query; determining, using the hardware processor, keyword information from a subset of the web search results, wherein a plurality of media entities are determined from at least a portion of the keyword information; assigning, using the hardware processor, a topic score for the each of the plurality of media entities based on occurrence in the web search results; selecting, using the hardware processor, at least one media entity from the plurality of media entities based on the topic score; identifying, using the hardware processor, a plurality of media assets from a corpus of media assets based at least in part on the selected media entity; and causing, using the hardware processor, a subset of the plurality of media assets to be presented to a user in response to the media search query. | 10. A method for searching for content, the method comprising: receiving, using a hardware processor, a media search query for one or more media assets; identifying, using the hardware processor, a plurality of web search results from a corpus of web resources that are responsive to the media search query; determining, using the hardware processor, keyword information from a subset of the web search results, wherein a plurality of media entities are determined from at least a portion of the keyword information; assigning, using the hardware processor, a topic score for the each of the plurality of media entities based on occurrence in the web search results; selecting, using the hardware processor, at least one media entity from the plurality of media entities based on the topic score; identifying, using the hardware processor, a plurality of media assets from a corpus of media assets based at least in part on the selected media entity; and causing, using the hardware processor, a subset of the plurality of media assets to be presented to a user in response to the media search query. 16. The method of claim 10 , further comprising: identifying a second plurality of media assets from a media data feed that are responsive to the media search query; and causing a plurality of one or more the plurality of media assets, one or more of the plurality of web search results, and one or more of the second plurality of media assets to the user in response to the media search query. | 0.5 |
8,533,178 | 17 | 19 | 17. The method of claim 15 further comprising determining, for a character sequence, changes in the correlated list of phrases over time. | 17. The method of claim 15 further comprising determining, for a character sequence, changes in the correlated list of phrases over time. 19. The method of claim 17 further comprising generating, based on the changes in the correlated list of phrases over time and information about changes in product offerings, a report on changes in consumer interest as product offerings change. | 0.5 |
7,921,092 | 10 | 22 | 10. The method of claim 1 , wherein determining the first key concept comprises automatically extracting one or more key concepts from the particular document, wherein the one or more key concepts include addresses, names, and other named entities. | 10. The method of claim 1 , wherein determining the first key concept comprises automatically extracting one or more key concepts from the particular document, wherein the one or more key concepts include addresses, names, and other named entities. 22. A machine-readable storage medium that stores instructions which, when executed by one or more processors, cause the one or more processors to perform the method of claim 10 . | 0.5 |
9,218,815 | 11 | 12 | 11. The system of claim 9 , wherein the recording of the audio and the video yields a dynamic image feature. | 11. The system of claim 9 , wherein the recording of the audio and the video yields a dynamic image feature. 12. The system of claim 11 , wherein the dynamic image feature comprises a pattern of movement. | 0.830961 |
9,779,724 | 17 | 19 | 17. A system comprising: one or more computers; and a computer-readable medium having stored thereon instructions that, when executed by the one or more computers, cause the one or more computers to perform operations comprising: receiving data indicating multiple speech recognition hypotheses for an utterance; identifying multiple alternates for a particular word in a transcription of the utterance, wherein identifying the multiple alternates for the particular word of the transcription of the utterance comprises: determining a time that the particular word begins or ends with respect to the utterance; accessing data indicating times that words in the multiple speech recognition hypotheses begin or end with respect to the utterance; and identifying the words in the multiple speech recognition hypotheses based on a measure indicative of a distance between (i) the time that the particular word begins or ends with respect to the utterance and (ii) the times that the words in the multiple speech recognition hypotheses begin or end with respect to the utterance; for each of the identified alternates: determining one or more feature scores for the alternate; inputting the one or more feature scores to a trained classifier; and receiving an output from the classifier; selecting a subset of the identified alternates to provide for display based on the classifier outputs; and providing, for display, data indicating the selected subset of the alternates. | 17. A system comprising: one or more computers; and a computer-readable medium having stored thereon instructions that, when executed by the one or more computers, cause the one or more computers to perform operations comprising: receiving data indicating multiple speech recognition hypotheses for an utterance; identifying multiple alternates for a particular word in a transcription of the utterance, wherein identifying the multiple alternates for the particular word of the transcription of the utterance comprises: determining a time that the particular word begins or ends with respect to the utterance; accessing data indicating times that words in the multiple speech recognition hypotheses begin or end with respect to the utterance; and identifying the words in the multiple speech recognition hypotheses based on a measure indicative of a distance between (i) the time that the particular word begins or ends with respect to the utterance and (ii) the times that the words in the multiple speech recognition hypotheses begin or end with respect to the utterance; for each of the identified alternates: determining one or more feature scores for the alternate; inputting the one or more feature scores to a trained classifier; and receiving an output from the classifier; selecting a subset of the identified alternates to provide for display based on the classifier outputs; and providing, for display, data indicating the selected subset of the alternates. 19. The system of claim 17 , wherein the multiple speech recognition hypotheses are ranked in a ranking; and wherein determining the one or more feature scores for the alternate comprises determining, for the alternate, a feature score indicating whether the speech recognition hypotheses at a specific position in the ranking includes the alternate. | 0.5 |
9,613,025 | 1 | 3 | 1. A natural language question answering system, comprising: one or more processors that process computer executable program code embodied in computer readable storage media, the computer executable program code comprising: conversion program code that generates a plurality of modified questions by paraphrasing a user's question; question answering engine program code that receives each of the user's question and the modified questions, and select candidate answers corresponding to each of the user's question and the modified questions; and detection program code that detects at least one among the searched candidate answers as an answer, wherein the conversion program code comprises: evaluation program code that confirms at least one among meaning preservation, grammar preservation, and an expression popularity on a plurality of paraphrase questions paraphrasing the user's question, and calculates a reliability value of the plurality of paraphrase questions; and prioritization program code that selects N modified questions in a sequence in which the reliability value is great among the plurality of paraphrase questions, and outputs the N modified question, and wherein the conversion program code calculates the reliability value of the plurality of paraphrase questions by considering a weight value with respect to a possibility in which a specific postposition or affix is added to a word or a phrase included in the plurality of paraphrase questions from a language model which is previously learned. | 1. A natural language question answering system, comprising: one or more processors that process computer executable program code embodied in computer readable storage media, the computer executable program code comprising: conversion program code that generates a plurality of modified questions by paraphrasing a user's question; question answering engine program code that receives each of the user's question and the modified questions, and select candidate answers corresponding to each of the user's question and the modified questions; and detection program code that detects at least one among the searched candidate answers as an answer, wherein the conversion program code comprises: evaluation program code that confirms at least one among meaning preservation, grammar preservation, and an expression popularity on a plurality of paraphrase questions paraphrasing the user's question, and calculates a reliability value of the plurality of paraphrase questions; and prioritization program code that selects N modified questions in a sequence in which the reliability value is great among the plurality of paraphrase questions, and outputs the N modified question, and wherein the conversion program code calculates the reliability value of the plurality of paraphrase questions by considering a weight value with respect to a possibility in which a specific postposition or affix is added to a word or a phrase included in the plurality of paraphrase questions from a language model which is previously learned. 3. The natural language question answering system of claim 1 , wherein the conversion program code performs at least one of an operation of substituting a word or a phrase of another language for a word or a phrase included in the user's question, and an operation of changing a sentence structure of the user's question and substituting a synonym for the word included in the user's question, and generates the plurality of modified questions. | 0.505568 |
8,694,347 | 8 | 9 | 8. The non-transitory computer readable medium of claim 7 wherein processing the first monitoring entity and the second monitoring entity to identify the occurrence of improper activity with respect to the monitored entity comprising comparing the first monitoring entity to the second monitoring entity. | 8. The non-transitory computer readable medium of claim 7 wherein processing the first monitoring entity and the second monitoring entity to identify the occurrence of improper activity with respect to the monitored entity comprising comparing the first monitoring entity to the second monitoring entity. 9. The non-transitory computer readable medium of claim 8 wherein the occurrence of improper activity is identified based on differences between the initial information from the first source record as stored in the first monitoring entity and the subsequent information from the first source record as stored in the second monitoring entity. | 0.5 |
9,332,401 | 17 | 18 | 17. The computer program product of claim 15 , wherein converting content further comprises: computer usable program code configured to capture audio spoken into an input device of the public address system in a digital format; and computer usable program code configured to transform the audio into text using a speech-to-text engine. | 17. The computer program product of claim 15 , wherein converting content further comprises: computer usable program code configured to capture audio spoken into an input device of the public address system in a digital format; and computer usable program code configured to transform the audio into text using a speech-to-text engine. 18. The computer program product of claim 17 , wherein, when the content is to be provided in an audio format, said translating of the content further comprises: computer usable program code configured to return the text of the translated content to audio using a text-to-speech engine appropriate to the corresponding native language. | 0.5 |
8,255,215 | 4 | 5 | 4. The method as set forth in claim 2 , wherein each of the distances is represented with use of a codebook vector distance, the code book vector distance being a distance between any two of the codebook vectors. | 4. The method as set forth in claim 2 , wherein each of the distances is represented with use of a codebook vector distance, the code book vector distance being a distance between any two of the codebook vectors. 5. The method as set forth in claim 4 , wherein the codebook vector distance is stored in advance in a matrix form. | 0.5 |
8,635,571 | 12 | 13 | 12. The method of claim 3 , further comprising providing the configuration settings for the secret portion to the user without providing an HDL or a netlist implementation of the secret portion to the user. | 12. The method of claim 3 , further comprising providing the configuration settings for the secret portion to the user without providing an HDL or a netlist implementation of the secret portion to the user. 13. The method of claim 12 , further comprising: generating, by the computer system, an exclusion list of resources to be reserved for the secret portion; and providing the exclusion list of resources to the user. | 0.659744 |
8,374,975 | 1 | 7 | 1. A method performed by one or more server devices, the method comprising: receiving, by a processor associated with the one or more server devices, a comment associated with a first document, the comment providing an opinion of, or remarks upon, a content of the first document; obtaining, by a processor associated with the one or more server devices, document clustering information which indicates that the first document is clustered with one or more second documents, the document clustering information being based on: textual information included in the first document, and textual information included in the one or more second documents; storing, in a memory associated with the one or more server devices, the comment in association with the first document and the one or more second documents based on the document clustering information indicating that the first document is clustered with the one or more second documents; presenting, by the one or more server devices, the comment in connection with the first document when the first document is accessed by a client device; and presenting, by the one or more server devices, the comment in connection with one of the one or more second documents when the one of the one or more second documents is accessed by the client device. | 1. A method performed by one or more server devices, the method comprising: receiving, by a processor associated with the one or more server devices, a comment associated with a first document, the comment providing an opinion of, or remarks upon, a content of the first document; obtaining, by a processor associated with the one or more server devices, document clustering information which indicates that the first document is clustered with one or more second documents, the document clustering information being based on: textual information included in the first document, and textual information included in the one or more second documents; storing, in a memory associated with the one or more server devices, the comment in association with the first document and the one or more second documents based on the document clustering information indicating that the first document is clustered with the one or more second documents; presenting, by the one or more server devices, the comment in connection with the first document when the first document is accessed by a client device; and presenting, by the one or more server devices, the comment in connection with one of the one or more second documents when the one of the one or more second documents is accessed by the client device. 7. The method of claim 1 , further comprising: generating a score for the comment with respect to one of the first document or the one of the one or more second documents; and determining whether to present the comment or a manner in which to present the comment based on the score. | 0.709278 |
8,930,691 | 1 | 10 | 1. A method, comprising: at a computer processor: receiving a collection of files, each file in the collection of files comprising text; encrypting each file in the collection of files; receiving a list of keywords; generating an index that indicates, for each of the files in the collection of files, which of the keywords are included in the respective files, the index being a tree structure; encrypting the index to generate an encrypted index, the encrypted index being searchable such that a list of files that includes a keyword set forth by a user is returned to the user responsive to the user querying the encrypted index with the keyword; receiving an indication that one of: a file has been added to the collection of files; or a file has been removed from the collection of files; and updating the encrypted index to represent addition of the file to the collection of files or removal of the file from the collection of files without re-encrypting each file in the collection of files. | 1. A method, comprising: at a computer processor: receiving a collection of files, each file in the collection of files comprising text; encrypting each file in the collection of files; receiving a list of keywords; generating an index that indicates, for each of the files in the collection of files, which of the keywords are included in the respective files, the index being a tree structure; encrypting the index to generate an encrypted index, the encrypted index being searchable such that a list of files that includes a keyword set forth by a user is returned to the user responsive to the user querying the encrypted index with the keyword; receiving an indication that one of: a file has been added to the collection of files; or a file has been removed from the collection of files; and updating the encrypted index to represent addition of the file to the collection of files or removal of the file from the collection of files without re-encrypting each file in the collection of files. 10. The method of claim 1 , wherein the tree structure is a red-black tree that comprises a plurality of nodes, each node in the plurality of nodes being assigned a respective vector that includes a plurality of entries, values of the plurality of entries indicating which keywords from a dictionary are included in a file corresponding to a respective node and which keywords from the dictionary are not included in the file corresponding to the respective node. | 0.552224 |
7,987,169 | 42 | 45 | 42. A machine implemented method comprising: receiving by a search engine, from a content searching or consuming application, a search expression having a plurality of recursively embedded sub-expressions, the search engine and the content searching or consuming application being operated on one or more different or same computing devices; receiving a content page nominally associated with the search expression, or access information of the content page, by the search engine; generating in response, by the search engine, one or more scores indicative of relative relevance of a content page or one or more portions of the content page to the search expression, wherein the generating by the search engine comprising recursively generating one or more scores for one or more structures indicative of relative relevance of the content page or one or more portions of the content page to each of the recursively embedded sub-expressions, wherein a structure includes substructures, wherein at least one of the recursively generating is based at least in part on a distance function, and a scoring function, wherein the distance function measures distances between sub-structures within the structure being scored to facilitate determining of mutual relevance of occurrence positions, and wherein the scoring function is positionally sensitive, yielding different scores for at least some different occurrence positions of a search sub-expression in substructures of the structure being scored, irrespective of substructure category memberships; and conditionally providing or not providing the content page or one or more portions of the content page, or access information of the content page or one or more portions of the content page, to the content searching or consuming application, by the search engine, based at least in part on the generated one or more scores. | 42. A machine implemented method comprising: receiving by a search engine, from a content searching or consuming application, a search expression having a plurality of recursively embedded sub-expressions, the search engine and the content searching or consuming application being operated on one or more different or same computing devices; receiving a content page nominally associated with the search expression, or access information of the content page, by the search engine; generating in response, by the search engine, one or more scores indicative of relative relevance of a content page or one or more portions of the content page to the search expression, wherein the generating by the search engine comprising recursively generating one or more scores for one or more structures indicative of relative relevance of the content page or one or more portions of the content page to each of the recursively embedded sub-expressions, wherein a structure includes substructures, wherein at least one of the recursively generating is based at least in part on a distance function, and a scoring function, wherein the distance function measures distances between sub-structures within the structure being scored to facilitate determining of mutual relevance of occurrence positions, and wherein the scoring function is positionally sensitive, yielding different scores for at least some different occurrence positions of a search sub-expression in substructures of the structure being scored, irrespective of substructure category memberships; and conditionally providing or not providing the content page or one or more portions of the content page, or access information of the content page or one or more portions of the content page, to the content searching or consuming application, by the search engine, based at least in part on the generated one or more scores. 45. The method of claim 42 , wherein the plurality of recursively embedded sub-expressions comprise first and second sub-expressions specifying first and second matching criteria for first and second content pages respectively, and the search expression further comprises directive directing return or non-return of the first or second matching content page. | 0.757781 |
8,688,676 | 40 | 41 | 40. The method of claim 1 , wherein the metadata comprises: programming language(s) for the programming code; number of lines of the programming instructions, comments, mixed programming instructions and comments, and blank lines in the programming code; length of the programming instructions; length of the comments; embedded licenses; keywords used in the programming code; and the frequency of the keywords used in the programming code. | 40. The method of claim 1 , wherein the metadata comprises: programming language(s) for the programming code; number of lines of the programming instructions, comments, mixed programming instructions and comments, and blank lines in the programming code; length of the programming instructions; length of the comments; embedded licenses; keywords used in the programming code; and the frequency of the keywords used in the programming code. 41. The method of claim 40 , wherein each of the statistical information are determined by parsing the programming code using a regular expression pattern matching system which matches patterns specific to the programming language found in the programming code; and the index generation includes parsing the programming code using a regular expression system to find matches of each word using a pattern matching expression that is specific to the syntax of the particular programming language used in the programming code, and where a maintaining a table of words and their frequency in the programming code is generated and maintained by adding each new word found to the table with a frequency count of 1 and incrementing the frequency count in the table for each additional time the word is found in contents of the programming code. | 0.5 |
