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8,051,139 | 1 | 2 | 1. A non-transitory computer-readable medium encoding instructions which, when executed by a computer system, cause the computer system to: parse an electronic text document to generate a document vector for the electronic text document, wherein the document vector includes a feature count component and a feature position component, wherein the feature count component includes a plurality of feature count indicators for the electronic text document, wherein the feature position component includes a data structure selected from a group consisting of an ordered list and a tree of document substructure indicators, each document substructure indicator denoting a type of substructure in the electronic text document, and wherein a position of said each document substructure indicator in the data structure characterizes a position of a corresponding substructure in the electronic text document; determine a plurality of composite hyperspace distances between the document vector and a plurality of reference vectors, each composite hyperspace distance being defined between the document vector and a reference vector of the plurality of reference vectors, wherein each composite hyperspace distance is a function of a Euclidean-space distance dependent on the feature count component of the document vector and of an edit distance dependent on the feature position component of the document vector; and classify the electronic text document according to at least one of the plurality of composite hyperspace distances. | 1. A non-transitory computer-readable medium encoding instructions which, when executed by a computer system, cause the computer system to: parse an electronic text document to generate a document vector for the electronic text document, wherein the document vector includes a feature count component and a feature position component, wherein the feature count component includes a plurality of feature count indicators for the electronic text document, wherein the feature position component includes a data structure selected from a group consisting of an ordered list and a tree of document substructure indicators, each document substructure indicator denoting a type of substructure in the electronic text document, and wherein a position of said each document substructure indicator in the data structure characterizes a position of a corresponding substructure in the electronic text document; determine a plurality of composite hyperspace distances between the document vector and a plurality of reference vectors, each composite hyperspace distance being defined between the document vector and a reference vector of the plurality of reference vectors, wherein each composite hyperspace distance is a function of a Euclidean-space distance dependent on the feature count component of the document vector and of an edit distance dependent on the feature position component of the document vector; and classify the electronic text document according to at least one of the plurality of composite hyperspace distances. 2. The computer-readable medium of claim 1 , wherein the Euclidean-space distance is a Euclidean distance or a Manhattan distance. | 0.80597 |
9,741,142 | 13 | 15 | 13. The apparatus of claim 12 , further comprising instructions that, when executed by the one or more processors, cause the computer to create the synthesized digital font by: extracting the original set of characters from the scanned document; extracting a monochrome pixmap for each extracted original character; merging similar monochrome pixmaps that are determined to most close match a same system font; and creating a vector outline for each character in the set of original characters. | 13. The apparatus of claim 12 , further comprising instructions that, when executed by the one or more processors, cause the computer to create the synthesized digital font by: extracting the original set of characters from the scanned document; extracting a monochrome pixmap for each extracted original character; merging similar monochrome pixmaps that are determined to most close match a same system font; and creating a vector outline for each character in the set of original characters. 15. The apparatus of claim 13 , further comprising instructions that, when executed by the one or more processors, cause the computer to merge similar monochrome pixmaps by: determining two or more monochrome pixmaps that are determined to be most closely matched to a same system font; and combining and upscaling the two or more monochrome pixmaps. | 0.725275 |
7,580,839 | 1 | 7 | 1. A speech processing apparatus comprising: a speech storage configured to store a plurality of speech units of a conversion-source speaker and source-speaker attribute information corresponding to the speech units; a speech-unit extractor configured to divide the speech of a conversion-target speaker into a predetermined type of a speech unit to form target-speaker speech units; an attribute-information generator configured to generate target-speaker attribute information corresponding to the target-speaker speech units from the speech of the conversion-target speaker or linguistic information of the speech; a speech-unit selector configured to calculate costs on the target-speaker attribute information and the source-speaker attribute information using cost functions, and selects one or a plurality of speech units with the same phoneme from the speech storage according to the costs to form a source-speaker speech unit; and a voice-conversion-rule generator configured to generate speech conversion functions for converting the one or the plurality of source-speaker speech units to the target-speaker speech units based on the target-speaker speech units and the one or the plurality of source-speakerspeech units. | 1. A speech processing apparatus comprising: a speech storage configured to store a plurality of speech units of a conversion-source speaker and source-speaker attribute information corresponding to the speech units; a speech-unit extractor configured to divide the speech of a conversion-target speaker into a predetermined type of a speech unit to form target-speaker speech units; an attribute-information generator configured to generate target-speaker attribute information corresponding to the target-speaker speech units from the speech of the conversion-target speaker or linguistic information of the speech; a speech-unit selector configured to calculate costs on the target-speaker attribute information and the source-speaker attribute information using cost functions, and selects one or a plurality of speech units with the same phoneme from the speech storage according to the costs to form a source-speaker speech unit; and a voice-conversion-rule generator configured to generate speech conversion functions for converting the one or the plurality of source-speaker speech units to the target-speaker speech units based on the target-speaker speech units and the one or the plurality of source-speakerspeech units. 7. The apparatus according to claim 1 , further comprising: a voice converter configured to convert the voice quality of the speech of the conversion-source speaker using the voice conversion function. | 0.887079 |