9,753,906 | 12 | 14 | 12. A computer-readable storage medium encoded with instructions that, when executed, cause at least one processor to: output for display, a graphical user interface including a text display region and a graphical keyboard comprising a plurality of character keys, wherein a visual indicator is positioned at a first location of the text display region; responsive to receiving a first gesture detected at a second location of the text display region, reposition the visual indicator to the second location of the text display region; select, based at least in part on the first gesture detected at the second location of the text display region, at least a portion of a character string included in the text display region; responsive to receiving a second gesture to select at least one character key from the plurality of character keys, output, for display within the text display region a replacement character string that replaces at least the portion of the character string, the replacement character string being based at least in part on the at least one character key; and responsive to outputting the replacement string and without receiving a third gesture to reposition the visual indicator, automatically reposition the visual indicator to the first location. | 12. A computer-readable storage medium encoded with instructions that, when executed, cause at least one processor to: output for display, a graphical user interface including a text display region and a graphical keyboard comprising a plurality of character keys, wherein a visual indicator is positioned at a first location of the text display region; responsive to receiving a first gesture detected at a second location of the text display region, reposition the visual indicator to the second location of the text display region; select, based at least in part on the first gesture detected at the second location of the text display region, at least a portion of a character string included in the text display region; responsive to receiving a second gesture to select at least one character key from the plurality of character keys, output, for display within the text display region a replacement character string that replaces at least the portion of the character string, the replacement character string being based at least in part on the at least one character key; and responsive to outputting the replacement string and without receiving a third gesture to reposition the visual indicator, automatically reposition the visual indicator to the first location. 14. The computer-readable storage medium of claim 12 , wherein automatically repositioning the visual indicator to the first location is further responsive to receiving an indication of a termination of the second gesture. | 0.504464 |
7,930,168 | 6 | 8 | 6. A processor-based system for processing spoken language comprising: a speech recognition unit of the processor-based system configured to execute program code to convert spoken words into a text word sequence; a part-of-speech (POS) tagger of the processor-based system configured to execute program code to tag words in the text word sequence with part-of-speech tags; a disfluence identifier of the processor-based system configured to execute program code to tag edited words in the text word sequence with a feature set created with techniques comprising, matching only the highest level POS tags in a multi-level hierarchy of such tags; a parser for parsing the text word sequence into machine instructions with the aid of POS-tag and edited-word-tag information; and modifying a conventional definition of a rough copy by allowing single mismatching in POS-tag sequences of rough copy, wherein the conventional definition of rough copy in a string of tagged words has the form of ∂ 1 βλ∂ 2 , where, ∂ 1 (the source) and ∂ 2 (the copy) both begin with non-punctuation; the strings of non-punctuation POS tags of ∂ 1 and ∂ 2 are identical; β (the free final) consists of zero or more sequences of a free final word (see below) followed by optional punctuation; and interregnum “λ” consists of sequences of an interregnum string followed by optional punctuation. | 6. A processor-based system for processing spoken language comprising: a speech recognition unit of the processor-based system configured to execute program code to convert spoken words into a text word sequence; a part-of-speech (POS) tagger of the processor-based system configured to execute program code to tag words in the text word sequence with part-of-speech tags; a disfluence identifier of the processor-based system configured to execute program code to tag edited words in the text word sequence with a feature set created with techniques comprising, matching only the highest level POS tags in a multi-level hierarchy of such tags; a parser for parsing the text word sequence into machine instructions with the aid of POS-tag and edited-word-tag information; and modifying a conventional definition of a rough copy by allowing single mismatching in POS-tag sequences of rough copy, wherein the conventional definition of rough copy in a string of tagged words has the form of ∂ 1 βλ∂ 2 , where, ∂ 1 (the source) and ∂ 2 (the copy) both begin with non-punctuation; the strings of non-punctuation POS tags of ∂ 1 and ∂ 2 are identical; β (the free final) consists of zero or more sequences of a free final word (see below) followed by optional punctuation; and interregnum “λ” consists of sequences of an interregnum string followed by optional punctuation. 8. A system as in claim 6 wherein the disfluence identifier operates with a feature set created with techniques further comprising including distance to next identical orthographic word as a conditioning variable. | 0.52027 |
8,620,918 | 11 | 16 | 11. A system comprising: one or more computers configured to perform operations comprising: receiving a plurality of electronic documents associated with a domain at a server, wherein each of the plurality of electronic documents includes meta-data and textual content; for each electronic document in at least a subset of the plurality of electronic documents: identifying one or more text strings in the textual content that are to be processed differently than an identical or similar text string in other electronic documents based on the meta-data associated with the electronic document; and associating, with the electronic document, data indicating that each of the identified text strings is to be processed differently than an identical or similar text string in other electronic documents; and performing an analysis of the electronic documents to identify one or more subsets of the electronic documents that include related subject matter, wherein a first degree of relatedness of subject matter is associated with identical or similar text strings that do not have associated data indicating that each of the identical or similar text strings is to be processed differently; and wherein a second degree of relatedness of subject matter, different than the first degree of relatedness, is associated with identical or similar text strings, in which one of the text strings has associated data indicating that the text string is to be processed differently and the other text string does not have data indicating that the text string is to be processed differently. | 11. A system comprising: one or more computers configured to perform operations comprising: receiving a plurality of electronic documents associated with a domain at a server, wherein each of the plurality of electronic documents includes meta-data and textual content; for each electronic document in at least a subset of the plurality of electronic documents: identifying one or more text strings in the textual content that are to be processed differently than an identical or similar text string in other electronic documents based on the meta-data associated with the electronic document; and associating, with the electronic document, data indicating that each of the identified text strings is to be processed differently than an identical or similar text string in other electronic documents; and performing an analysis of the electronic documents to identify one or more subsets of the electronic documents that include related subject matter, wherein a first degree of relatedness of subject matter is associated with identical or similar text strings that do not have associated data indicating that each of the identical or similar text strings is to be processed differently; and wherein a second degree of relatedness of subject matter, different than the first degree of relatedness, is associated with identical or similar text strings, in which one of the text strings has associated data indicating that the text string is to be processed differently and the other text string does not have data indicating that the text string is to be processed differently. 16. The system of claim 11 , wherein the analysis includes using a particular text string as a potential feature for use in clustering documents if the particular text string has not been identified to be processed differently. | 0.514957 |
9,916,539 | 1 | 16 | 1. A computer implemented method for generating a probabilistic model usable to identify instances of a target feature in geophysical data sets stored on a memory device, the computer implemented method including: (a) using a computer processing unit to generate a probabilistic model from a training library, the model for use in identifying instances of the target feature in the geophysical data sets, the training library including one or more target examples, each target example including a signature being indicative of the target feature, and one or more non-target examples, each non-target example including a signature being indicative of a non-target feature; (b) applying, using the computer processing unit, the probabilistic model to one or more of the geophysical data sets to generate a plurality of results, each result associated with a processed geophysical data set and indicating a level of certainty as to whether that geophysical data set includes the target feature; (c) processing, using the computer processing unit, the plurality of results according to an acceptability criteria in order to identify a plurality of candidate results, the candidate results being results associated with data sets having potential significance to the performance of the probabilistic model; (d) receiving a selection of one or more of the candidate results and for each selected candidate result displaying on a display the result and its associated geophysical data set to assist a user in making an assessment as to whether or not the probabilistic model is an acceptable model for the processing of the geophysical data sets; (e) receiving from a user an assessment as to whether or not the probabilistic model is an acceptable model; and (f) if the assessment received indicates the probabilistic model is an acceptable model for processing the geophysical data, outputting the probabilistic model and/or the training library; and wherein if the assessment received at step (e) indicates the probabilistic model is not an acceptable model for the processing of the geophysical data, the method further includes: (g) receiving a selection of at least one example to be added to the training library, each example including a signature of either a target or non-target feature and being included in a data set associated with a candidate result, and modifying the training library by adding the at least one example; and/or presenting the training library examples to the user, receiving a selection of one or more examples for removal from the training library, and modifying the training library by removing the example or examples selected for removal; and (h) repeating steps (a) to (f) in respect of the modified training library. | 1. A computer implemented method for generating a probabilistic model usable to identify instances of a target feature in geophysical data sets stored on a memory device, the computer implemented method including: (a) using a computer processing unit to generate a probabilistic model from a training library, the model for use in identifying instances of the target feature in the geophysical data sets, the training library including one or more target examples, each target example including a signature being indicative of the target feature, and one or more non-target examples, each non-target example including a signature being indicative of a non-target feature; (b) applying, using the computer processing unit, the probabilistic model to one or more of the geophysical data sets to generate a plurality of results, each result associated with a processed geophysical data set and indicating a level of certainty as to whether that geophysical data set includes the target feature; (c) processing, using the computer processing unit, the plurality of results according to an acceptability criteria in order to identify a plurality of candidate results, the candidate results being results associated with data sets having potential significance to the performance of the probabilistic model; (d) receiving a selection of one or more of the candidate results and for each selected candidate result displaying on a display the result and its associated geophysical data set to assist a user in making an assessment as to whether or not the probabilistic model is an acceptable model for the processing of the geophysical data sets; (e) receiving from a user an assessment as to whether or not the probabilistic model is an acceptable model; and (f) if the assessment received indicates the probabilistic model is an acceptable model for processing the geophysical data, outputting the probabilistic model and/or the training library; and wherein if the assessment received at step (e) indicates the probabilistic model is not an acceptable model for the processing of the geophysical data, the method further includes: (g) receiving a selection of at least one example to be added to the training library, each example including a signature of either a target or non-target feature and being included in a data set associated with a candidate result, and modifying the training library by adding the at least one example; and/or presenting the training library examples to the user, receiving a selection of one or more examples for removal from the training library, and modifying the training library by removing the example or examples selected for removal; and (h) repeating steps (a) to (f) in respect of the modified training library. 16. A non-transient computer readable storage media including instructions which, when executed, facilitate a computer implemented method according to claim 1 . | 0.89704 |
9,189,954 | 13 | 17 | 13. A method for controlling a radio device, comprising: receiving a gesture input through a gesture pad that distinguishes between a plurality of fingers used for a gesture, that recognizes the orientation of the distinguished finger, and that performs a function that is dependent on the distinguished finger and its orientation; and modifying an operation of the radio device based on the received gesture input. | 13. A method for controlling a radio device, comprising: receiving a gesture input through a gesture pad that distinguishes between a plurality of fingers used for a gesture, that recognizes the orientation of the distinguished finger, and that performs a function that is dependent on the distinguished finger and its orientation; and modifying an operation of the radio device based on the received gesture input. 17. The method of claim 13 , wherein the gesture input comprises one or more taps of the gesture pad. | 0.784188 |
9,953,640 | 7 | 9 | 7. The speech recognition system of claim 1 , wherein: the confidence threshold comprises a set of confidence thresholds, the set of confidence thresholds including a first confidence threshold and at least one subsequent confidence threshold that is lower than the first confidence threshold; and the decision controller: determines that none of the text data is associated with the respective confidence level that exceeds the first confidence threshold; and determines whether any text data is associated with the respective confidence level that exceeds the at least one subsequent confidence threshold. | 7. The speech recognition system of claim 1 , wherein: the confidence threshold comprises a set of confidence thresholds, the set of confidence thresholds including a first confidence threshold and at least one subsequent confidence threshold that is lower than the first confidence threshold; and the decision controller: determines that none of the text data is associated with the respective confidence level that exceeds the first confidence threshold; and determines whether any text data is associated with the respective confidence level that exceeds the at least one subsequent confidence threshold. 9. The speech recognition system of claim 7 , wherein: the at least one subsequent confidence threshold comprises a first subsequent confidence threshold and a second subsequent confidence threshold that is lower than the first subsequent confidence threshold; and the decision controller: determines that none of the text data is associated with a confidence level that exceeds the first subsequent confidence threshold; determines that at least one text data is associated with a confidence level that exceeds the second subsequent confidence threshold; and indicates additional processing on the at least one text data is required to translate the raw audio data. | 0.5 |
10,152,509 | 1 | 4 | 1. A computer-implemented method for query hint learning in a database management system, the method comprising: detecting, by the database management system, a hint in a first query, wherein the first query has a first signature, and wherein the hint includes statistics associated with a table in a database management system; receiving, by the database management system, a second query having a second signature, wherein the second query is received at a later time relative to the first query; determining, by the database management system, that the second signature correlates with the first signature; establishing, by the database management system, a query plan for executing the second query utilizing the hint in the first query, wherein establishing the query plan for executing the second query includes using a machine-learning technique to learn from the first query and the hint in the first query and creating a third query which is based on the second query and uses the hint in the first query; and processing, by the database management system in response to receiving the second query, the query plan for executing the second query. | 1. A computer-implemented method for query hint learning in a database management system, the method comprising: detecting, by the database management system, a hint in a first query, wherein the first query has a first signature, and wherein the hint includes statistics associated with a table in a database management system; receiving, by the database management system, a second query having a second signature, wherein the second query is received at a later time relative to the first query; determining, by the database management system, that the second signature correlates with the first signature; establishing, by the database management system, a query plan for executing the second query utilizing the hint in the first query, wherein establishing the query plan for executing the second query includes using a machine-learning technique to learn from the first query and the hint in the first query and creating a third query which is based on the second query and uses the hint in the first query; and processing, by the database management system in response to receiving the second query, the query plan for executing the second query. 4. The method of claim 1 , further comprising: capturing, by processing the second query, a set of second query metadata; capturing, by processing the third query, a set of third query metadata; determining, by comparing the set of second query metadata with the set of third query metadata, that processing the third query has a benefit value with respect to processing the second query; and providing the benefit value. | 0.793425 |
9,760,634 | 1 | 2 | 1. A method for defining a content relevance model for a particular category, the content relevance model for determining whether a content segment is relevant to the particular category, the method comprising: receiving a first set of content segments that contain content previously determined to be relevant to the particular category and a second set of content segments that contain content previously determined to be not relevant to the particular category; identifying a set of key word sets that appear more frequently in the first set of content segments than the second set of content segments; and defining a content relevance model that comprises a set of groups of word sets and a score for each group, each of the groups of word sets comprising a key word set from the identified set of key word sets and at least one word set found in a context of the key word set in at least one of the received content segments, the content relevance model for scoring new content segments that are different from the content segments of the first and second sets of content segments, in order to determine relevance of the new content segments to the particular category. | 1. A method for defining a content relevance model for a particular category, the content relevance model for determining whether a content segment is relevant to the particular category, the method comprising: receiving a first set of content segments that contain content previously determined to be relevant to the particular category and a second set of content segments that contain content previously determined to be not relevant to the particular category; identifying a set of key word sets that appear more frequently in the first set of content segments than the second set of content segments; and defining a content relevance model that comprises a set of groups of word sets and a score for each group, each of the groups of word sets comprising a key word set from the identified set of key word sets and at least one word set found in a context of the key word set in at least one of the received content segments, the content relevance model for scoring new content segments that are different from the content segments of the first and second sets of content segments, in order to determine relevance of the new content segments to the particular category. 2. The method of claim 1 , wherein the content segments comprise text documents. | 0.874608 |
8,990,126 | 1 | 4 | 1. A method comprising: collecting a plurality of task-oriented, human-human dialog interactions between users and human agents for a given domain; after collecting the plurality of task-oriented, human-human dialog interactions, extracting, via a processor, a respective dialog structure associated with each of the plurality of task-oriented, human-human dialog interactions, wherein extracting the respective dialog structure comprises: identifying a respective task in each of the plurality of task-oriented, human-human dialog interactions; identifying subtasks in the respective task and associating relations between the subtasks, wherein the identifying of the subtask is done using a chunk model equation PT*=argmax Σ ST P(ST|U)P(PT|ST), where PT* is the most likely plan tree, ST is each subtask in a sequence of utterances U, PT represents likelihood of each plan tree, and P represents the individual probabilities within the chunk model equation, where the chunk model equation uses the respective dialog structures U and ST as a weighted lattice; and identifying a dialog act and a set of predicate-argument relations for the subtasks by annotating user utterances in the plurality of task-oriented, human-human dialog interactions with tags; generating a clause from the set of predicate-argument relations; removing speech repairs and dysfluencies from a respective user utterance in each of the plurality of task-oriented, human-human dialog interactions; and storing the respective task, the subtasks, the dialog act, the set of predicate-argument relations, the clause, inter-clausal relations within the clause, and a set of dominance and precedence relations associated with the respective task as a dialog interaction set represented as a single tree. | 1. A method comprising: collecting a plurality of task-oriented, human-human dialog interactions between users and human agents for a given domain; after collecting the plurality of task-oriented, human-human dialog interactions, extracting, via a processor, a respective dialog structure associated with each of the plurality of task-oriented, human-human dialog interactions, wherein extracting the respective dialog structure comprises: identifying a respective task in each of the plurality of task-oriented, human-human dialog interactions; identifying subtasks in the respective task and associating relations between the subtasks, wherein the identifying of the subtask is done using a chunk model equation PT*=argmax Σ ST P(ST|U)P(PT|ST), where PT* is the most likely plan tree, ST is each subtask in a sequence of utterances U, PT represents likelihood of each plan tree, and P represents the individual probabilities within the chunk model equation, where the chunk model equation uses the respective dialog structures U and ST as a weighted lattice; and identifying a dialog act and a set of predicate-argument relations for the subtasks by annotating user utterances in the plurality of task-oriented, human-human dialog interactions with tags; generating a clause from the set of predicate-argument relations; removing speech repairs and dysfluencies from a respective user utterance in each of the plurality of task-oriented, human-human dialog interactions; and storing the respective task, the subtasks, the dialog act, the set of predicate-argument relations, the clause, inter-clausal relations within the clause, and a set of dominance and precedence relations associated with the respective task as a dialog interaction set represented as a single tree. 4. The method of claim 1 , wherein the subtasks are identified using a chunk-based model. | 0.763298 |
9,442,915 | 1 | 9 | 1. A non-transitory computer-readable medium storing computer-readable instructions that, when read by a computing device, cause the computing device to: determine that a data security event or a data logging event occurs at an application running on a computing domain; in response to determining that the data security event or the data logging event occurs at the application running on the computing domain, determine that the application uses a first concept name from an application dictionary associated with the application to describe the data security event or the data logging event; generate an entry for the data security event or the data logging event and add the entry to a data log for the application running on the computing domain, wherein the entry includes the first concept name from the application dictionary associated with the application; determine that the first concept name from the application dictionary corresponds to a second concept name from a domain dictionary associated with a plurality of applications running on the computing domain; and generate a mapping of the first concept name from the application dictionary to the second concept name from the domain dictionary associated with the plurality of applications running on the computing domain. | 1. A non-transitory computer-readable medium storing computer-readable instructions that, when read by a computing device, cause the computing device to: determine that a data security event or a data logging event occurs at an application running on a computing domain; in response to determining that the data security event or the data logging event occurs at the application running on the computing domain, determine that the application uses a first concept name from an application dictionary associated with the application to describe the data security event or the data logging event; generate an entry for the data security event or the data logging event and add the entry to a data log for the application running on the computing domain, wherein the entry includes the first concept name from the application dictionary associated with the application; determine that the first concept name from the application dictionary corresponds to a second concept name from a domain dictionary associated with a plurality of applications running on the computing domain; and generate a mapping of the first concept name from the application dictionary to the second concept name from the domain dictionary associated with the plurality of applications running on the computing domain. 9. The non-transitory computer-readable medium of claim 1 , storing computer-readable instructions that, when read by the computing device, further cause the computing device to: in response to a determination that a second event occurs at the application, determine that the application uses a third concept name from the domain dictionary to describe the second event; and add a second entry for the second event to the data log for the application, wherein the second entry includes the third concept name from the domain dictionary. | 0.5 |
9,471,569 | 15 | 16 | 15. A computer-implemented method comprising: identifying, in at least one computing device, parameter metadata associated with constructing one of a plurality of tailored documents from a first general document; obtaining, in the at least one computing device, a plurality of supplied values associated with the parameter metadata; identifying, in the at least one computing device, a plurality of values that may be retrieved, the plurality of values being identified based at least in part on the parameter metadata, the plurality of supplied values, and at least one data store; obtaining, in the at least one computing device, a plurality of retrieved values associated with the parameter metadata; constructing, in the at least one computing device, the one of the plurality of tailored documents from the first general document, at least one portion of a second general document, the plurality of supplied values, and the plurality of retrieved values, wherein the plurality of supplied values and the plurality of retrieved values supplant at least a portion of a plurality of parameter labels within a body of the first general document and the second general document; determining, in the at least one computing device, a positive degree of correlation between a subset of the plurality of tailored documents, wherein the subset of the plurality of tailored documents includes multiple tailored documents and the positive degree of correlation indicates that the subset of the plurality of tailored documents are positively correlated by recognizing similarities within the plurality of tailored documents; recognizing, in the at least one computing device, at least one symptom of a systemic event collectively reported within the subset of the plurality of tailored documents that are positively correlated; identifying, in the at least one computing device, a response to the at least one symptom of the systemic event; and implementing the response as part of the subset of the plurality of tailored documents. | 15. A computer-implemented method comprising: identifying, in at least one computing device, parameter metadata associated with constructing one of a plurality of tailored documents from a first general document; obtaining, in the at least one computing device, a plurality of supplied values associated with the parameter metadata; identifying, in the at least one computing device, a plurality of values that may be retrieved, the plurality of values being identified based at least in part on the parameter metadata, the plurality of supplied values, and at least one data store; obtaining, in the at least one computing device, a plurality of retrieved values associated with the parameter metadata; constructing, in the at least one computing device, the one of the plurality of tailored documents from the first general document, at least one portion of a second general document, the plurality of supplied values, and the plurality of retrieved values, wherein the plurality of supplied values and the plurality of retrieved values supplant at least a portion of a plurality of parameter labels within a body of the first general document and the second general document; determining, in the at least one computing device, a positive degree of correlation between a subset of the plurality of tailored documents, wherein the subset of the plurality of tailored documents includes multiple tailored documents and the positive degree of correlation indicates that the subset of the plurality of tailored documents are positively correlated by recognizing similarities within the plurality of tailored documents; recognizing, in the at least one computing device, at least one symptom of a systemic event collectively reported within the subset of the plurality of tailored documents that are positively correlated; identifying, in the at least one computing device, a response to the at least one symptom of the systemic event; and implementing the response as part of the subset of the plurality of tailored documents. 16. The computer-implemented method of claim 15 , wherein the plurality of values that may be retrieved comprise a query of the at least one data store, wherein the query is associated with at least one supplied value. | 0.655063 |
8,051,371 | 19 | 25 | 19. A non-transitory recording medium storing a program code for executing a process by computer, the process comprises: receiving a document, the document comprising one of structured document or a semi-structured document, rendering the received document, and storing the rendered document as an image in a storage unit; grouping document description elements of the document included in the image that are juxtaposed in a horizontal or vertical direction in the image, relating the grouped document description elements to layout components of the document that describe a layout of the document description elements of the document, and storing the related grouped document description elements and layout components in the storage unit; and outputting the layout based on the stored related grouped document description elements and layout components, the layout identifying the layout components, the layout components referencing the grouped document description elements. | 19. A non-transitory recording medium storing a program code for executing a process by computer, the process comprises: receiving a document, the document comprising one of structured document or a semi-structured document, rendering the received document, and storing the rendered document as an image in a storage unit; grouping document description elements of the document included in the image that are juxtaposed in a horizontal or vertical direction in the image, relating the grouped document description elements to layout components of the document that describe a layout of the document description elements of the document, and storing the related grouped document description elements and layout components in the storage unit; and outputting the layout based on the stored related grouped document description elements and layout components, the layout identifying the layout components, the layout components referencing the grouped document description elements. 25. The recording medium according to any of claims 19 to 24 , wherein the process further comprises: generating and outputting an index document based on the document and information of the layout of the document; and generating and outputting a document which describes a content of an item of the index document based on the information of the layout. | 0.774235 |
9,098,566 | 16 | 23 | 16. A computer program product embodied on a non-transitory computer usable medium, the computer usable medium having stored thereon a sequence of instructions which, when executed by a processor causes the processor to execute a method for implementing relational views from RDF data, the method comprising: identifying a class from a class identifier in RDF data identifying information for two or more properties relating to a subject of the class, wherein the two or more properties comprises a single-value property and a multi-value property; creating a first view definition for the class from the RDF data, where the first view definition corresponds to a first relational view of the RDF data that maps the single-value property; and creating a second view definition for property information corresponding to the multi-value property from the RDF data, where the second view definition corresponds to a second relational view of the RDF data that maps the multi-value property, wherein the second relational view is populated with multi-value property objects of the subject in the first relational view. | 16. A computer program product embodied on a non-transitory computer usable medium, the computer usable medium having stored thereon a sequence of instructions which, when executed by a processor causes the processor to execute a method for implementing relational views from RDF data, the method comprising: identifying a class from a class identifier in RDF data identifying information for two or more properties relating to a subject of the class, wherein the two or more properties comprises a single-value property and a multi-value property; creating a first view definition for the class from the RDF data, where the first view definition corresponds to a first relational view of the RDF data that maps the single-value property; and creating a second view definition for property information corresponding to the multi-value property from the RDF data, where the second view definition corresponds to a second relational view of the RDF data that maps the multi-value property, wherein the second relational view is populated with multi-value property objects of the subject in the first relational view. 23. The computer program product of claim 16 in which an unmapped RDF triple is mapped to an unmapped triples view or in which the unmapped RDF triple is analyzed to create an additional RDF triple for the RDF data, in which the additional RDF triple is used to map a class view. | 0.5 |
6,167,409 | 86 | 87 | 86. A computer program product for producing a digital form of a digital document, wherein the document can be one of several different types and with varying content, the product comprising: a computer readable medium having computer program logic stored thereon, wherein the computer program logic defines: a data requesting component which produces a request for at least part of a document at an output; a data access component which accesses a definition of additional content for a type of the document; a data generation component which receives the type of the document as an input and produces additional content at an output, wherein the data processing component generates the additional content according to the definition of additional content for the type of the document; and a data processing component which receives a selected portion of the content of the document at one input and the output of the data generation component at another input and produces a digital form of the document at an output, wherein the portion was selected in accordance with the request, and wherein the data processing component combines the additional content and the content of the selected portion. | 86. A computer program product for producing a digital form of a digital document, wherein the document can be one of several different types and with varying content, the product comprising: a computer readable medium having computer program logic stored thereon, wherein the computer program logic defines: a data requesting component which produces a request for at least part of a document at an output; a data access component which accesses a definition of additional content for a type of the document; a data generation component which receives the type of the document as an input and produces additional content at an output, wherein the data processing component generates the additional content according to the definition of additional content for the type of the document; and a data processing component which receives a selected portion of the content of the document at one input and the output of the data generation component at another input and produces a digital form of the document at an output, wherein the portion was selected in accordance with the request, and wherein the data processing component combines the additional content and the content of the selected portion. 87. The computer program product of claim 86, wherein the document is a digital document having fixed text content and fixed structure defined by descriptive markup defining a plurality of hierarchical elements providing an indication of structure of the digital document. | 0.636364 |
9,015,666 | 9 | 13 | 9. The computer-readable storage media of claim 8 , wherein at least one tag in the script includes code which performs the capturing of the content items and the inserting of the content items into the documentation. | 9. The computer-readable storage media of claim 8 , wherein at least one tag in the script includes code which performs the capturing of the content items and the inserting of the content items into the documentation. 13. The computer-readable storage media of claim 9 , the operations further comprising: analyzing content items generated by the application in response to the script; and providing an indication to a user if at least one of text and images displayed by the application does not relate to any of the one or more tags of the script or the corresponding tags of the documentation. | 0.5 |
7,827,169 | 3 | 4 | 3. The data processing method of claim 1 , wherein at least one of the predefined resource description framework (RDF) queries is performed using an RDF data access query language. | 3. The data processing method of claim 1 , wherein at least one of the predefined resource description framework (RDF) queries is performed using an RDF data access query language. 4. The data processing method of claim 3 , wherein SPARQL, SquishQL, RDQL, and/or Triple is used as the RDF data access query language. | 0.5 |
8,739,023 | 1 | 3 | 1. A method for visually re-ordering interface elements comprising: a computing device identifying a plurality of elements rendered within a canvas of an interface, wherein the plurality of elements is associated with at least one of an element attribute and an element data, wherein the plurality of elements is a user interface element, wherein the plurality of elements is associated with a document object model of a markup language document, wherein the interface is a Web browser; and the computing device visually sorting the elements differently via a graphical transition effect designed so that no visual motion resulting from the transition effect is shown upon the display, wherein the transition effect causes a visual resorting or re-positioning to occur without visual effects of the transition being seen by an end-user, wherein a visual sorting of the plurality of elements occurs in a manner that does not modify the document object model, the plurality of elements as specified in the markup language document being rendered, or the element data of the elements that are visually sorted. | 1. A method for visually re-ordering interface elements comprising: a computing device identifying a plurality of elements rendered within a canvas of an interface, wherein the plurality of elements is associated with at least one of an element attribute and an element data, wherein the plurality of elements is a user interface element, wherein the plurality of elements is associated with a document object model of a markup language document, wherein the interface is a Web browser; and the computing device visually sorting the elements differently via a graphical transition effect designed so that no visual motion resulting from the transition effect is shown upon the display, wherein the transition effect causes a visual resorting or re-positioning to occur without visual effects of the transition being seen by an end-user, wherein a visual sorting of the plurality of elements occurs in a manner that does not modify the document object model, the plurality of elements as specified in the markup language document being rendered, or the element data of the elements that are visually sorted. 3. The method of claim 1 , wherein the transition effect is implemented as a sort previous that has minimal computing resource usage, wherein multiple applications of transition effects for sorting allows sorting of elements in a user desired manner without necessitating mutable access to the document object model. | 0.671518 |
8,099,278 | 5 | 6 | 5. The device of claim 3 , wherein the one or more processors are further configured to: determine a response time associated with the voice data as an amount of time between providing the query and receiving the voice data, and determine the likely age range associated with the user based on the confidence score, the correctness of the query answer, and the response time. | 5. The device of claim 3 , wherein the one or more processors are further configured to: determine a response time associated with the voice data as an amount of time between providing the query and receiving the voice data, and determine the likely age range associated with the user based on the confidence score, the correctness of the query answer, and the response time. 6. The device of claim 5 , wherein a faster response time corresponds to an increased likelihood that the user is an adult and a slower response time corresponds to a decreased likelihood that the user is an adult. | 0.544681 |
8,954,317 | 6 | 7 | 6. An apparatus configured to process a text message from a mobile station at a third party provider via text messaging, the apparatus comprising: a receiver configured to receive the text message from the mobile station, wherein the text message comprises a transaction request from a user; a processor configured to parse the message, perform a natural language interpretation of the message to identify at least one emotion related keyword or phrase included in the message, process the parsed message to determine the user's requested objective from the transaction request and to assign an emotion status to the message based on the identified at least one emotion related keyword or phrase by identifying a frequency of occurrences of the at least one emotion related keyword or phrase and associating the identified at least one keyword or phrase with the emotion status if a threshold predefined set number of occurrences of the at least one emotion related keyword or phrase is identified, and generate a response to the message based on the user's requested objective and the assigned emotion status, wherein the response comprises a pre-defined return text message acknowledging the user's requested objective corresponding to the transaction and an incentive based on the assigned emotional status; and a transmitter configured to send the response to the mobile station if a confidence of the generated response exceeds a threshold; and wherein if the confidence does not exceed the threshold, the transmitter is further configured to send the response to a live agent for correction, send the corrected response to the mobile station and wherein the processor stores the corrected response as the pre-defined return text message. | 6. An apparatus configured to process a text message from a mobile station at a third party provider via text messaging, the apparatus comprising: a receiver configured to receive the text message from the mobile station, wherein the text message comprises a transaction request from a user; a processor configured to parse the message, perform a natural language interpretation of the message to identify at least one emotion related keyword or phrase included in the message, process the parsed message to determine the user's requested objective from the transaction request and to assign an emotion status to the message based on the identified at least one emotion related keyword or phrase by identifying a frequency of occurrences of the at least one emotion related keyword or phrase and associating the identified at least one keyword or phrase with the emotion status if a threshold predefined set number of occurrences of the at least one emotion related keyword or phrase is identified, and generate a response to the message based on the user's requested objective and the assigned emotion status, wherein the response comprises a pre-defined return text message acknowledging the user's requested objective corresponding to the transaction and an incentive based on the assigned emotional status; and a transmitter configured to send the response to the mobile station if a confidence of the generated response exceeds a threshold; and wherein if the confidence does not exceed the threshold, the transmitter is further configured to send the response to a live agent for correction, send the corrected response to the mobile station and wherein the processor stores the corrected response as the pre-defined return text message. 7. The apparatus of claim 6 , wherein the processor is further configured to confirm the user's requested objective has been fulfilled in the response. | 0.65991 |
9,501,551 | 17 | 18 | 17. The computer-implemented method of claim 16 , further comprising comparing the item information with at least one second description for a second item category in the item categories maintained by the network-based service to determine a similarity between the item information and the at least one second description; and automatically determining the second item category to be at least one category recommendation for the first item based upon the similarity between the item information and at least one second description. | 17. The computer-implemented method of claim 16 , further comprising comparing the item information with at least one second description for a second item category in the item categories maintained by the network-based service to determine a similarity between the item information and the at least one second description; and automatically determining the second item category to be at least one category recommendation for the first item based upon the similarity between the item information and at least one second description. 18. The computer-implemented method of claim 17 , wherein enabling selection of the first item category comprises enabling automatic selection of the first item category from the at least one category recommendation. | 0.522124 |