8,146,156 | 31 | 34 | 31. A computer-implemented method, comprising: receiving, at an information-capturing system, a text capture indication corresponding to a text capture operation performed on a rendered document that captured less than a whole page of text; the information-capturing system generating a text entry based upon said text capture indication, wherein the text entry specifies a number of documents matching text of the text capture operation; and the information-capturing system publishing the text entry. | 31. A computer-implemented method, comprising: receiving, at an information-capturing system, a text capture indication corresponding to a text capture operation performed on a rendered document that captured less than a whole page of text; the information-capturing system generating a text entry based upon said text capture indication, wherein the text entry specifies a number of documents matching text of the text capture operation; and the information-capturing system publishing the text entry. 34. The method of claim 31 , wherein publishing the text entry comprises posting at least part of the text entry to at least one of a weblog, wiki, micro-blog, news feed, photo-stream, and a website. | 0.587137 |
8,990,211 | 15 | 21 | 15. An entity management system comprising one or more computers and one or more storage devices storing instructions that, when executed by the one or more computers, cause the one or more computers to perform operations comprising: generating, by one or more processors of the entity management system, a user interface document that, when rendered by a user device, presents a plurality of attribute values associated with an entity to a user and allows the user to modify one or more of the plurality of attribute values; generating, by one or more processors of the entity management system, an immutable observation that includes a user-modified value of one of the plurality of attribute values and a context, wherein the context is generated based on one or more of the plurality of attribute values sent to the user device for presentation, wherein the immutable observation is not modifiable after generation of the immutable observation; identifying, by one or more processors of the entity management system, a cluster of immutable observations that represent the entity using the context; associating, by one or more processors of the entity management system, the immutable observation with the cluster that represents the entity; and sending, by one or more processors of the entity management system, the cluster to a summarization system, wherein the summarization system determines a summarized cluster to represent the current state of the entity, the summarized cluster comprising a subset of the cluster of immutable observations. | 15. An entity management system comprising one or more computers and one or more storage devices storing instructions that, when executed by the one or more computers, cause the one or more computers to perform operations comprising: generating, by one or more processors of the entity management system, a user interface document that, when rendered by a user device, presents a plurality of attribute values associated with an entity to a user and allows the user to modify one or more of the plurality of attribute values; generating, by one or more processors of the entity management system, an immutable observation that includes a user-modified value of one of the plurality of attribute values and a context, wherein the context is generated based on one or more of the plurality of attribute values sent to the user device for presentation, wherein the immutable observation is not modifiable after generation of the immutable observation; identifying, by one or more processors of the entity management system, a cluster of immutable observations that represent the entity using the context; associating, by one or more processors of the entity management system, the immutable observation with the cluster that represents the entity; and sending, by one or more processors of the entity management system, the cluster to a summarization system, wherein the summarization system determines a summarized cluster to represent the current state of the entity, the summarized cluster comprising a subset of the cluster of immutable observations. 21. The entity management system of claim 15 , wherein the entity comprises an entity having a geographic location. | 0.914179 |
8,527,276 | 6 | 7 | 6. The method of claim 1 , wherein the identified phonetic speech unit is a phoneme, and the plurality of phonetic speech units is a corresponding plurality of phonemes, and wherein the data indicating the phonetic context of the identified phonetic speech unit comprises at least one label identifying a temporally-prior phoneme of the plurality of phonemes, and at least one label identifying a temporally-subsequent phoneme of the plurality of phonemes. | 6. The method of claim 1 , wherein the identified phonetic speech unit is a phoneme, and the plurality of phonetic speech units is a corresponding plurality of phonemes, and wherein the data indicating the phonetic context of the identified phonetic speech unit comprises at least one label identifying a temporally-prior phoneme of the plurality of phonemes, and at least one label identifying a temporally-subsequent phoneme of the plurality of phonemes. 7. The method of claim 6 , wherein the data indicating the phonetic context of the identified phonetic speech unit further comprises data indicating a classifying characteristic of the identified phoneme, the classifying characteristic being at least one of nasal, fricative, vowel, linguistic context, or part of speech. | 0.5 |
7,533,023 | 8 | 21 | 8. A speech processing system customizing speech parameters across a networked environment, comprising: at least one of a first speech recognizer and a first speaker recognizer residing on a first computing device, the at least one first speech recognizer and first speaker recognizer capturing customized speech parameters for a given speaker and communicate the customized speech parameters across a network; and an intermediary speech processor residing on a second computing device, the second computing device being interconnected by the network to the first computing device; said intermediary speech processor receiving customized speech parameters, said intermediary speech processor retrieving one or more device parameters for a third computing device from a device parameter data store and transform the customized speech parameters for use on the third computing device based on the one or more device parameters for the third computing device, the device parameters selected from either available memory space on the third computing device or the available processing resources of the third computing device. | 8. A speech processing system customizing speech parameters across a networked environment, comprising: at least one of a first speech recognizer and a first speaker recognizer residing on a first computing device, the at least one first speech recognizer and first speaker recognizer capturing customized speech parameters for a given speaker and communicate the customized speech parameters across a network; and an intermediary speech processor residing on a second computing device, the second computing device being interconnected by the network to the first computing device; said intermediary speech processor receiving customized speech parameters, said intermediary speech processor retrieving one or more device parameters for a third computing device from a device parameter data store and transform the customized speech parameters for use on the third computing device based on the one or more device parameters for the third computing device, the device parameters selected from either available memory space on the third computing device or the available processing resources of the third computing device. 21. The speech processing system of claim 8 wherein the customized speech parameters are further defined as a plurality of acoustic models employed by the first speech recognizer, such that the intermediary speech processor modifies the plurality of acoustic models based on an operational characteristic of the third computing device. | 0.562663 |
9,262,126 | 1 | 5 | 1. A system for generating requirements specification to develop functionalities of a software, the system comprising: a processor; and a memory coupled to the processor, wherein the memory comprises, a mapping module configured to map knowledge elements of at least one ontology instance for a domain, selected from among a plurality of ontology instances in a knowledge base, with another ontology instance, selected from among the plurality of ontology instances based on an input received from a user and wherein the ontology instance defines a common vocabulary, in a machine-readable and processable format, for users who share information within the domain associated with the software being developed, for which the requirements specification is being generated, wherein the mapping module includes a plurality of bridge classes to specify semantic mappings of conclusions drawn from at least one said plurality of ontology instances; and an agile recommendation module configured to, generate a knowledge corpus from the knowledge base based on the mapped knowledge elements and one or more environmental parameters selected by the user, wherein: the one or more environmental parameters are selected to define requirements for developing functionalities of the software; the one or more environmental parameters include a geographic region; and the knowledge corpus includes features and associated knowledge elements based on the selected one or more environmental parameters; receive at least one modification in the knowledge corpus based on a selection of one or more of the features and the associated knowledge elements in the knowledge corpus by the user, wherein the selection includes one of a modification and enhancement of the features and the associated knowledge elements; generate recommendations specific to the domain from the knowledge base based on the received at least one modification, said generation facilitated by using a plurality of lexical decomposition techniques to resolve a plurality of requirement descriptions; and generate the requirements specification for the domain by incorporating the user's response to the recommendations, wherein the requirements specification indicates prioritization of a plurality of iterations, the plurality of iterations being indicative of logical and coherent units of functionalities of the software to be developed; and an extraction module configured to, apply at least one inference rule on the plurality of ontology instances in the knowledge base to identify an ontology instance from among the plurality of ontology instances based on the selected one or more environmental parameters; and extract semantic inferences from the identified ontology instance by matching the selected one or more environmental parameters and the mapped knowledge elements to provide the knowledge corpus. | 1. A system for generating requirements specification to develop functionalities of a software, the system comprising: a processor; and a memory coupled to the processor, wherein the memory comprises, a mapping module configured to map knowledge elements of at least one ontology instance for a domain, selected from among a plurality of ontology instances in a knowledge base, with another ontology instance, selected from among the plurality of ontology instances based on an input received from a user and wherein the ontology instance defines a common vocabulary, in a machine-readable and processable format, for users who share information within the domain associated with the software being developed, for which the requirements specification is being generated, wherein the mapping module includes a plurality of bridge classes to specify semantic mappings of conclusions drawn from at least one said plurality of ontology instances; and an agile recommendation module configured to, generate a knowledge corpus from the knowledge base based on the mapped knowledge elements and one or more environmental parameters selected by the user, wherein: the one or more environmental parameters are selected to define requirements for developing functionalities of the software; the one or more environmental parameters include a geographic region; and the knowledge corpus includes features and associated knowledge elements based on the selected one or more environmental parameters; receive at least one modification in the knowledge corpus based on a selection of one or more of the features and the associated knowledge elements in the knowledge corpus by the user, wherein the selection includes one of a modification and enhancement of the features and the associated knowledge elements; generate recommendations specific to the domain from the knowledge base based on the received at least one modification, said generation facilitated by using a plurality of lexical decomposition techniques to resolve a plurality of requirement descriptions; and generate the requirements specification for the domain by incorporating the user's response to the recommendations, wherein the requirements specification indicates prioritization of a plurality of iterations, the plurality of iterations being indicative of logical and coherent units of functionalities of the software to be developed; and an extraction module configured to, apply at least one inference rule on the plurality of ontology instances in the knowledge base to identify an ontology instance from among the plurality of ontology instances based on the selected one or more environmental parameters; and extract semantic inferences from the identified ontology instance by matching the selected one or more environmental parameters and the mapped knowledge elements to provide the knowledge corpus. 5. The system as claimed in claim 1 , wherein the plurality of ontology instances comprises a Problem Domain Ontology Instance, an Agile Requirements Ontology Instance, a Requirements Ontology Instance, and an Environmental Context Ontology Instance. | 0.5 |
8,826,123 | 1 | 7 | 1. A non-transitory computer-readable medium having stored thereon instructions that, when executed, provide a method of displaying documents, the method comprising: (a) displaying an axis of an array of documents at separate display times in different manners by switching between two different types of timescale used for a timeline of the axis, the two different types of timescale comprising a linear timescale of the displayed axis and a nonlinear timescale of the displayed axis, the step of displaying including (i) displaying the axis with the timeline having a linear timescale by displaying documents of the axis along the timeline at substantially variable intervals of distance between adjacent documents on the axis, the timescale of the timeline having a time distribution that is substantially constant, with substantially equal periods of time being visually represented in substantially equal lengths of distance along the axis; and (ii) displaying the axis with the timeline having a non-linear timescale by displaying documents of the axis along the timeline at substantially constant intervals of distance between adjacent