8,386,435 | 5 | 22 | 5. A data processing system for retrieving one or more data values stored in a searchable archive, the system comprising: a data store storing one or more compacted files; a processor coupled to the data store; a memory coupled to the processor, the memory having stored therein program instructions executable by the processor and which cause the processor to: select a compacted file from one or more compacted files associated with a data archive, the selected compacted file including comprising a plurality of compressed segments of tokenized data and a metadata file including segment metadata more for each of the plurality of compressed segments of tokenized data within the selected compacted file, wherein the tokenized data a comprises one or more token values corresponding to one or more data values stored in the data archive; enable access of the metadata file within the selected compacted file; select a compressed segment from the plurality of compressed segments in the selected compacted file based on the segment metadata stored in the metadata file; generate a decompressed segment from the selected compressed segment without decompressing the entire compacted file; search the decompressed segment to determine if the decompressed segment includes one or more token values corresponding to the one or more data values being searched for; compile search results from each compacted file which match the token value being searched for and return the search results. | 5. A data processing system for retrieving one or more data values stored in a searchable archive, the system comprising: a data store storing one or more compacted files; a processor coupled to the data store; a memory coupled to the processor, the memory having stored therein program instructions executable by the processor and which cause the processor to: select a compacted file from one or more compacted files associated with a data archive, the selected compacted file including comprising a plurality of compressed segments of tokenized data and a metadata file including segment metadata more for each of the plurality of compressed segments of tokenized data within the selected compacted file, wherein the tokenized data a comprises one or more token values corresponding to one or more data values stored in the data archive; enable access of the metadata file within the selected compacted file; select a compressed segment from the plurality of compressed segments in the selected compacted file based on the segment metadata stored in the metadata file; generate a decompressed segment from the selected compressed segment without decompressing the entire compacted file; search the decompressed segment to determine if the decompressed segment includes one or more token values corresponding to the one or more data values being searched for; compile search results from each compacted file which match the token value being searched for and return the search results. 22. The data processing system of claim 5 , wherein said selection by the data processing system of a compressed segment is performed without decompressing the one or more compressed segments of tokenized data. | 0.689349 |
8,850,415 | 1 | 9 | 1. A non-transitory machine-readable medium storing one or more instructions for generating a labeled transition system for use with model checking of source code in a computer, which when executed by a processor of the computer, cause the processor to perform operations comprising: generating a transition system from the source code; generating an extensible markup language (XML) representation of the source code comprised of nodes, each node having an identifier; generating labels for the transition system to form the labeled transition system by running a query on the XML representation of the source code using query language to return an identifier of a node that matches the query and labeling a node in the transition system identified by the same identifier as the node that matched the query; and using the labels and the structure of the labeled transition system as input to model checking techniques to analyze the source code. | 1. A non-transitory machine-readable medium storing one or more instructions for generating a labeled transition system for use with model checking of source code in a computer, which when executed by a processor of the computer, cause the processor to perform operations comprising: generating a transition system from the source code; generating an extensible markup language (XML) representation of the source code comprised of nodes, each node having an identifier; generating labels for the transition system to form the labeled transition system by running a query on the XML representation of the source code using query language to return an identifier of a node that matches the query and labeling a node in the transition system identified by the same identifier as the node that matched the query; and using the labels and the structure of the labeled transition system as input to model checking techniques to analyze the source code. 9. The processor performed operations of claim 1 , wherein the operations of generating the transition system and generating the XML representation of the source code are performed, at least in part, simultaneously. | 0.798689 |
9,158,821 | 1 | 7 | 1. A method comprising: establishing a pool of potential matches for a user in a computer-implemented matching system, wherein each of the potential matches meet at least one criteria of the user; determining a messaging score for each of the potential matches of the pool, the messaging score indicating a messaging aptitude of the potential match; and ranking the potential matches, wherein each of the potential matches is ranked based on a similarity of the messaging score of the potential match to a messaging score of the user; wherein the messaging score for each of the potential matches comprises a combination of at least one of a number of three way interactions initiated by the potential match as compared to an average number of three way interactions initiated by other users having a similar age and same gender as the potential match, a number of messages sent by the potential match as compared to an average number of messages sent by other users having a similar age and same gender as the potential match, and a number of messages received by the potential match as compared to an average number of messages received by other users having a similar age and same gender as the potential match. | 1. A method comprising: establishing a pool of potential matches for a user in a computer-implemented matching system, wherein each of the potential matches meet at least one criteria of the user; determining a messaging score for each of the potential matches of the pool, the messaging score indicating a messaging aptitude of the potential match; and ranking the potential matches, wherein each of the potential matches is ranked based on a similarity of the messaging score of the potential match to a messaging score of the user; wherein the messaging score for each of the potential matches comprises a combination of at least one of a number of three way interactions initiated by the potential match as compared to an average number of three way interactions initiated by other users having a similar age and same gender as the potential match, a number of messages sent by the potential match as compared to an average number of messages sent by other users having a similar age and same gender as the potential match, and a number of messages received by the potential match as compared to an average number of messages received by other users having a similar age and same gender as the potential match. 7. The method of claim 1 , further comprising: offering a subscription to a website-based service in exchange for a fee, wherein the website is configured to provide one or more results associated with the ranking of the set of like profiles. | 0.809449 |
7,979,369 | 1 | 18 | 1. A method for classifying digital content, wherein the digital content includes two or more items, the method comprising the following acts performed by one or more hardware processors: identifying a grouping of the two or more items in the digital content; assigning a raw score to the identified items based on predetermined criteria; deriving an aggregate score for the digital content, wherein the aggregate score is derived from the raw scores; and using the aggregate score to output a classification of the digital content, wherein the aggregate score is used to derive a category score, and wherein the category score is derived for a user account. | 1. A method for classifying digital content, wherein the digital content includes two or more items, the method comprising the following acts performed by one or more hardware processors: identifying a grouping of the two or more items in the digital content; assigning a raw score to the identified items based on predetermined criteria; deriving an aggregate score for the digital content, wherein the aggregate score is derived from the raw scores; and using the aggregate score to output a classification of the digital content, wherein the aggregate score is used to derive a category score, and wherein the category score is derived for a user account. 18. The method of claim 1 , wherein the category score is derived for a web page. | 0.710714 |
9,594,756 | 29 | 30 | 29. The system of claim 27 , wherein the ranking calculator is configured to calculate the ranking values for the two or more contributors in an iterative process comprising repeated calculation of interim ranking values for the two or more contributors. | 29. The system of claim 27 , wherein the ranking calculator is configured to calculate the ranking values for the two or more contributors in an iterative process comprising repeated calculation of interim ranking values for the two or more contributors. 30. The system of claim 29 , wherein the ranking calculator is configured to calculate the interim ranking values in an iterative process that comprises calculating the interim ranking values in a calculation sequence, the system further comprising a sequencing module to determine the calculation sequence based at least in part on respective initial ranking values, with lower initial ranking values being earlier in the calculation sequence. | 0.5 |
8,091,070 | 12 | 17 | 12. A method of program language integration, comprising: employing a processor to execute computer executable instructions stored on a computer readable storage medium to implement the following acts: analyzing a first computer program language and a second computer program language; employing at least one of a deep embedding operation or a modified shallow mapping operation including comparing a first cost of employing the deep embedding operation and a second cost of employing the modified shallow mapping operation, wherein the deep embedding operation includes decomposing the first computer program language into semantic components and explicitly implementing the semantic components in the second computer program language, and wherein the modified shallow mapping operation includes directly mapping lower level semantic components associated with the first computer program language to corresponding lower level semantic components associated with the second computer program language; and integrating the first computer program language with the second computer program language according to at least one of the deep embedding operation or the shallow mapping operation. | 12. A method of program language integration, comprising: employing a processor to execute computer executable instructions stored on a computer readable storage medium to implement the following acts: analyzing a first computer program language and a second computer program language; employing at least one of a deep embedding operation or a modified shallow mapping operation including comparing a first cost of employing the deep embedding operation and a second cost of employing the modified shallow mapping operation, wherein the deep embedding operation includes decomposing the first computer program language into semantic components and explicitly implementing the semantic components in the second computer program language, and wherein the modified shallow mapping operation includes directly mapping lower level semantic components associated with the first computer program language to corresponding lower level semantic components associated with the second computer program language; and integrating the first computer program language with the second computer program language according to at least one of the deep embedding operation or the shallow mapping operation. 17. The method of claim 12 , further comprising generating a map from the first computer program language to the second computer program language to facilitate translation of applications between the first computer program language and the second computer program language. | 0.5 |
8,892,492 | 4 | 8 | 4. A system for declarative network access control, comprising: an interpreter to transform a plurality of sentences in a declarative network access control language to a plurality of rules, wherein one of the plurality of sentences comprises at least one client, an authentication result, at least one condition, and a consequence; a rules engine to evaluate the rules to produce a plurality of actions for providing access control to at least one network at a point of access; at least one storage device to store instructions for the interpreter, the rules and the rules engine; and at least one processor coupled to the storage device to execute the instructions for the interpreter and the rules engine. | 4. A system for declarative network access control, comprising: an interpreter to transform a plurality of sentences in a declarative network access control language to a plurality of rules, wherein one of the plurality of sentences comprises at least one client, an authentication result, at least one condition, and a consequence; a rules engine to evaluate the rules to produce a plurality of actions for providing access control to at least one network at a point of access; at least one storage device to store instructions for the interpreter, the rules and the rules engine; and at least one processor coupled to the storage device to execute the instructions for the interpreter and the rules engine. 8. The system of claim 4 , wherein at least one of the rules comprises the at least one client and the authentication result. | 0.767658 |
7,630,972 | 1 | 5 | 1. A method for searching data using a network device in communication with a client device, comprising: receiving, from the client device, one or more search terms; determining at least one concept based on an analysis of the one or more search terms; identifying one or more concept-specific data sources over which to perform searches based on the at least one concept; performing a search over each of the one or more concept-specific data sources based at least on the search term to generate a plurality of clusters, search results from each concept-specific data source comprising a different cluster in the plurality of clusters; determining a relevance score for each result within each cluster based on user specific contextual data for a user of the client device; for each cluster in the plurality of clusters, employing at least the relevance scores of each result within the respective cluster to determine a respective cluster score; generating an ordered listing of each of the plurality of clusters based on the respective cluster scores; and displaying at the client device at least a portion of the ordered plurality of clusters. | 1. A method for searching data using a network device in communication with a client device, comprising: receiving, from the client device, one or more search terms; determining at least one concept based on an analysis of the one or more search terms; identifying one or more concept-specific data sources over which to perform searches based on the at least one concept; performing a search over each of the one or more concept-specific data sources based at least on the search term to generate a plurality of clusters, search results from each concept-specific data source comprising a different cluster in the plurality of clusters; determining a relevance score for each result within each cluster based on user specific contextual data for a user of the client device; for each cluster in the plurality of clusters, employing at least the relevance scores of each result within the respective cluster to determine a respective cluster score; generating an ordered listing of each of the plurality of clusters based on the respective cluster scores; and displaying at the client device at least a portion of the ordered plurality of clusters. 5. The method of claim 1 , wherein determining a relevance score further comprises employing weighting factors to determine the relevance score that employs a user behavior information. | 0.809278 |
8,682,644 | 13 | 14 | 13. A non-transitory computer-readable storage medium storing a plurality of instructions which cause a processor to perform a method comprising: for a first string in a plurality of strings in a first database table, the plurality of strings including at least one string in a first language and at least one string in a second language different from the first language: calculating a first sort key for the first string in a first language; storing a first reference record in a second database table, the first reference record linking the first sort key to a data record in the first database table that includes the first string; calculating a second sort key for the first string in a second language different from the first language; and storing a second reference record in the second database table, the second reference record linking the second sort key to the data record in the first database table that includes the first string; receiving a request for a sorted result of a plurality of items in the first database table for the first language; generating a result set, the result set including a first entry for the first string wherein the position of the first entry in the result set is determined based upon the first sort key; filtering the result set to remove entries for strings in the second language; and providing the result set to a user. | 13. A non-transitory computer-readable storage medium storing a plurality of instructions which cause a processor to perform a method comprising: for a first string in a plurality of strings in a first database table, the plurality of strings including at least one string in a first language and at least one string in a second language different from the first language: calculating a first sort key for the first string in a first language; storing a first reference record in a second database table, the first reference record linking the first sort key to a data record in the first database table that includes the first string; calculating a second sort key for the first string in a second language different from the first language; and storing a second reference record in the second database table, the second reference record linking the second sort key to the data record in the first database table that includes the first string; receiving a request for a sorted result of a plurality of items in the first database table for the first language; generating a result set, the result set including a first entry for the first string wherein the position of the first entry in the result set is determined based upon the first sort key; filtering the result set to remove entries for strings in the second language; and providing the result set to a user. 14. A computer-readable medium according to claim 13 , said method further comprising: calculating a sort key for the string in each of a plurality of languages; and storing a reference record in the second database table for each calculated sort key that is not a duplicate of a sort key previously stored in the second database table. | 0.691176 |
9,990,421 | 10 | 12 | 10. A system for selecting documents from a document collection in response to a query, the 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: obtain, from a phrase-based index for an Internet search engine, a list of documents from a collection of documents available via the Internet that contain a first phrase, the first phrase being relevant to a query, for each document in the list: determine, using related phrase information stored in the index for each document in the list of documents, whether the document includes one or more related phrases of the first phrase, where each related phrase has an actual co-occurrence rate of the related phrase and the first phrase in the document collection that exceeds an expected co-occurrence rate of the related phrase and the first phrase in the document collection, rank the documents in the list based on a quantity of related phrases determined for each document, so that documents with more related phrases are ranked higher than documents with fewer related phrases, and select at least some of the highest-ranked documents to include in a result to the query. | 10. A system for selecting documents from a document collection in response to a query, the 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: obtain, from a phrase-based index for an Internet search engine, a list of documents from a collection of documents available via the Internet that contain a first phrase, the first phrase being relevant to a query, for each document in the list: determine, using related phrase information stored in the index for each document in the list of documents, whether the document includes one or more related phrases of the first phrase, where each related phrase has an actual co-occurrence rate of the related phrase and the first phrase in the document collection that exceeds an expected co-occurrence rate of the related phrase and the first phrase in the document collection, rank the documents in the list based on a quantity of related phrases determined for each document, so that documents with more related phrases are ranked higher than documents with fewer related phrases, and select at least some of the highest-ranked documents to include in a result to the query. 12. The system of claim 10 , wherein a document with a low frequency of query terms but a plurality of related phrases for the first phrase ranks higher than a document with a higher frequency of query terms but with no related phrases. | 0.777358 |
9,830,039 | 17 | 20 | 17. A system for using a wizard control panel in a natural language (NL) conversational system, comprising: at least one processor; a display; and a memory operatively connected with the at least one processor, the memory storing instructions, that when executed on the at least one processor, causes the at least one processor to perform operations comprising: receiving a user interaction with the NL conversational system during processing of a dialog flow; receiving graphical entities from a user's display; displaying the wizard control panel comprising a display of a representative state of the user's display for interacting with the user's display by a human wizard; processing the user interaction to display results for the user interaction within the wizard control panel and display the graphical entities; displaying a timer that indicates an amount of time left of a predetermined time before the NL conversational system performs an automatic operation using results currently shown in the wizard control panel; determining when an input is received into the wizard control panel within the predetermined time, wherein the input updates the results displayed in the wizard control panel; and in response to determining that the input is received into the wizard control panel, submitting updated results based on the input, wherein the updated results modifies operation of the NL conversational system before the automatic operation is processed. | 17. A system for using a wizard control panel in a natural language (NL) conversational system, comprising: at least one processor; a display; and a memory operatively connected with the at least one processor, the memory storing instructions, that when executed on the at least one processor, causes the at least one processor to perform operations comprising: receiving a user interaction with the NL conversational system during processing of a dialog flow; receiving graphical entities from a user's display; displaying the wizard control panel comprising a display of a representative state of the user's display for interacting with the user's display by a human wizard; processing the user interaction to display results for the user interaction within the wizard control panel and display the graphical entities; displaying a timer that indicates an amount of time left of a predetermined time before the NL conversational system performs an automatic operation using results currently shown in the wizard control panel; determining when an input is received into the wizard control panel within the predetermined time, wherein the input updates the results displayed in the wizard control panel; and in response to determining that the input is received into the wizard control panel, submitting updated results based on the input, wherein the updated results modifies operation of the NL conversational system before the automatic operation is processed. 20. The system of claim 17 , wherein submission of the updated results further comprises transmitting a control action to manipulate an entity on the user's display. | 0.715517 |