documents on the axis, the timescale having a time distribution that is substantially variable, with substantially equal periods of time not being visually represented in substantially equal lengths of distance along the axis; (b) wherein the switching of the type of timescale of the axis between linear and nonlinear timescales in displaying documents of the axis to the user is effected by input from the user. | 1. A non-transitory computer-readable medium having stored thereon instructions that, when executed, provide a method of displaying documents, the method comprising: (a) displaying an axis of an array of documents at separate display times in different manners by switching between two different types of timescale used for a timeline of the axis, the two different types of timescale comprising a linear timescale of the displayed axis and a nonlinear timescale of the displayed axis, the step of displaying including (i) displaying the axis with the timeline having a linear timescale by displaying documents of the axis along the timeline at substantially variable intervals of distance between adjacent documents on the axis, the timescale of the timeline having a time distribution that is substantially constant, with substantially equal periods of time being visually represented in substantially equal lengths of distance along the axis; and (ii) displaying the axis with the timeline having a non-linear timescale by displaying documents of the axis along the timeline at substantially constant intervals of distance between adjacent documents on the axis, the timescale having a time distribution that is substantially variable, with substantially equal periods of time not being visually represented in substantially equal lengths of distance along the axis; (b) wherein the switching of the type of timescale of the axis between linear and nonlinear timescales in displaying documents of the axis to the user is effected by input from the user. 7. The non-transitory computer-readable medium of claim 1 having stored thereon instructions that, when executed, provide a method of displaying documents, wherein the step of the method of determining a manner in which an array of documents are to be displayed along an axis having a timeline having a timescale comprises receiving user input representing a selection of the manner in which an array of documents are displayed along an axis having a timeline having a timescale, the selection indicating whether the timeline has a linear timescale or a non-linear timescale. | 0.597339 |
9,934,219 | 12 | 13 | 12. The computer program product of claim 8 , wherein the program instructions to score each of the plurality of keywords comprise: program instructions to retrieve a level of recognition of the plurality of keywords and a level of relevance of the plurality of keywords, wherein the level of recognition of the plurality of keywords is based in part on the native language of a user; program instructions to generate a list of results, wherein each keyword from the list of results is associated with a recognition value score. | 12. The computer program product of claim 8 , wherein the program instructions to score each of the plurality of keywords comprise: program instructions to retrieve a level of recognition of the plurality of keywords and a level of relevance of the plurality of keywords, wherein the level of recognition of the plurality of keywords is based in part on the native language of a user; program instructions to generate a list of results, wherein each keyword from the list of results is associated with a recognition value score. 13. The computer program product of claim 12 , further comprising: program instructions to generate a list of results, wherein each keyword from the list of results is associated with a recognition value score. | 0.5 |
7,644,356 | 1 | 14 | 1. A machine-implemented method of albuming graphic elements, comprising: identifying candidate relative layouts of graphic elements on a page, wherein each of the candidate relative layouts describes a respective set of layout relationships among the graphic elements; generating for each of the candidate relative layouts a respective set of constraints describing the corresponding set of layout relationships among the graphic elements; determining a respective determinate layout of the graphic elements on the page from each set of constraints; and selecting one of the determinate layouts as a final layout of the graphic elements on the page. | 1. A machine-implemented method of albuming graphic elements, comprising: identifying candidate relative layouts of graphic elements on a page, wherein each of the candidate relative layouts describes a respective set of layout relationships among the graphic elements; generating for each of the candidate relative layouts a respective set of constraints describing the corresponding set of layout relationships among the graphic elements; determining a respective determinate layout of the graphic elements on the page from each set of constraints; and selecting one of the determinate layouts as a final layout of the graphic elements on the page. 14. The method of claim 1 , wherein the generating comprises generating constraints specifying minimal dimensions for at least one of the graphic elements. | 0.788251 |
9,471,627 | 1 | 2 | 1. A computer implemented natural language processing method for resolving partial matches, comprising: receiving, using a computer processor, a natural language input query that does not fully specify an entity; tokenizing, using the computer processor, the input query into a constituent set of query tokens; searching, using the computer processor, an entity index by comparing the query tokens to contents of the index, the contents representing a plurality of entities, each of which is tokenized into a constituent set of entity tokens associated with the tokenized entity; identifying, using the computer processor, a plurality of partial match query tokens from the set of query tokens, each partial match query token matching at least one entity token in the index; determining, using the computer processor, whether a sequential break exists in the input query between the partial match query tokens; for each partial match query token, determining, using the computer processor, the entity corresponding to each partial match query token by identifying the entity associated with each entity token in the index that matches the partial match query token; determining, using the computer processor, whether there is an intersection between the identified entities corresponding to the partial match query tokens; and when a sequential break exists in the input query between the partial match query tokens and there is no intersection between the identified entities corresponding to the partial match query tokens determining, using the computer processor, that the input query relates to a plurality of entities, and presenting a response to the received natural language input query to a user based upon the identified entities. | 1. A computer implemented natural language processing method for resolving partial matches, comprising: receiving, using a computer processor, a natural language input query that does not fully specify an entity; tokenizing, using the computer processor, the input query into a constituent set of query tokens; searching, using the computer processor, an entity index by comparing the query tokens to contents of the index, the contents representing a plurality of entities, each of which is tokenized into a constituent