8,527,494 | 15 | 16 | 15. A computer system for recommending a tool associated with a functionality, comprising: a computer memory to store program code; and a processor to execute the program code to: receive a user request at a client computing device, the user request comprising a search query with one or more keywords; identify one or more verbs and nouns in the search query; based on the identified one or more verbs and nouns and a context of the user, execute the search query at one or more server computing devices to obtain a list of tools, wherein each tool is included in a corresponding service running in the one or more server computing devices and the list of tools are associated with one or more functionalities; based on the identified nouns and the context of the user, rank the list of tools; display the ranked list of tools on a graphical user interface; receive a selection of a tool from the displayed list of tools; and create a tool instance corresponding to the selected tool, wherein the tool instance represents a proxy to a service including the selected tool and a handler in the tool instance manages ongoing interaction with the selected tool and provides a reference to a library class that manages the lifecycle of the created tool instance. | 15. A computer system for recommending a tool associated with a functionality, comprising: a computer memory to store program code; and a processor to execute the program code to: receive a user request at a client computing device, the user request comprising a search query with one or more keywords; identify one or more verbs and nouns in the search query; based on the identified one or more verbs and nouns and a context of the user, execute the search query at one or more server computing devices to obtain a list of tools, wherein each tool is included in a corresponding service running in the one or more server computing devices and the list of tools are associated with one or more functionalities; based on the identified nouns and the context of the user, rank the list of tools; display the ranked list of tools on a graphical user interface; receive a selection of a tool from the displayed list of tools; and create a tool instance corresponding to the selected tool, wherein the tool instance represents a proxy to a service including the selected tool and a handler in the tool instance manages ongoing interaction with the selected tool and provides a reference to a library class that manages the lifecycle of the created tool instance. 16. The system of claim 15 , wherein the context of the user is determined by the user request. | 0.787946 |
9,135,396 | 31 | 32 | 31. A computer-readable non-transitory storage medium storing program instructions computer-executable to: for each particular item of a plurality of items: determine one or more other items of the plurality of items that are each distinct from but similar to the particular item, wherein said determining is based on accessing data that includes, for each item of the plurality of items, a textual description of the item that describes the item but is not itself an item in the plurality of items; for each given item of the determined one or more other items, identify an item data pair with one member comprising a sequence of text strings from the textual description of the particular item, and the other member comprising another sequence of text strings from the textual description of the given item; subsequent to said identifying, align each identified item data pair, wherein to align the identified item data pair the program instructions are configured to align text in the sequence of text strings from the textual description of the particular item with text in the other sequence of text strings from the textual description of the given item; and for each aligned item data pair, determine one or more misalignments of the aligned item data pair, and assign a similarity score to the aligned item data pair dependent on the one or more misalignments, wherein the similarity score indicates a degree of confidence that the given item and the particular item are distinct variants of each other; and based on a plurality of the aligned item data pairs and similarity scores assigned to each of those aligned item data pairs, determine a variant set comprising multiple ones of the plurality of items, wherein each item of the variant set is determined to be a variant of each other item of the variant set; wherein at least one of the aligned item data pairs comprises multiple misalignments; for each misalignment of the multiple misalignments, determine a respective subscore based on that misalignment; wherein to assign the similarity score to said at least one aligned item data pair, the program instructions are configured to assign a result of a combination of each of said subscores to said at least one aligned item data pair. | 31. A computer-readable non-transitory storage medium storing program instructions computer-executable to: for each particular item of a plurality of items: determine one or more other items of the plurality of items that are each distinct from but similar to the particular item, wherein said determining is based on accessing data that includes, for each item of the plurality of items, a textual description of the item that describes the item but is not itself an item in the plurality of items; for each given item of the determined one or more other items, identify an item data pair with one member comprising a sequence of text strings from the textual description of the particular item, and the other member comprising another sequence of text strings from the textual description of the given item; subsequent to said identifying, align each identified item data pair, wherein to align the identified item data pair the program instructions are configured to align text in the sequence of text strings from the textual description of the particular item with text in the other sequence of text strings from the textual description of the given item; and for each aligned item data pair, determine one or more misalignments of the aligned item data pair, and assign a similarity score to the aligned item data pair dependent on the one or more misalignments, wherein the similarity score indicates a degree of confidence that the given item and the particular item are distinct variants of each other; and based on a plurality of the aligned item data pairs and similarity scores assigned to each of those aligned item data pairs, determine a variant set comprising multiple ones of the plurality of items, wherein each item of the variant set is determined to be a variant of each other item of the variant set; wherein at least one of the aligned item data pairs comprises multiple misalignments; for each misalignment of the multiple misalignments, determine a respective subscore based on that misalignment; wherein to assign the similarity score to said at least one aligned item data pair, the program instructions are configured to assign a result of a combination of each of said subscores to said at least one aligned item data pair. 32. The computer-readable non-transitory storage medium of claim 31 , wherein to determine the variant set, the program instructions are configured to: generate a stored representation of an affinity graph comprising multiple nodes, each node coupled to an other node by a weighted edge; wherein each of said multiple nodes corresponds to one of said plurality of items; wherein each weighted edge coupling a particular node to an other node is assigned an item similarity weight based on at least one of said assigned similarity scores assigned to an aligned item data pair comprising a sequence of text strings from the textual description of the item corresponding to said particular node, and further comprising another sequence of text strings from the textual description of the item corresponding to said other node; perform graph clustering on said stored representation of the affinity graph to determine a cluster of nodes, wherein said graph clustering determines which nodes to include in the cluster based on the item similarity weights assigned to the weighted edges coupling the nodes of the multiple nodes; and for each node of the cluster, indicate that the item that corresponds to that node is a member of said variant set. | 0.5 |
8,768,731 | 1 | 16 | 1. A computer implemented method comprising: generating a data pool to receive and publish data that includes ultrasound echo data, wherein generating the data pool is performed by a microprocessor of the computer executing instructions stored in a non-transitory computer memory and comprises: receiving ultrasound echo data from one or more medical devices; securing the ultrasound echo data with a conditional access mechanism to provide a secure item; adding metadata to the secure item that includes metadata identifying characteristics of a patient; publishing the secure item and the metadata in a syndicated data feed using the microprocessor of a computer executing instructions stored in a non-transitory computer memory; subscribing to the syndicated data feed; storing the syndicated data feed in the data pool, wherein the format of the syndicated data feed is selected from the group consisting of: Really Simple Syndication, Resource Description Framework Site Summary, Rich Site Summary and Outline Processor Markup Language; extracting data related to an abnormality of the ultrasound echo data from the syndicated data feed; comparing the extracted data related to the abnormality with comparable ultrasound echo data from a normal specimen; extracting data related to adverse patient outcomes from the syndicated data feed; and correlating adverse patient outcomes with the abnormality of the ultrasound echo data. | 1. A computer implemented method comprising: generating a data pool to receive and publish data that includes ultrasound echo data, wherein generating the data pool is performed by a microprocessor of the computer executing instructions stored in a non-transitory computer memory and comprises: receiving ultrasound echo data from one or more medical devices; securing the ultrasound echo data with a conditional access mechanism to provide a secure item; adding metadata to the secure item that includes metadata identifying characteristics of a patient; publishing the secure item and the metadata in a syndicated data feed using the microprocessor of a computer executing instructions stored in a non-transitory computer memory; subscribing to the syndicated data feed; storing the syndicated data feed in the data pool, wherein the format of the syndicated data feed is selected from the group consisting of: Really Simple Syndication, Resource Description Framework Site Summary, Rich Site Summary and Outline Processor Markup Language; extracting data related to an abnormality of the ultrasound echo data from the syndicated data feed; comparing the extracted data related to the abnormality with comparable ultrasound echo data from a normal specimen; extracting data related to adverse patient outcomes from the syndicated data feed; and correlating adverse patient outcomes with the abnormality of the ultrasound echo data. 16. The computer implemented method of claim 1 further comprising encrypting the secure item to prevent unauthorized access. | 0.886654 |
8,090,080 | 1 | 4 | 1. A method for providing automated directory assistance, the method comprising: receiving a user utterance from a user accessing directory assistance; detecting a keyword in the user utterance; sending a query to a directory assistance database, the query based at least in part on the keyword; receiving, from the directory assistance database, received records that satisfy the query; determining hierarchical categories associated with the received records; and disambiguating the received records based at least in part on the hierarchical categories, including disambiguating a subset of the received records, wherein records in the subset share a common hierarchical category. | 1. A method for providing automated directory assistance, the method comprising: receiving a user utterance from a user accessing directory assistance; detecting a keyword in the user utterance; sending a query to a directory assistance database, the query based at least in part on the keyword; receiving, from the directory assistance database, received records that satisfy the query; determining hierarchical categories associated with the received records; and disambiguating the received records based at least in part on the hierarchical categories, including disambiguating a subset of the received records, wherein records in the subset share a common hierarchical category. 4. The method of claim 1 , wherein the user utterance includes an utterance selected from a name utterance, a category utterance, a city utterance, a state utterance, a location utterance, and an address utterance. | 0.708447 |
8,645,381 | 4 | 6 | 4. A taxonomy generation data processing system configured for generating a document taxonomy based upon tag data in groupings of tags, the system comprising: a host computer with at least one processor and memory; a tagging system executing in the memory of the host computer; tag grouping module coupled to the tagging system, the tag grouping module comprising program code enabled to group individual tags into different groupings of tags for different corresponding documents and establish weights for the tags in the groups, the tags comprising meta-information provided by users to annotate bookmarks associated with the documents; and, taxonomy generation logic executing in the memory of the host computer, the logic comprising program code enabled to: derive a folksonomy from the groups of tags for the documents, infer parent-child relationships amongst the groupings of tags based on sorting the tags for each document by the weights; organize the folksonomy into a hierarchy of nodes based on the inferred parent-child relationships, each of the nodes being associated with a different subject in the folksonomy; and publish the hierarchy of nodes as a taxonomy for the documents to provide a top-down view of the documents. | 4. A taxonomy generation data processing system configured for generating a document taxonomy based upon tag data in groupings of tags, the system comprising: a host computer with at least one processor and memory; a tagging system executing in the memory of the host computer; tag grouping module coupled to the tagging system, the tag grouping module comprising program code enabled to group individual tags into different groupings of tags for different corresponding documents and establish weights for the tags in the groups, the tags comprising meta-information provided by users to annotate bookmarks associated with the documents; and, taxonomy generation logic executing in the memory of the host computer, the logic comprising program code enabled to: derive a folksonomy from the groups of tags for the documents, infer parent-child relationships amongst the groupings of tags based on sorting the tags for each document by the weights; organize the folksonomy into a hierarchy of nodes based on the inferred parent-child relationships, each of the nodes being associated with a different subject in the folksonomy; and publish the hierarchy of nodes as a taxonomy for the documents to provide a top-down view of the documents. 6. The system of claim 4 , wherein the folksonomy is organized into a hierarchy of nodes wherein each of the nodes of the hierarchy corresponds to a different subject in the folksonomy and wherein each of the nodes excepting for a root node of the hierarchy has a parent relationship with a parent one of the nodes as a child of the parent one of the nodes. | 0.5 |
10,146,752 | 1 | 2 | 1. A method for tracking events associated with a web document on a client device, the method comprising performing, by the client device: receiving the web document at the client device; receiving a capture agent in conjunction with a delivery of the web document, the capture agent configured to execute on the client device; parsing the web document to generate a Document Object Model (DOM) tree, the DOM tree including a plurality of nodes; identifying, by the capture agent, a DOM tree modification resulting in a modified DOM tree by monitoring the DOM tree for addition of nodes to the DOM tree, removal of nodes from the DOM tree, and modification of nodes in the DOM tree, the DOM tree modification containing a first node modification associated with a first node and a second node modification associated with a second node; determining, by the capture agent, the first node modification and the second node modification to be an overlapping modification based on the first node being an ancestor of the second node in the DOM tree, the first node modification targeting a subtree of the DOM tree including the second node; determining, by the capture agent, first identification information for uniquely identifying the first node; generating, by the capture agent, an event record for the DOM tree modification, the event record including the first identification information for uniquely identifying the first node and the first node modification, the event record suppressing duplicate information by not including the second node modification based on the determination that the first node modification and the second node modification are the overlapping modification; and transmitting, from the capture agent, the event record to a server-side web session storage engine, wherein the server-side web session storage engine uses a server-side captured DOM tree of the web document, the first identification information, and the first node modification to generate the modified DOM tree. | 1. A method for tracking events associated with a web document on a client device, the method comprising performing, by the client device: receiving the web document at the client device; receiving a capture agent in conjunction with a delivery of the web document, the capture agent configured to execute on the client device; parsing the web document to generate a Document Object Model (DOM) tree, the DOM tree including a plurality of nodes; identifying, by the capture agent, a DOM tree modification resulting in a modified DOM tree by monitoring the DOM tree for addition of nodes to the DOM tree, removal of nodes from the DOM tree, and modification of nodes in the DOM tree, the DOM tree modification containing a first node modification associated with a first node and a second node modification associated with a second node; determining, by the capture agent, the first node modification and the second node modification to be an overlapping modification based on the first node being an ancestor of the second node in the DOM tree, the first node modification targeting a subtree of the DOM tree including the second node; determining, by the capture agent, first identification information for uniquely identifying the first node; generating, by the capture agent, an event record for the DOM tree modification, the event record including the first identification information for uniquely identifying the first node and the first node modification, the event record suppressing duplicate information by not including the second node modification based on the determination that the first node modification and the second node modification are the overlapping modification; and transmitting, from the capture agent, the event record to a server-side web session storage engine, wherein the server-side web session storage engine uses a server-side captured DOM tree of the web document, the first identification information, and the first node modification to generate the modified DOM tree. 2. The method of claim 1 , wherein the event includes a user interaction event associated with the web document. | 0.846995 |
9,697,827 | 25 | 27 | 25. A non-transitory computer-readable medium comprising one or more modules configured to execute in one or more processors of a computing device, the one or more modules being further configured to: receive an utterance; generate a plurality of speech recognition hypotheses based on the received utterance, wherein each hypothesis of the plurality of speech recognition hypotheses comprises a sequence of subword units; obtain a grammar of utterances, wherein each utterance of the plurality of utterances comprises a sequence of subword units; generate an input finite state transducer (FST) from the plurality of hypotheses; generate a grammar FST from the grammar of utterances, wherein the grammar FST comprises a plurality of paths of subword units, and wherein a path of the grammar FST corresponds to a command; generate an output FST using the input FST and the grammar FST, wherein the output FST comprises: a first path indicative of a difference between a first path of the input FST and a first path of the grammar FST; and a second path indicative of a difference between a second path of the input FST and a second path of the grammar FST; compute a first difference score using the first path of the output FST; compute a second difference score using the second path of the output FST; and determine a command representative of the received utterance based at least in part on the first difference score and the second difference score. | 25. A non-transitory computer-readable medium comprising one or more modules configured to execute in one or more processors of a computing device, the one or more modules being further configured to: receive an utterance; generate a plurality of speech recognition hypotheses based on the received utterance, wherein each hypothesis of the plurality of speech recognition hypotheses comprises a sequence of subword units; obtain a grammar of utterances, wherein each utterance of the plurality of utterances comprises a sequence of subword units; generate an input finite state transducer (FST) from the plurality of hypotheses; generate a grammar FST from the grammar of utterances, wherein the grammar FST comprises a plurality of paths of subword units, and wherein a path of the grammar FST corresponds to a command; generate an output FST using the input FST and the grammar FST, wherein the output FST comprises: a first path indicative of a difference between a first path of the input FST and a first path of the grammar FST; and a second path indicative of a difference between a second path of the input FST and a second path of the grammar FST; compute a first difference score using the first path of the output FST; compute a second difference score using the second path of the output FST; and determine a command representative of the received utterance based at least in part on the first difference score and the second difference score. 27. The non-transitory computer readable medium of claim 25 , wherein said computing the first difference score and said computing the second difference score further comprises generating an edit FST that assigns penalties associated with insertions, deletions and substitutions of subword units between the input FST and the grammar FST. | 0.5 |