set of entity tokens associated with the tokenized entity; identifying, using the computer processor, a plurality of partial match query tokens from the set of query tokens, each partial match query token matching at least one entity token in the index; determining, using the computer processor, whether a sequential break exists in the input query between the partial match query tokens; for each partial match query token, determining, using the computer processor, the entity corresponding to each partial match query token by identifying the entity associated with each entity token in the index that matches the partial match query token; determining, using the computer processor, whether there is an intersection between the identified entities corresponding to the partial match query tokens; and when a sequential break exists in the input query between the partial match query tokens and there is no intersection between the identified entities corresponding to the partial match query tokens determining, using the computer processor, that the input query relates to a plurality of entities, and presenting a response to the received natural language input query to a user based upon the identified entities. 2. The computer-implemented method according to claim 1 , further comprising: when there is an intersection between the identified entities corresponding to the partial match query tokens resolving and reducing the identified entities into a single entity, and presenting a response to the received natural language input query to the user based upon the single entity. | 0.547794 |
9,940,476 | 6 | 7 | 6. A computer system for selective exposure of document tags associated with a plurality of online search engine content based on a predetermined user criteria to facilitate selective exposure of documents by specifying additional qualifications on a search tag, the computer system comprising: one or more processors, one or more computer-readable memories, one or more computer-readable tangible storage devices, and program instructions stored on at least one of the one or more storage devices for execution by at least one of the one or more processors via at least one of the one or more memories, wherein the computer system is capable of performing a method comprising: adding, by a computing device, a content tag associated with the plurality of search engine content with a plurality of metadata, wherein the plurality of metadata includes a text and an access control, wherein the text comprises at least one of a phrase, a keyword, and a string, and wherein the access control is a group of users that is defined by a criteria comprising of at least one of a membership, a credential, an age group, and an authentication; accepting, by the computing device, key words and a user group id, wherein the user group id corresponds to the access control; searching, by the computing device, the plurality of online search engine content with key words; collecting, by the computing device, the searched plurality of online search engine content, wherein the key words match the content tag; removing, by the computing device, the collected plurality of online search engine content that is not associated with the user group id to create a filtered list; and returning, by the computing device, the filtered list. | 6. A computer system for selective exposure of document tags associated with a plurality of online search engine content based on a predetermined user criteria to facilitate selective exposure of documents by specifying additional qualifications on a search tag, the computer system comprising: one or more processors, one or more computer-readable memories, one or more computer-readable tangible storage devices, and program instructions stored on at least one of the one or more storage devices for execution by at least one of the one or more processors via at least one of the one or more memories, wherein the computer system is capable of performing a method comprising: adding, by a computing device, a content tag associated with the plurality of search engine content with a plurality of metadata, wherein the plurality of metadata includes a text and an access control, wherein the text comprises at least one of a phrase, a keyword, and a string, and wherein the access control is a group of users that is defined by a criteria comprising of at least one of a membership, a credential, an age group, and an authentication; accepting, by the computing device, key words and a user group id, wherein the user group id corresponds to the access control; searching, by the computing device, the plurality of online search engine content with key words; collecting, by the computing device, the searched plurality of online search engine content, wherein the key words match the content tag; removing, by the computing device, the collected plurality of online search engine content that is not associated with the user group id to create a filtered list; and returning, by the computing device, the filtered list. 7. The computer system of claim 6 , wherein the selective exposure of document tags is associated with at least one of a search, an access, and a feed associated with an online search engine. | 0.709726 |
9,110,971 | 6 | 7 | 6. The computer-based system of claim 5 , wherein the IPC-overlap of a given patent document is an average of the IPC-overlap scores between the IPC codes of that patent document and all the IPC codes of the initial high-ranking set of patent documents. | 6. The computer-based system of claim 5 , wherein the IPC-overlap of a given patent document is an average of the IPC-overlap scores between the IPC codes of that patent document and all the IPC codes of the initial high-ranking set of patent documents. 7. The computer-based system of claim 6 , wherein an IPC-overlap score for a patent document is directly related to a relevance score of said patent document. | 0.5 |
9,545,579 | 15 | 16 | 15. The computer-readable non-transitory medium storing the program according to claim 1 , wherein the computer is caused to further execute a detection of a state of the game which requires preferential display of the specific character, and the determination includes: a first determination that determines the preferential display character according to the judgment result by the judgment when the state which requires preferential display of the specific character is not detected by the detection, and a second determination that determines the specific character as the preferential display character when the state which requires preferential display of the specific character is detected by the detection. | 15. The computer-readable non-transitory medium storing the program according to claim 1 , wherein the computer is caused to further execute a detection of a state of the game which requires preferential display of the specific character, and the determination includes: a first determination that determines the preferential display character according to the judgment result by the judgment when the state which requires preferential display of the specific character is not detected by the detection, and a second determination that determines the specific character as the preferential display character when the state which requires preferential display of the specific character is detected by the detection. 16. The computer-readable non-transitory medium storing the program according to claim 15 , wherein the detection involves detecting the state which requires preferential display of the specific character when the specific character is within a predetermined range from a goal of the game. | 0.5 |
9,298,852 | 1 | 10 | 1. A system comprising: one or more computers and one or more storage devices storing instructions that are operable, when executed by the one or more computers, to cause the one or more computers to perform operations comprising: receiving a query prefix from a user; obtaining query completions for the query prefix, each query completion having a respective ranking score, the query completions having a first ranking based on the ranking scores; obtaining a reference query for the user; identifying matching user activity sessions, the matching user activity sessions being user activity session that each include an occurrence of the reference query; identifying one or more likely queries, the likely queries being queries that occur in the matching user activity sessions, wherein each of the likely queries is associated with a respective likelihood score, wherein the likelihood score represents a likelihood of the likely query occurring in the matching user activity sessions relative to a likelihood of the likely query occurring over all user activity sessions; designating, as a matching query completion, a first query completion of the query completions that matches a first likely query of the one or more likely queries; boosting the ranking score for the matching query completion by an amount based on the likelihood score associated with the first likely query; determining a modified ranking of the query completions using the boosted ranking score of the matching query completion; and providing the modified ranking of the query completions in response to receiving the query prefix. | 1. A system comprising: one or more computers and one or more storage devices storing instructions that are operable, when executed by the one or more computers, to cause the one or more computers to perform operations comprising: receiving a query prefix from a user; obtaining query completions for the query prefix, each query completion having a respective ranking score, the query completions having a first ranking based on the ranking scores; obtaining a reference query for the user; identifying matching user activity sessions, the matching user activity sessions being user activity session that each include an occurrence of the reference query; identifying one or more likely queries, the likely queries being queries that occur in the matching user activity sessions, wherein each of the likely queries is associated with a respective likelihood score, wherein the likelihood score represents a likelihood of the likely query occurring in the matching user activity sessions relative to a likelihood of the likely query occurring over all user activity sessions; designating, as a matching query completion, a first query completion of the query completions that matches a first likely query of the one or more likely queries; boosting the ranking score for the matching query completion by an amount based on the likelihood score associated with the first likely query; determining a modified ranking of the query completions using the boosted ranking score of the matching query completion; and providing the modified ranking of the query completions in response to receiving the query prefix. 10. The system of claim 1 , wherein identifying the one or more likely queries comprises obtaining a precomputed distribution of one or more likely queries for the reference query. | 0.744318 |
7,606,716 | 10 | 11 | 10. The system of claim 8 , wherein the first sender matrix encoder is arranged to receive the plurality of surround audio channels as input and is configured to output two channels of matrix encoded surround audio. | 10. The system of claim 8 , wherein the first sender matrix encoder is arranged to receive the plurality of surround audio channels as input and is configured to output two channels of matrix encoded surround audio. 11. The system of claim 10 , wherein the sender encoder is adapted to compress the two channels of matrix encoded surround audio and the plurality of dialog channels into the second pair of encoded channels for transmission by the transmission system. | 0.5 |
9,305,090 | 1 | 4 | 1. A non-transitory computer-readable medium having a plurality of computer instructions executable in a computing device, wherein, when executed, the plurality of computer instructions causes the computing device to: periodically provide user input to a server, the user input entered into a search query form; request a plurality of suggested keywords from the server in response to the user input, wherein at least one of the plurality of suggested keywords is based at least in part on a shopping history associated with a user account corresponding to an electronic commerce application, and wherein at least one of the plurality of suggested keywords comprises at least one enhanced suggested keyword, the at least one enhanced suggested keyword including at least one spelling correction to the user input; provide a number of a plurality of speculative search queries to the server, wherein individual ones of the plurality of speculative search queries include at least one of the plurality of suggested keywords, and wherein the number of the plurality of speculative search queries is based at least in part on a length of time that the user account has been associated with the electronic commerce application, and wherein individual ones of the plurality of speculative search queries that include the at least one of the plurality of suggested keywords that comprise the at least one enhanced suggested keyword are weighted higher than individual ones of the plurality of speculative search queries that fail to include the at least one of the plurality of suggested keywords that comprise the at least one enhanced suggested keyword, wherein the weights of the suggested keywords are used to prefer the at least one of the plurality of suggested keywords that comprise the at least one enhanced suggested keyword over individual ones of the plurality of speculative search queries that fail to include the at least one of the plurality of suggested keywords that comprise the at least one enhanced suggested keyword when determining suggested keywords to include in the speculative search queries; process a plurality of responses, individual ones of the plurality of responses corresponding to at least one of the plurality of speculative search queries, the individual ones of the plurality of responses including a corresponding plurality of speculative search results; render, in a hidden portion of a browser window, at least a portion of the plurality of speculative search results from more than one of the plurality of responses, wherein at least one of the more than one of the plurality of responses corresponds to the at least one of the plurality of speculative search queries that includes the at least one of the plurality of suggested keywords that comprises the at least one enhanced suggested keyword; move the rendered portion of at least two of the plurality of speculative search results from the hidden portion of the browser window to a visible portion of the browser window; render the at least a portion of the plurality of speculative search results in the visible portion of the browser window in response to receiving a user instruction to execute a committed search query that includes a suggested keyword in at least one of the plurality of speculative queries, wherein the visible portion of the browser window is separate from the