9,110,882 | 1 | 2 | 1. A computer-implemented method for extracting structured knowledge from unstructured text for use in a knowledge representation system, the knowledge representation system comprising a knowledge base that represents knowledge using a structured, machine-readable format, the structured, machine-readable format comprising fact triples, the method comprising: identifying sentences in the unstructured text using one or more computing devices; using the one or more computing devices, converting each of a subset of the sentences to one or more simplified assertion statements of the form: subject noun phrase, verb phrase, object noun phrase; converting each of a subset of the simplified assertion statements to a corresponding fact triple using the one or more computing devices, each fact triple being constructed from three knowledge base objects, the three knowledge base objects comprising two entity objects and a relationship object expressing a relationship between the two entity objects; using the one or more computing devices, grouping the fact triples into a plurality of quarantine groups such that each of the fact triples is included in more than one of the quarantine groups, each quarantine group being defined by a corresponding one of a plurality of fact characteristics, a first one of the fact characteristics being that all of the fact triples in the corresponding quarantine group include a same one of the entity objects, a second one of the fact characteristics being that all of the fact triples in the corresponding quarantine group include a same one of the relationship objects; determining a reliability for each quarantine group with reference to the knowledge base; determining that more than one of the quarantine groups in which a first fact triple is included has at least a specified reliability; and classifying the first fact triple as a reliable fact triple in response to determining that more than one of the quarantine groups in which the first fact triple is included has at least the specified reliability. | 1. A computer-implemented method for extracting structured knowledge from unstructured text for use in a knowledge representation system, the knowledge representation system comprising a knowledge base that represents knowledge using a structured, machine-readable format, the structured, machine-readable format comprising fact triples, the method comprising: identifying sentences in the unstructured text using one or more computing devices; using the one or more computing devices, converting each of a subset of the sentences to one or more simplified assertion statements of the form: subject noun phrase, verb phrase, object noun phrase; converting each of a subset of the simplified assertion statements to a corresponding fact triple using the one or more computing devices, each fact triple being constructed from three knowledge base objects, the three knowledge base objects comprising two entity objects and a relationship object expressing a relationship between the two entity objects; using the one or more computing devices, grouping the fact triples into a plurality of quarantine groups such that each of the fact triples is included in more than one of the quarantine groups, each quarantine group being defined by a corresponding one of a plurality of fact characteristics, a first one of the fact characteristics being that all of the fact triples in the corresponding quarantine group include a same one of the entity objects, a second one of the fact characteristics being that all of the fact triples in the corresponding quarantine group include a same one of the relationship objects; determining a reliability for each quarantine group with reference to the knowledge base; determining that more than one of the quarantine groups in which a first fact triple is included has at least a specified reliability; and classifying the first fact triple as a reliable fact triple in response to determining that more than one of the quarantine groups in which the first fact triple is included has at least the specified reliability. 2. The method of claim 1 further comprising filtering first ones of the sentences unlikely to include an assertion that could be a basis for one of the simplified assertion statements. | 0.895928 |
9,009,292 | 1 | 2 | 1. A computer-implemented method for context-based data pre-fetching for an application, the method comprising: creating a context model that defines a domain of situational information associated with an application, wherein the context model comprises one or more context variables and context events, and wherein each of the context variables is associated with an update frequency that controls how frequently chances in the context variables occur; populating one or more of the context variables within the context model based on a state of the application; instantiating a context for a specific mobile device and its user, based on the context model, to monitor one or more aspects of the situational information for the specific mobile device and its user; determining whether the instantiated context is active or inactive; subscribing to the context events of the instantiated context, wherein the context events are associated with changes in the context variables corresponding to the situational information for the specific mobile device and its user, upon a determination that the instantiated context is active; identifying a likely set of data needed by the application based on values of one or more of the context variables and the context events of the instantiated context; and executing a data selection function to generate a dataset for the application, based on the identified likely set of data needed by the application, wherein the dataset is unmodifiable by the application. | 1. A computer-implemented method for context-based data pre-fetching for an application, the method comprising: creating a context model that defines a domain of situational information associated with an application, wherein the context model comprises one or more context variables and context events, and wherein each of the context variables is associated with an update frequency that controls how frequently chances in the context variables occur; populating one or more of the context variables within the context model based on a state of the application; instantiating a context for a specific mobile device and its user, based on the context model, to monitor one or more aspects of the situational information for the specific mobile device and its user; determining whether the instantiated context is active or inactive; subscribing to the context events of the instantiated context, wherein the context events are associated with changes in the context variables corresponding to the situational information for the specific mobile device and its user, upon a determination that the instantiated context is active; identifying a likely set of data needed by the application based on values of one or more of the context variables and the context events of the instantiated context; and executing a data selection function to generate a dataset for the application, based on the identified likely set of data needed by the application, wherein the dataset is unmodifiable by the application. 2. The method of claim 1 , wherein the context events comprise notification events, input events, and state events. | 0.802405 |
9,785,680 | 1 | 2 | 1. A method for determining a service description that most closely matches a service name provided by a user, said method comprising: determining, by a processor of a computer system, that the service name provided by the user is not an exact match to a service name in a service registry that comprises service names and associated service descriptions; said processor generating a ranked service name, wherein the ranked service name comprises at least one alternative service name and a respective rank of each alternative service name, and wherein the respective rank indicates how closely the alternative service name associated with the respective rank resembles the service name provided by the user; said processor ascertaining a service description associated with a service name in the service registry that either matches the highest ranked alternative service name in the service name list or matches the next highest ranked alternative service name in the service name list; and said processor communicating the ascertained service description to the user, wherein said ascertaining comprises: (i) searching a top rank by locating a greatest value among all ranks in the ranked service name list; and (ii) looking up the service registry for the service description with a first alternative service name associated with the top rank from said searching such that the service description is associated with the highest ranked alternative service name. | 1. A method for determining a service description that most closely matches a service name provided by a user, said method comprising: determining, by a processor of a computer system, that the service name provided by the user is not an exact match to a service name in a service registry that comprises service names and associated service descriptions; said processor generating a ranked service name, wherein the ranked service name comprises at least one alternative service name and a respective rank of each alternative service name, and wherein the respective rank indicates how closely the alternative service name associated with the respective rank resembles the service name provided by the user; said processor ascertaining a service description associated with a service name in the service registry that either matches the highest ranked alternative service name in the service name list or matches the next highest ranked alternative service name in the service name list; and said processor communicating the ascertained service description to the user, wherein said ascertaining comprises: (i) searching a top rank by locating a greatest value among all ranks in the ranked service name list; and (ii) looking up the service registry for the service description with a first alternative service name associated with the top rank from said searching such that the service description is associated with the highest ranked alternative service name. 2. The method of claim 1 , wherein said generating the ranked service name list is performed in response to said determining that the service name provided by the user is not an exact match to a service name in the service registry. | 0.906073 |
8,732,667 | 17 | 18 | 17. A computer system comprising the following: one or more processors; system memory; and one or more computer-readable storage media having stored thereon computer-executable instructions that, when executed by the one or more processors, causes the computing system to perform a method for providing debugging functionality for debugging a programming language within an interactive development environment, the method comprising the following: an act of displaying an interactive development environment including a programming language grammar input receiving area that allows a user to view and interact with grammar inputs defining a particular programming language, a programming language input receiving area that allows the user to view and interact with programming language inputs that use the particular programming language as defined by the grammar inputs, and a parse output area that allows the user to view the current state of a parser, wherein the grammar input receiving area, the language input receiving area, and the parse output area are each displayed simultaneously; an act of receiving a step input from the user indicating that the interactive development environment is to begin stepping through each language input to determine whether the language inputs have created an error relative to the grammar that defines acceptable language inputs; and an act of presenting the resulting output for each language input, such that as the user provides subsequent step inputs, the interactive development environment successively steps through each language input and simultaneously presents, at each step: an indication within the language input receiving area of a present language input that is being parsed by the parser, an indication within the grammar input receiving area of at least one grammar rule that is being applied to the present language input, the indication visually distinguishing the at least one grammar rule that is being applied to the present language input from other grammar inputs, and corresponding output in the parse output area. | 17. A computer system comprising the following: one or more processors; system memory; and one or more computer-readable storage media having stored thereon computer-executable instructions that, when executed by the one or more processors, causes the computing system to perform a method for providing debugging functionality for debugging a programming language within an interactive development environment, the method comprising the following: an act of displaying an interactive development environment including a programming language grammar input receiving area that allows a user to view and interact with grammar inputs defining a particular programming language, a programming language input receiving area that allows the user to view and interact with programming language inputs that use the particular programming language as defined by the grammar inputs, and a parse output area that allows the user to view the current state of a parser, wherein the grammar input receiving area, the language input receiving area, and the parse output area are each displayed simultaneously; an act of receiving a step input from the user indicating that the interactive development environment is to begin stepping through each language input to determine whether the language inputs have created an error relative to the grammar that defines acceptable language inputs; and an act of presenting the resulting output for each language input, such that as the user provides subsequent step inputs, the interactive development environment successively steps through each language input and simultaneously presents, at each step: an indication within the language input receiving area of a present language input that is being parsed by the parser, an indication within the grammar input receiving area of at least one grammar rule that is being applied to the present language input, the indication visually distinguishing the at least one grammar rule that is being applied to the present language input from other grammar inputs, and corresponding output in the parse output area. 18. The system of claim 17 , further comprising presenting an additional debugger output area that displays the output generated by the interactive development environment's debugging engine. | 0.5 |
8,612,306 | 25 | 26 | 25. A system comprising: a computer readable medium including a program product; and one or more processors configured to execute the program product and perform operations comprising: receiving a query from a client device wherein the query specifies a first product by specifying a product brand and a product model; determining, using the product brand and the product model, one or more specific product attributes associated with the first product; determining a respective correspondence between the one or more specific product attributes associated with the first product and one or more category attributes associated with one or more product categories, each product category having a predetermined plurality of category attributes; identifying, by data processing apparatus, a product category associated with the first product based on the determined respective correspondence; identifying a first set of one or more second products, the first set of second products having one or more attributes corresponding to the one or more category attributes associated with the product category of the first product; calculating, by data processing apparatus, respective distances between the one or more specific product attributes of the first product and corresponding attributes of products in the first set of second products; determining that one or more of the calculated respective distances satisfy a threshold; in response to the determining, identifying at least one product in the first set of second products as a suggested product based on the determination that the threshold is satisfied by one of the calculated respective distances; and providing the at least one identified product as a suggestion in response to the query. | 25. A system comprising: a computer readable medium including a program product; and one or more processors configured to execute the program product and perform operations comprising: receiving a query from a client device wherein the query specifies a first product by specifying a product brand and a product model; determining, using the product brand and the product model, one or more specific product attributes associated with the first product; determining a respective correspondence between the one or more specific product attributes associated with the first product and one or more category attributes associated with one or more product categories, each product category having a predetermined plurality of category attributes; identifying, by data processing apparatus, a product category associated with the first product based on the determined respective correspondence; identifying a first set of one or more second products, the first set of second products having one or more attributes corresponding to the one or more category attributes associated with the product category of the first product; calculating, by data processing apparatus, respective distances between the one or more specific product attributes of the first product and corresponding attributes of products in the first set of second products; determining that one or more of the calculated respective distances satisfy a threshold; in response to the determining, identifying at least one product in the first set of second products as a suggested product based on the determination that the threshold is satisfied by one of the calculated respective distances; and providing the at least one identified product as a suggestion in response to the query. 26. The system of claim 25 in which the query comprises one or more of a product brand and a product model. | 0.843109 |
9,544,650 | 13 | 14 | 13. The system of claim 12 , wherein the hardware processor is further configured to: extract an audio stream from each of a plurality of television channels; generate, for each of the plurality of television channels, at least one audio fingerprint from at least a portion of the extracted audio stream that corresponds to one of the plurality of television channels; and store the at least one audio fingerprint in a database indexed by channel. | 13. The system of claim 12 , wherein the hardware processor is further configured to: extract an audio stream from each of a plurality of television channels; generate, for each of the plurality of television channels, at least one audio fingerprint from at least a portion of the extracted audio stream that corresponds to one of the plurality of television channels; and store the at least one audio fingerprint in a database indexed by channel. 14. The system of claim 13 , wherein the hardware processor is further configured to: compare the audio fingerprint with the at least one stored audio fingerprint; and identify the channel based on the comparison. | 0.5 |
9,390,196 | 1 | 13 | 1. A computer program product for constructing and utilizing an ontological graph, the computer program product comprising a non-transitory computer readable storage medium having program code embodied therewith, the program code readable and executable by a processor to perform a method comprising: receiving a seed term from a user; receiving a first expansion signal from the user; constructing an ontological graph that includes nodes representing the seed term plus other terms that are located in accordance with instructions derived from the first expansion signal, wherein the seed term and the other terms share a common trait; and displaying terms from the ontological graph as string literals in a dictionary, wherein the dictionary contains related other terms at a resolution level that is controlled by the first expansion signal from the user and the seed term, wherein the first expansion signal causes additional nodes to be identified for the ontological graph, wherein the dictionary is an original dictionary that contains the seed term, wherein the first expansion signal further causes terms represented by the additional nodes to populate an expanded dictionary, and wherein the expanded dictionary is expanded from the original dictionary in which the seed term was located. | 1. A computer program product for constructing and utilizing an ontological graph, the computer program product comprising a non-transitory computer readable storage medium having program code embodied therewith, the program code readable and executable by a processor to perform a method comprising: receiving a seed term from a user; receiving a first expansion signal from the user; constructing an ontological graph that includes nodes representing the seed term plus other terms that are located in accordance with instructions derived from the first expansion signal, wherein the seed term and the other terms share a common trait; and displaying terms from the ontological graph as string literals in a dictionary, wherein the dictionary contains related other terms at a resolution level that is controlled by the first expansion signal from the user and the seed term, wherein the first expansion signal causes additional nodes to be identified for the ontological graph, wherein the dictionary is an original dictionary that contains the seed term, wherein the first expansion signal further causes terms represented by the additional nodes to populate an expanded dictionary, and wherein the expanded dictionary is expanded from the original dictionary in which the seed term was located. 13. The computer program product of claim 1 , wherein the method further comprises: identifying a common topic of a predefined set of documents; and determining the common trait that is shared between the seed term and the other terms based on the common topic of the predefined set of documents, wherein terms that are found within all of the predefined set of documents are deemed to share the common trait. | 0.638053 |
7,848,988 | 25 | 29 | 25. The method of claim 23 , wherein the measuring step comprises accumulating a historical collection of IT data and evaluating results in terms of the one or more metrics. | 25. The method of claim 23 , wherein the measuring step comprises accumulating a historical collection of IT data and evaluating results in terms of the one or more metrics. 29. The method of claim 25 , wherein the one or more metrics comprise a measurement that directly measures the performance of a party to the electronic contract. | 0.5 |
9,740,731 | 13 | 17 | 13. A method of automatically sorting disaster and/or accident related news stories by their temporal characteristics using a computing system comprising: a) identifying a first news vent involving a disaster and/or accident related event; wherein said first news event is associated by the computing system with a plurality of corresponding event status states defined for a progress template for such first news event; wherein said event status states are associated by the computing system with content for the first news event which is distinctive to different temporal periods within said progress template; b) analyzing a first electronic document describing said first event with the computing system to identify first content snippets determinative of a first status state of said first news event relative to said event status states defined for said progress template; c) analyzing a second electronic document with second content snippets describing a second status state for said first news event with the computing system; and d) determining which of said first and second electronic documents contains content describing a more current status state of said first event by comparing said first status state to said second status state and generating an output with the computing system indicating which of said electronic documents describes the more current status state; wherein changes in corresponding content snippets relating to one of least property damage, loss of lives, injuries survivors and/or an absolute time of such first event are tracked between documents and used by the computing system to derive a relative temporal relationship between said first electronic document and second electronic document. | 13. A method of automatically sorting disaster and/or accident related news stories by their temporal characteristics using a computing system comprising: a) identifying a first news vent involving a disaster and/or accident related event; wherein said first news event is associated by the computing system with a plurality of corresponding event status states defined for a progress template for such first news event; wherein said event status states are associated by the computing system with content for the first news event which is distinctive to different temporal periods within said progress template; b) analyzing a first electronic document describing said first event with the computing system to identify first content snippets determinative of a first status state of said first news event relative to said event status states defined for said progress template; c) analyzing a second electronic document with second content snippets describing a second status state for said first news event with the computing system; and d) determining which of said first and second electronic documents contains content describing a more current status state of said first event by comparing said first status state to said second status state and generating an output with the computing system indicating which of said electronic documents describes the more current status state; wherein changes in corresponding content snippets relating to one of least property damage, loss of lives, injuries survivors and/or an absolute time of such first event are tracked between documents and used by the computing system to derive a relative temporal relationship between said first electronic document and second electronic document. 17. The method of claim 13 wherein temporally related information concerning said electronic documents is collected from a plurality of online sources, including blog, message boards and/or social networking sites. | 0.606618 |