hidden portion of the browser window; request a remaining portion of at least one of the speculative search results; and render, in the visible portion of the browser window, the remaining portion of at least one of the speculative search results. | 1. A non-transitory computer-readable medium having a plurality of computer instructions executable in a computing device, wherein, when executed, the plurality of computer instructions causes the computing device to: periodically provide user input to a server, the user input entered into a search query form; request a plurality of suggested keywords from the server in response to the user input, wherein at least one of the plurality of suggested keywords is based at least in part on a shopping history associated with a user account corresponding to an electronic commerce application, and wherein at least one of the plurality of suggested keywords comprises at least one enhanced suggested keyword, the at least one enhanced suggested keyword including at least one spelling correction to the user input; provide a number of a plurality of speculative search queries to the server, wherein individual ones of the plurality of speculative search queries include at least one of the plurality of suggested keywords, and wherein the number of the plurality of speculative search queries is based at least in part on a length of time that the user account has been associated with the electronic commerce application, and wherein individual ones of the plurality of speculative search queries that include the at least one of the plurality of suggested keywords that comprise the at least one enhanced suggested keyword are weighted higher than individual ones of the plurality of speculative search queries that fail to include the at least one of the plurality of suggested keywords that comprise the at least one enhanced suggested keyword, wherein the weights of the suggested keywords are used to prefer the at least one of the plurality of suggested keywords that comprise the at least one enhanced suggested keyword over individual ones of the plurality of speculative search queries that fail to include the at least one of the plurality of suggested keywords that comprise the at least one enhanced suggested keyword when determining suggested keywords to include in the speculative search queries; process a plurality of responses, individual ones of the plurality of responses corresponding to at least one of the plurality of speculative search queries, the individual ones of the plurality of responses including a corresponding plurality of speculative search results; render, in a hidden portion of a browser window, at least a portion of the plurality of speculative search results from more than one of the plurality of responses, wherein at least one of the more than one of the plurality of responses corresponds to the at least one of the plurality of speculative search queries that includes the at least one of the plurality of suggested keywords that comprises the at least one enhanced suggested keyword; move the rendered portion of at least two of the plurality of speculative search results from the hidden portion of the browser window to a visible portion of the browser window; render the at least a portion of the plurality of speculative search results in the visible portion of the browser window in response to receiving a user instruction to execute a committed search query that includes a suggested keyword in at least one of the plurality of speculative queries, wherein the visible portion of the browser window is separate from the hidden portion of the browser window; request a remaining portion of at least one of the speculative search results; and render, in the visible portion of the browser window, the remaining portion of at least one of the speculative search results. 4. The non-transitory computer-readable medium of claim 1 , wherein the number of the plurality of speculative search queries also varies in accordance with a number of characters in a user input provided to the server. | 0.618467 |
9,807,205 | 1 | 4 | 1. A computer-implemented method for efficient packet forwarding, the method comprising: storing, in a storage device in a first node, a static dictionary comprising a mapping between a type and length (TL) string and a compressed replacement string; in response to identifying the TL string in a packet, replacing the TL string with the compressed replacement string, wherein the packet is a content-centric network (CCN) message, and wherein a name for the CCN message is a hierarchically structured variable length identifier (HSVLI) which comprises contiguous name components ordered from a most general level to a most specific level; replacing a fixed header of the CCN message with a compressed fixed header; and transmitting the packet to a second node storing the static dictionary in a local storage device, thereby facilitating compression of a TL string. | 1. A computer-implemented method for efficient packet forwarding, the method comprising: storing, in a storage device in a first node, a static dictionary comprising a mapping between a type and length (TL) string and a compressed replacement string; in response to identifying the TL string in a packet, replacing the TL string with the compressed replacement string, wherein the packet is a content-centric network (CCN) message, and wherein a name for the CCN message is a hierarchically structured variable length identifier (HSVLI) which comprises contiguous name components ordered from a most general level to a most specific level; replacing a fixed header of the CCN message with a compressed fixed header; and transmitting the packet to a second node storing the static dictionary in a local storage device, thereby facilitating compression of a TL string. 4. The method of claim 1 , wherein the TL string includes contiguous TL pairs without an intermediate value. | 0.907692 |
7,934,236 | 1 | 7 | 1. A content navigation method on a mobile device including a control switch, a selection switch, and a plurality of search switches, the method comprising: entering a pattern search mode by receiving an indication of activation of the selection switch; indicating a search pattern by receiving an indication of activation of at least one search switch once, wherein the search pattern includes a subset of characters associated with the at least one search switch and wherein other subsets of characters are associated with other corresponding search switches; identifying a set of names corresponding to a set of content objects stored in a storage device responsive to the search pattern, the set of names including one or more names each including in a first character position the subset of characters associated with the at least one search switch; responsive to the set of names including only one name with the search pattern, displaying the one name; responsive to the set of names including more than the one name with the search pattern, displaying in a first display position at least one place holder character associated with at least one of the subset of characters included in the set of names; and repeating the entering, indicating, identifying for subsequent character positions, and displaying in subsequent display positions further ones of the at least one place holder for subsequent search patterns until the set of names includes only the one name; wherein the at least one place holder character is associated with the more than one name and a count of unresolved characters in the set of names. | 1. A