9,304,826 | 15 | 24 | 15. An interactive self-help application system, comprising: an application script for implementing the interactive self-help service; a deployment platform on a network having a plurality of application servers distributed over the network and application resources able to execute the application script; a specification for preferred application resources needed for executing the application script; a centralized list of application resources, said centralized list of resources being checked and updated from a first node on the network to maintain a list of application resources prioritized by performance and including resource preference and availability; a selected application server located at a second node on the network for executing the application script, said selected application server being selected from the plurality of application servers on the network; a local list of application resources maintained at the selected application server, said local list of application resources listing application resources previously servicing the selected application server including quality of service of the application resources from the second node; a central view list of application resources obtained by querying the centralized list by resource type to conform to a predefined specification and prioritized by performance; a local view list of resources generated by querying the local list to select a list of previously contacted resources prioritized by availability and indicating a degree of success of previous browser attempts for each previously contacted resource; and wherein the application script is executed by the selected application server with application resources located dynamically from a final prioritized list created based upon the central view list filtered by using the local view list. | 15. An interactive self-help application system, comprising: an application script for implementing the interactive self-help service; a deployment platform on a network having a plurality of application servers distributed over the network and application resources able to execute the application script; a specification for preferred application resources needed for executing the application script; a centralized list of application resources, said centralized list of resources being checked and updated from a first node on the network to maintain a list of application resources prioritized by performance and including resource preference and availability; a selected application server located at a second node on the network for executing the application script, said selected application server being selected from the plurality of application servers on the network; a local list of application resources maintained at the selected application server, said local list of application resources listing application resources previously servicing the selected application server including quality of service of the application resources from the second node; a central view list of application resources obtained by querying the centralized list by resource type to conform to a predefined specification and prioritized by performance; a local view list of resources generated by querying the local list to select a list of previously contacted resources prioritized by availability and indicating a degree of success of previous browser attempts for each previously contacted resource; and wherein the application script is executed by the selected application server with application resources located dynamically from a final prioritized list created based upon the central view list filtered by using the local view list. 24. The system as in claim 15 , wherein the specification for preferred application resources needed for executing the application script includes application resources having a tariff below a predefined limit. | 0.606742 |
10,089,578 | 14 | 29 | 14. A system for predicting a content attribute, comprising: an input interface configured to receive from an audio media object database one or more labeled media objects including audio content, wherein the one or more labeled media objects are labeled with one or more training attributes, the training attributes including at least one of danceability, energy, speechiness, liveness, acousticness, valence, and instrumentalness; a ground-truth database configured to store the one or more training attributes; a local feature extraction engine configured to extract one or more local features associated with the audio content; a mid-level feature extraction engine configured to retrieve from the one or more labeled media objects one or more mid-level features associated with the audio content; a selector engine configured to construct a feature vector of one or more content-derived values for each of the one or more labeled media objects based on (i) the one or more local features, (ii) the one or more mid-level features, or (iii) a combination of (i) and (ii); a machine learning processor configured to generate a model based on the feature vector and the one or more attributes; the input interface further configured to receive one or more unlabeled media objects including audio content; and an acoustic attribution prediction subsystem configured to apply the model to one or more unlabeled media objects to generate one or more predicted acoustic attribute labels, the predicted acoustic attribute labels including at least one of danceability, energy, speechiness, liveness, acousticness, valence, and instrumentalness; and a processor device configured to, based on the one or more predicted acoustic attribute labels, generate and output to a peripheral device at least one of (i) a playlist, (ii) a recommendation, (iii) a catalog search result, and (iv) one or more catalogs categorized into one of a plurality of subclasses. | 14. A system for predicting a content attribute, comprising: an input interface configured to receive from an audio media object database one or more labeled media objects including audio content, wherein the one or more labeled media objects are labeled with one or more training attributes, the training attributes including at least one of danceability, energy, speechiness, liveness, acousticness, valence, and instrumentalness; a ground-truth database configured to store the one or more training attributes; a local feature extraction engine configured to extract one or more local features associated with the audio content; a mid-level feature extraction engine configured to retrieve from the one or more labeled media objects one or more mid-level features associated with the audio content; a selector engine configured to construct a feature vector of one or more content-derived values for each of the one or more labeled media objects based on (i) the one or more local features, (ii) the one or more mid-level features, or (iii) a combination of (i) and (ii); a machine learning processor configured to generate a model based on the feature vector and the one or more attributes; the input interface further configured to receive one or more unlabeled media objects including audio content; and an acoustic attribution prediction subsystem configured to apply the model to one or more unlabeled media objects to generate one or more predicted acoustic attribute labels, the predicted acoustic attribute labels including at least one of danceability, energy, speechiness, liveness, acousticness, valence, and instrumentalness; and a processor device configured to, based on the one or more predicted acoustic attribute labels, generate and output to a peripheral device at least one of (i) a playlist, (ii) a recommendation, (iii) a catalog search result, and (iv) one or more catalogs categorized into one of a plurality of subclasses. 29. The system according to claim 14 , wherein the one or more attributes are at least one of (i) on a continuous scale and (ii) a binary classification type. | 0.834728 |
8,315,966 | 2 | 3 | 2. A system as set forth in claim 1 , wherein said requirement solver comprises a partial evaluator, a model-finder and a SAT solver. | 2. A system as set forth in claim 1 , wherein said requirement solver comprises a partial evaluator, a model-finder and a SAT solver. 3. A system as set forth in claim 2 , wherein said model-finder is a Kodkod model-finder. | 0.707237 |
8,104,017 | 53 | 58 | 53. The computer readable medium of claim 52 , where the first part is a portion, of the graphical model, associated with the graphical affordance and the second part is the segment associated with the graphical affordance. | 53. The computer readable medium of claim 52 , where the first part is a portion, of the graphical model, associated with the graphical affordance and the second part is the segment associated with the graphical affordance. 58. The computer readable medium of claim 53 , where the instructions are further for: updating an inferred property of the portion; and implementing the inferred property as a specified property related to the portion. | 0.594444 |
8,346,750 | 1 | 4 | 1. A method for generating search results comprising web documents with associated expert information, the method comprising: identifying a plurality of web documents tagged as falling within one or more categories of web documents responsive to one or more search queries; ranking the plurality of web documents tagged as falling within the one or more categories responsive to the one or more search queries based at least on a number of experts of the one or more categories that tagged respective ones of the plurality of web documents, wherein a given expert is determined based upon a number of users that agree with the input of one or more tags by the given expert exceeding a threshold value; and generating a list of ranked search results responsive to the one or more search queries. | 1. A method for generating search results comprising web documents with associated expert information, the method comprising: identifying a plurality of web documents tagged as falling within one or more categories of web documents responsive to one or more search queries; ranking the plurality of web documents tagged as falling within the one or more categories responsive to the one or more search queries based at least on a number of experts of the one or more categories that tagged respective ones of the plurality of web documents, wherein a given expert is determined based upon a number of users that agree with the input of one or more tags by the given expert exceeding a threshold value; and generating a list of ranked search results responsive to the one or more search queries. 4. The method of claim 1 wherein generating a list of ranked search results responsive to the one or more search queries further comprises displaying one or more one or more corresponding web documents that are linked to the one or more documents of the result set with the list of search results. | 0.52707 |
8,935,609 | 22 | 23 | 22. The system of claim 21 , wherein the second scripting language code is further adapted to cause a user terminal upon which it is executed to transmit the coordinate information continuously or on a periodic basis. | 22. The system of claim 21 , wherein the second scripting language code is further adapted to cause a user terminal upon which it is executed to transmit the coordinate information continuously or on a periodic basis. 23. The system of claim 22 , wherein the second scripting language code is further adapted to cause a user terminal upon which it is executed to transmit action information defining a clicking action of a user-controlled cursor, and wherein, in response to the clicking action, one of a plurality of browser-based actions is performed based the position of the user controlled cursor within the image as indicated by the coordinate information. | 0.5 |
7,620,245 | 1 | 2 | 1. A method for improving cursive handwriting recognition comprising the steps of: receiving a cursive handwriting input from a user; performing a first same space search against a mixed database having print and cursive handwriting samples to generate a first mixed alternate list of possible matches to the cursive handwriting input; performing a second same space search against a cursive database having cursive samples to generate a first cursive alternate list of possible matches to the cursive handwriting input; merging the first mixed alternate list with the first cursive alternate list to form a first combined alternate list; using the first combined alternate list as a constraint, performing a first dynamic time warp search against the mixed database to generate a second mixed alternate list of possible matches to the cursive handwriting input; using the first combined alternate list as a constraint, performing a second dynamic time warp search against the cursive database to generate a second cursive alternate list of possible matches to the cursive handwriting input; and merging the second mixed alternate list with the second cursive alternate list to form a second combined alternate list. | 1. A method for improving cursive handwriting recognition comprising the steps of: receiving a cursive handwriting input from a user; performing a first same space search against a mixed database having print and cursive handwriting samples to generate a first mixed alternate list of possible matches to the cursive handwriting input; performing a second same space search against a cursive database having cursive samples to generate a first cursive alternate list of possible matches to the cursive handwriting input; merging the first mixed alternate list with the first cursive alternate list to form a first combined alternate list; using the first combined alternate list as a constraint, performing a first dynamic time warp search against the mixed database to generate a second mixed alternate list of possible matches to the cursive handwriting input; using the first combined alternate list as a constraint, performing a second dynamic time warp search against the cursive database to generate a second cursive alternate list of possible matches to the cursive handwriting input; and merging the second mixed alternate list with the second cursive alternate list to form a second combined alternate list. 2. The method of claim 1 , further comprising: making a recognition decision using the second combined alternate list. | 0.569343 |
8,939,837 | 1 | 7 | 1. A system comprising: a memory to store computer instructions; and a processor coupled with the memory, wherein the processor, responsive to executing the computer instructions performs operations comprising: receiving a signal generated in response to a user input at a first accessory operably coupled with a first computing device, wherein the first computing device is programmed to present a video game; identifying matching words between the signal and a plurality of words stored in a multi-lingual library; and providing an audio signal to a second accessory for presentation to an intended recipient, wherein the audio signal is generated based on the received signal using the identified matching words, and wherein the audio signal is associated with the video game. | 1. A system comprising: a memory to store computer instructions; and a processor coupled with the memory, wherein the processor, responsive to executing the computer instructions performs operations comprising: receiving a signal generated in response to a user input at a first accessory operably coupled with a first computing device, wherein the first computing device is programmed to present a video game; identifying matching words between the signal and a plurality of words stored in a multi-lingual library; and providing an audio signal to a second accessory for presentation to an intended recipient, wherein the audio signal is generated based on the received signal using the identified matching words, and wherein the audio signal is associated with the video game. 7. The system of claim 1 , wherein the user input is speech, and wherein the signal is generated based on the speech using speech recognition performed at the first computing device. | 0.756684 |
8,056,048 | 6 | 7 | 6. A method in accordance with claim 1 further including defining a descriptive manifest for said pattern comprising searchable information about said pattern. | 6. A method in accordance with claim 1 further including defining a descriptive manifest for said pattern comprising searchable information about said pattern. 7. A method in accordance with claim 6 further including placing said descriptive manifest in a searchable repository. | 0.5 |
9,430,451 | 1 | 3 | 1. A computer-implemented method for parsing author names in a document, the method comprising: electronically scanning a document that contains an author name text string, the author name text string comprising a set of initials, one or more surnames, and punctuation, and the author name text string comprising at least one author name in non-standardized format; identifying a character sequence in the document as potentially being the author name text string, wherein the identifying is based on: (i) a sequence of title-case words, capital letters, and punctuation in the character sequence, and (ii) the character sequence ending with a recognized indicator; and parsing the identified character sequence and determining whether the identified character sequence is the author name text string, wherein the parsing and determining comprises: updating the identified character sequence by converting any punctuation or whitespace between terms in the character sequence to single space character, and determining whether one or more author names are contained in the updated character sequence by identifying a pattern of surname and set of initials in the updated character sequence, such that an author name in non-standardized format in the document is identified and output in standardized format. | 1. A computer-implemented method for parsing author names in a document, the method comprising: electronically scanning a document that contains an author name text string, the author name text string comprising a set of initials, one or more surnames, and punctuation, and the author name text string comprising at least one author name in non-standardized format; identifying a character sequence in the document as potentially being the author name text string, wherein the identifying is based on: (i) a sequence of title-case words, capital letters, and punctuation in the character sequence, and (ii) the character sequence ending with a recognized indicator; and parsing the identified character sequence and determining whether the identified character sequence is the author name text string, wherein the parsing and determining comprises: updating the identified character sequence by converting any punctuation or whitespace between terms in the character sequence to single space character, and determining whether one or more author names are contained in the updated character sequence by identifying a pattern of surname and set of initials in the updated character sequence, such that an author name in non-standardized format in the document is identified and output in standardized format. 3. The computer-implemented method of claim 1 wherein the method parses more than one author name text strings. | 0.920144 |
8,221,126 | 7 | 8 | 7. The system of claim 6 , wherein the system further comprises a content selection component that is provided to one or more of the plurality of students, wherein the content selection component is configured to enable each of the one or more students to select a content that is to form a basis of the test provided to that student; and wherein, for each of the plurality of students, the test module is configured to generate the test to include the series of questions using the selected content. | 7. The system of claim 6 , wherein the system further comprises a content selection component that is provided to one or more of the plurality of students, wherein the content selection component is configured to enable each of the one or more students to select a content that is to form a basis of the test provided to that student; and wherein, for each of the plurality of students, the test module is configured to generate the test to include the series of questions using the selected content. 8. The system of claim 7 , wherein the test module communicates the series of questions to the user using audio data. | 0.5 |
9,390,377 | 10 | 18 | 10. An article of manufacture for feature extraction, comprising a non-transitory machine-readable recordable medium containing one or more programs which when executed implement the steps of: a) receiving at least one query to predict at least one future value of a given value series; b) generating a statistical model built on covariates to produce at least two predictions of the future value fulfilling at least the properties of 1) each being as statistically probable as possible given the statistical model wherein to be as statistically probable as possible an absolute distance of each of the predictions to a true value is less than a predetermined distance parameter with greater than a predetermined probability and 2) being mutually divert in terms of a numerical distance measure; and c) querying a user to select one of the predictions. | 10. An article of manufacture for feature extraction, comprising a non-transitory machine-readable recordable medium containing one or more programs which when executed implement the steps of: a) receiving at least one query to predict at least one future value of a given value series; b) generating a statistical model built on covariates to produce at least two predictions of the future value fulfilling at least the properties of 1) each being as statistically probable as possible given the statistical model wherein to be as statistically probable as possible an absolute distance of each of the predictions to a true value is less than a predetermined distance parameter with greater than a predetermined probability and 2) being mutually divert in terms of a numerical distance measure; and c) querying a user to select one of the predictions. 18. The article of manufacture of claim 10 , wherein the one or more programs which when executed further implement the steps of: recommending textual annotations from past interactions. | 0.760925 |
10,108,676 | 33 | 35 | 33. The system of claim 20 , further comprising: identifying one or more objects associated with the online social network, each of the identified object corresponding to at least a portion of the text string; accessing the grammar model, wherein the grammar model is a context-free grammar model comprising a plurality of grammars, each grammar comprising one or more tokens; and identifying one or more grammars, each identified grammar having one or more tokens corresponding to at least one of the identified objects. | 33. The system of claim 20 , further comprising: identifying one or more objects associated with the online social network, each of the identified object corresponding to at least a portion of the text string; accessing the grammar model, wherein the grammar model is a context-free grammar model comprising a plurality of grammars, each grammar comprising one or more tokens; and identifying one or more grammars, each identified grammar having one or more tokens corresponding to at least one of the identified objects. 35. The system of claim 33 , wherein the text string comprises one or more n-grams, and wherein each of the identified objects corresponds to at least one of the n-grams. | 0.838403 |
8,739,066 | 6 | 7 | 6. A non-transitory computer-readable medium storing instructions, the instructions comprising: one or more instructions that, when executed by at least one processor, cause the at least one processor to: receive a first document; identify links in a plurality of second documents that each include a link to the first document; determine a second document of the plurality of second documents that has been used to access the first document more frequently than any other second document of the plurality of second documents; extract concepts associated with the determined second document; and associate the extracted concepts with the first document, at least one extracted concept, of the extracted concepts, being associated with anchor text in the determined second document. | 6. A non-transitory computer-readable medium storing instructions, the instructions comprising: one or more instructions that, when executed by at least one processor, cause the at least one processor to: receive a first document; identify links in a plurality of second documents that each include a link to the first document; determine a second document of the plurality of second documents that has been used to access the first document more frequently than any other second document of the plurality of second documents; extract concepts associated with the determined second document; and associate the extracted concepts with the first document, at least one extracted concept, of the extracted concepts, being associated with anchor text in the determined second document. 7. The non-transitory computer-readable medium of claim 6 , where at least one other extracted concept, of the extracted concepts, is associated with an advertisement of the determined second document. | 0.840981 |
10,095,789 | 1 | 2 | 1. A computer-implemented method comprising: obtaining are annotated web Page, from an annotating proxy server, wherein the annotated web page comprises an annotated web-page element, wherein an annotation of the annotated web-page element is included in the annotated web page by an annotating proxy server, and wherein the annotation is stored in a database of web page annotations; annotating the annotated web page to include a query field; receiving a user-generated search query from the query field, wherein the user-generated search query is algorithmically generated from a term of a user annotation of the annotated web page and a user-provided image file, and wherein the term of the user annotation comprises a user comment that has merged into the annotated web page by an annotating proxy server before obtaining the annotated web page; searching the database of web page annotations according to the user-generated search query and an annotated web-page element attribute; determining the annotated web-page element attribute; and annotating the annotated web page with a result of the search of the database of web page annotations according to the user-generated search query and another search of the database of web page annotations based on the user annotation of the search result, wherein the result of the search of the database of web page annotations is merged into a webpage document of the annotated web page; wherein the annotated web page comprises search result of the search the database of web page annotations according to the user-generated search query and another search of the database of web page annotations based on the user annotation of the search result, wherein the least one characteristic of the image file comprises an image content, image metadata and a geolocation data of the image file, and wherein the user-generated search query comprises a user annotation of a search result. | 1. A computer-implemented method comprising: obtaining are annotated web Page, from an annotating proxy server, wherein the annotated web page comprises an annotated web-page element, wherein an annotation of the annotated web-page element is included in the annotated web page by an annotating proxy server, and wherein the annotation is stored in a database of web page annotations; annotating the annotated web page to include a query field; receiving a user-generated search query from the query field, wherein the user-generated search query is algorithmically generated from a term of a user annotation of the annotated web page and a user-provided image file, and wherein the term of the user annotation comprises a user comment that has merged into the annotated web page by an annotating proxy server before obtaining the annotated web page; searching the database of web page annotations according to the user-generated search query and an annotated web-page element attribute; determining the annotated web-page element attribute; and annotating the annotated web page with a result of the search of the database of web page annotations according to the user-generated search query and another search of the database of web page annotations based on the user annotation of the search result, wherein the result of the search of the database of web page annotations is merged into a webpage document of the annotated web page; wherein the annotated web page comprises search result of the search the database of web page annotations according to the user-generated search query and another search of the database of web page annotations based on the user annotation of the search result, wherein the least one characteristic of the image file comprises an image content, image metadata and a geolocation data of the image file, and wherein the user-generated search query comprises a user annotation of a search result. 2. The computer-implemented method of claim 1 further comprising: determining at least one characteristic of the image file. | 0.5 |
8,543,577 | 1 | 8 | 1. A computer-implemented method comprising: receiving, by one or more computer systems, first information from a first type of channel and second information from a second type of channel, with the first type of channel differing from the second type of channel; merging the first information received from the first type of channel with the second information received from the second type of channel that differs from the first type of channel; applying an unsupervised clustering model to the merged information; and generating, based on results of the applying, a cross-channel cluster, the cross-channel cluster comprising (i) a portion of the first information received from the first type of channel associated with a subject matter, and (ii) a portion of the second information received from the second type of channel that differs from the first type of channel associated with the subject matter, wherein the cross-channel cluster comprises a cluster of information that is received from at least two different types of channels. | 1. A computer-implemented method comprising: receiving, by one or more computer systems, first information from a first type of channel and second information from a second type of channel, with the first type of channel differing from the second type of channel; merging the first information received from the first type of channel with the second information received from the second type of channel that differs from the first type of channel; applying an unsupervised clustering model to the merged information; and generating, based on results of the applying, a cross-channel cluster, the cross-channel cluster comprising (i) a portion of the first information received from the first type of channel associated with a subject matter, and (ii) a portion of the second information received from the second type of channel that differs from the first type of channel associated with the subject matter, wherein the cross-channel cluster comprises a cluster of information that is received from at least two different types of channels. 8. The computer-implemented method of claim 1 , further comprising: generating a link between the information associated with the first type of channel in the plurality of channels and the information associated with the second type of channel in the plurality of channels. | 0.648196 |
8,214,351 | 9 | 11 | 9. A computer-implemented method comprising: receiving an abstract rule having a conditional statement and a consequential statement, wherein the consequential statement defines a particular recommendation that is returned when the conditional statement is satisfied, and wherein the conditional statement and the consequential statement are defined using logical field definitions defined in an abstraction model that models underlying physical data in a manner making a schema of the physical data transparent to a user of the abstraction model; determining one or more functions required to evaluate whether a rule predicate of the conditional statement is satisfied; determining one or more rule engines of a plurality of rule engines that include the required functions; grouping the required functions into two or more groupings; selecting, from the one or more rule engines determined to include the required functions, a rule engine to process each grouping of required functions; and executing the selected rule engines to produce an output, wherein each selected rule engine processes the abstract rule according to the corresponding grouping of required functions. | 9. A computer-implemented method comprising: receiving an abstract rule having a conditional statement and a consequential statement, wherein the consequential statement defines a particular recommendation that is returned when the conditional statement is satisfied, and wherein the conditional statement and the consequential statement are defined using logical field definitions defined in an abstraction model that models underlying physical data in a manner making a schema of the physical data transparent to a user of the abstraction model; determining one or more functions required to evaluate whether a rule predicate of the conditional statement is satisfied; determining one or more rule engines of a plurality of rule engines that include the required functions; grouping the required functions into two or more groupings; selecting, from the one or more rule engines determined to include the required functions, a rule engine to process each grouping of required functions; and executing the selected rule engines to produce an output, wherein each selected rule engine processes the abstract rule according to the corresponding grouping of required functions. 11. The method of claim 9 , wherein selecting a rule engine to process each grouping of required functions comprises: determining the rule engine having a lowest monetary cost for processing the required functions. | 0.881898 |
8,112,419 | 1 | 5 | 1. A method of creating a Unified Geographic Database (UGD) by registering a proprietary name for a geographical location of an entity, the method comprising: receiving geographical location information for an entity; receiving a proprietary name for the entity; geocoding the geographical location information into a hierarchical address; storing the proprietary name, hierarchical address, and geographical location information as a record in the Unified Geographic Database; and converting the proprietary name into a domain-name like, proprietary locational address based on the geographical location information, and the proprietary name, and storing the proprietary locational address as the proprietary name in the record, wherein converting the proprietary name into a domain-name like, proprietary locational address includes determining a regional or local grid having a centroid closest to the geographical location of the business or entity, the regional grid represented by an A.B.C naming format, where A represents a country, B represents a state or province, and C represents a city, and prefixing this A.B.C format to the proprietary name to create the domain-name like proprietary locational address. | 1. A method of creating a Unified Geographic Database (UGD) by registering a proprietary name for a geographical location of an entity, the method comprising: receiving geographical location information for an entity; receiving a proprietary name for the entity; geocoding the geographical location information into a hierarchical address; storing the proprietary name, hierarchical address, and geographical location information as a record in the Unified Geographic Database; and converting the proprietary name into a domain-name like, proprietary locational address based on the geographical location information, and the proprietary name, and storing the proprietary locational address as the proprietary name in the record, wherein converting the proprietary name into a domain-name like, proprietary locational address includes determining a regional or local grid having a centroid closest to the geographical location of the business or entity, the regional grid represented by an A.B.C naming format, where A represents a country, B represents a state or province, and C represents a city, and prefixing this A.B.C format to the proprietary name to create the domain-name like proprietary locational address. 5. The method of claim 1 , wherein the proprietary name is registered for a geographical location with a central registrar through an intermediary. | 0.804 |
9,740,680 | 9 | 11 | 9. One or more non-transitory computer storage media encoded with a data set, the data set associating each word in a vocabulary of words with a respective numeric representation of the word in a high-dimensional space, wherein the data set indicates, for each word of a plurality of the words in the vocabulary and by the position of the numeric representation of the word in the high-dimensional space, a semantic meaning of the word, wherein the data set indicates, for each of a plurality of pairs of words in the vocabulary and by the relative positions of the numeric representations of the words in the high-dimensional space, a degree of semantic relationship, syntactic relationship, or both between the words in the pair of words, whereby the non-transitory computer storage media, when encoded with the data set, provides the function of representing in a quantitative way semantic and syntactic relationships between and among words in the vocabulary, and wherein the one or more non-transitory computer storage media are encoded with the data set by a process comprising the steps of: obtaining a set of training data, wherein the set of training data comprises sequences of words; training a classifier and an embedding function on the set of training data, wherein the embedding function receives a plurality of words surrounding an unknown word in a sequence of words and maps the plurality of words into a numeric representation in accordance with a set of embedding function parameters, wherein the classifier processes the numeric representation of the sequence of words to generate a respective word score for each word in a pre-determined set of words, wherein each of the respective word scores measure a predicted likelihood that the corresponding word is the unknown word, and wherein training the embedding function comprises determining trained values of the embedding function parameters; processing each word in the vocabulary using the embedding function in accordance with the trained values of the embedding function parameters to generate a respective numeric representation of each word in the vocabulary in the high-dimensional space; generating the data set by associating each word in the vocabulary with the respective numeric representation of the word in the high-dimensional space; and storing the data set on the one or more non-transitory computer storage media. | 9. One or more non-transitory computer storage media encoded with a data set, the data set associating each word in a vocabulary of words with a respective numeric representation of the word in a high-dimensional space, wherein the data set indicates, for each word of a plurality of the words in the vocabulary and by the position of the numeric representation of the word in the high-dimensional space, a semantic meaning of the word, wherein the data set indicates, for each of a plurality of pairs of words in the vocabulary and by the relative positions of the numeric representations of the words in the high-dimensional space, a degree of semantic relationship, syntactic relationship, or both between the words in the pair of words, whereby the non-transitory computer storage media, when encoded with the data set, provides the function of representing in a quantitative way semantic and syntactic relationships between and among words in the vocabulary, and wherein the one or more non-transitory computer storage media are encoded with the data set by a process comprising the steps of: obtaining a set of training data, wherein the set of training data comprises sequences of words; training a classifier and an embedding function on the set of training data, wherein the embedding function receives a plurality of words surrounding an unknown word in a sequence of words and maps the plurality of words into a numeric representation in accordance with a set of embedding function parameters, wherein the classifier processes the numeric representation of the sequence of words to generate a respective word score for each word in a pre-determined set of words, wherein each of the respective word scores measure a predicted likelihood that the corresponding word is the unknown word, and wherein training the embedding function comprises determining trained values of the embedding function parameters; processing each word in the vocabulary using the embedding function in accordance with the trained values of the embedding function parameters to generate a respective numeric representation of each word in the vocabulary in the high-dimensional space; generating the data set by associating each word in the vocabulary with the respective numeric representation of the word in the high-dimensional space; and storing the data set on the one or more non-transitory computer storage media. 11. The computer storage media of claim 9 , wherein positions of numeric representations in the high-dimensional space reflect semantic similarities between words represented by the numeric representations. | 0.633452 |
9,842,590 | 1 | 9 | 1. A system for analyzing a face-to-face customer-agent communication, comprising: a node comprising a processor and a non-transitory computer readable medium operably coupled thereto, the non-transitory computer readable medium comprising a plurality of instructions stored in association therewith that are accessible to, and executable by, the processor, wherein the plurality of instructions when executed: record a mono recording of a communication between an agent and a customer using a microphone, wherein the mono recording is unseparated and includes agent voice data and customer voice data; separately record the agent voice data in an agent recording using a second microphone; align the unseparated mono recording and the agent recording so they are time-synched; subtract agent voice data from the unseparated mono recording using the agent recording to provide a separated recording including only customer voice data, wherein the agent voice data is subtracted from the unseparated mono recording based on the alignment, sound frequency analysis, or both; convert at least the customer voice data to text; and determine a personality type of the customer by applying one or more computer-implemented linguistic algorithms to the text of the customer voice data. | 1. A system for analyzing a face-to-face customer-agent communication, comprising: a node comprising a processor and a non-transitory computer readable medium operably coupled thereto, the non-transitory computer readable medium comprising a plurality of instructions stored in association therewith that are accessible to, and executable by, the processor, wherein the plurality of instructions when executed: record a mono recording of a communication between an agent and a customer using a microphone, wherein the mono recording is unseparated and includes agent voice data and customer voice data; separately record the agent voice data in an agent recording using a second microphone; align the unseparated mono recording and the agent recording so they are time-synched; subtract agent voice data from the unseparated mono recording using the agent recording to provide a separated recording including only customer voice data, wherein the agent voice data is subtracted from the unseparated mono recording based on the alignment, sound frequency analysis, or both; convert at least the customer voice data to text; and determine a personality type of the customer by applying one or more computer-implemented linguistic algorithms to the text of the customer voice data. 9. The system of claim 1 , which further comprises instructions that, when executed, generate and display on an agent device one or more actionable tasks for the agent based on the personality type of the customer. | 0.5 |
9,998,472 | 16 | 18 | 16. A computer-readable medium having instructions stored thereon, which, when executed by a processor, cause the processor to perform operations, comprising: receiving a query from a member of an enterprise; searching an index that includes member information of members of the enterprise and documents of the enterprise, wherein: the documents include data describing entities and entity facts related to the enterprise and relationships among the entities, and each document has a respective access control list specifying access privileges to the document for members of the enterprise; the entity facts are identified from the entities of the documents of the enterprise; each entity fact describes at least one feature of the entity, wherein the feature of the entity is a relationship between the entity and another entity and wherein each entity fact is derived from one or more corresponding documents in which the entity fact is described; and the index includes data defining access privileges to the data describing the entities and the entity facts according to respective entity fact access control lists, wherein each entity fact access control list is different from the access control lists provided for the documents of the enterprise, and each entity fact inherits an access control list of a document from which the entity fact is derived, wherein deriving the entity facts comprises selecting each document from the documents, and for the selected document: determining a first entity identified within the document; determining a second entity identified within the document; determining a relationship between the first entity and the second entity that is described within the document; and generating, as the entity fact, data describing the first entity, the second entity, and the relationship between the first entity and the second entity as described in the document; wherein multiple entity facts are derived from a selected document; determining the entity facts that are accessible to the member according to the entity fact access control lists; determining, based on member information of the member, and entity facts that are accessible to the member, search result data including data describing entities and entity facts relevant to the query; and providing search results, based on the search result data, to the member of the enterprise, the search results including data describing the entities and entity facts determined to be relevant to the query. | 16. A computer-readable medium having instructions stored thereon, which, when executed by a processor, cause the processor to perform operations, comprising: receiving a query from a member of an enterprise; searching an index that includes member information of members of the enterprise and documents of the enterprise, wherein: the documents include data describing entities and entity facts related to the enterprise and relationships among the entities, and each document has a respective access control list specifying access privileges to the document for members of the enterprise; the entity facts are identified from the entities of the documents of the enterprise; each entity fact describes at least one feature of the entity, wherein the feature of the entity is a relationship between the entity and another entity and wherein each entity fact is derived from one or more corresponding documents in which the entity fact is described; and the index includes data defining access privileges to the data describing the entities and the entity facts according to respective entity fact access control lists, wherein each entity fact access control list is different from the access control lists provided for the documents of the enterprise, and each entity fact inherits an access control list of a document from which the entity fact is derived, wherein deriving the entity facts comprises selecting each document from the documents, and for the selected document: determining a first entity identified within the document; determining a second entity identified within the document; determining a relationship between the first entity and the second entity that is described within the document; and generating, as the entity fact, data describing the first entity, the second entity, and the relationship between the first entity and the second entity as described in the document; wherein multiple entity facts are derived from a selected document; determining the entity facts that are accessible to the member according to the entity fact access control lists; determining, based on member information of the member, and entity facts that are accessible to the member, search result data including data describing entities and entity facts relevant to the query; and providing search results, based on the search result data, to the member of the enterprise, the search results including data describing the entities and entity facts determined to be relevant to the query. 18. The computer-readable medium of claim 16 , further comprising: ranking the search result data based on a relevance of the entities and entity facts in relation to the query and member information of the member; and providing the search results, based on the search result data, in an order of the rankings to the member of the enterprise including data describing the entities and entity facts determined to be relevant to the query. | 0.5 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.