content navigation method on a mobile device including a control switch, a selection switch, and a plurality of search switches, the method comprising: entering a pattern search mode by receiving an indication of activation of the selection switch; indicating a search pattern by receiving an indication of activation of at least one search switch once, wherein the search pattern includes a subset of characters associated with the at least one search switch and wherein other subsets of characters are associated with other corresponding search switches; identifying a set of names corresponding to a set of content objects stored in a storage device responsive to the search pattern, the set of names including one or more names each including in a first character position the subset of characters associated with the at least one search switch; responsive to the set of names including only one name with the search pattern, displaying the one name; responsive to the set of names including more than the one name with the search pattern, displaying in a first display position at least one place holder character associated with at least one of the subset of characters included in the set of names; and repeating the entering, indicating, identifying for subsequent character positions, and displaying in subsequent display positions further ones of the at least one place holder for subsequent search patterns until the set of names includes only the one name; wherein the at least one place holder character is associated with the more than one name and a count of unresolved characters in the set of names. 7. The content navigation method of claim 1 further comprising non-uniformly assigning different sets of characters to each of the plurality of search switches. | 0.888734 |
7,580,926 | 26 | 27 | 26. The method of claim 24 , further comprising: determining one or more tokens from one or more documents, each token comprising a token alphanumerical string that comprises one or more words and appears at least once in the documents; and determining a token order among the tokens. | 26. The method of claim 24 , further comprising: determining one or more tokens from one or more documents, each token comprising a token alphanumerical string that comprises one or more words and appears at least once in the documents; and determining a token order among the tokens. 27. The method of claim 26 , wherein generating a category signature vector that represents a category comprises: for each of the aliases of the category, generating an alias vector representing the alias; and combining the alias vectors to obtain the category signature vector. | 0.761168 |
9,154,372 | 19 | 20 | 19. The system of claim 15 , wherein the view definition editing component is further operable to enable the user to specify a first of the plurality of network object types, wherein the system further comprises an indexing component to determine whether the first network object type is an indexed network object type, to determine, in the event that the first network object type is an indexed network object type, an indexing variable for the first network object type, and to determine whether the indexing variable of the first network object type is compatible with an indexing variable being used by the table, and wherein the view definition editing component is further operable to edit the at least one column based at least in part on the determination of whether the indexing variable of the first network object type is compatible with the indexing variable used for the table. | 19. The system of claim 15 , wherein the view definition editing component is further operable to enable the user to specify a first of the plurality of network object types, wherein the system further comprises an indexing component to determine whether the first network object type is an indexed network object type, to determine, in the event that the first network object type is an indexed network object type, an indexing variable for the first network object type, and to determine whether the indexing variable of the first network object type is compatible with an indexing variable being used by the table, and wherein the view definition editing component is further operable to edit the at least one column based at least in part on the determination of whether the indexing variable of the first network object type is compatible with the indexing variable used for the table. 20. The system of claim 19 , wherein the indexing component is further operable to determine that the indexing variable of the first network object type is not compatible with the indexing variable being used by the table, and to prevent an editing of a column to represent the first network object type based on the determination of incompatibility. | 0.770039 |
8,392,438 | 12 | 14 | 12. A server comprising hardware configured to perform operations comprising: obtaining two words to be identified; determining that a shortest edit distance between the two words is less than or equal to an edit distance threshold; determining whether both of the two words exist in a preset knowledge database; if at least one of the two words does not exist in the preset knowledge database, segmenting one or more unfound words; determining whether all of the words after segmentation exist in the knowledge database; and if all of the words after segmentation exist in the knowledge database, finding a smallest granularity type with highest weight value for each such word in the knowledge database; and if both of the two words exist in the preset knowledge database, finding the smallest granularity type with highest weight value for each word in the knowledge database; determining whether the two words have a same smallest granularity type with highest weight value; if the two words have the same smallest granularity type with highest weight value, determining that the two words are synonyms; and if the two words do not have the same smallest granularity type with highest weight value, determining that the two words are non-synonyms. | 12. A server comprising hardware configured to perform operations comprising: obtaining two words to be identified; determining that a shortest edit distance between the two words is less than or equal to an edit distance threshold; determining whether both of the two words exist in a preset knowledge database; if at least one of the two words does not exist in the preset knowledge database, segmenting one or more unfound words; determining whether all of the words after segmentation exist in the knowledge database; and if all of the words after segmentation exist in the knowledge database, finding a smallest granularity type with highest weight value for each such word in the knowledge database; and if both of the two words exist in the preset knowledge database, finding the smallest granularity type with highest weight value for each word in the knowledge database; determining whether the two words have a same smallest granularity type with highest weight value; if the two words have the same smallest granularity type with highest weight value, determining that the two words are synonyms; and if the two words do not have the same smallest granularity type with highest weight value, determining that the two words are non-synonyms. 14. A server as recited in claim 12 , wherein the knowledge database comprises: one or more terms and concepts, each term or concept corresponding to at least one type, each type corresponding to the term or concept having a weight value. | 0.787879 |
9,436,951 | 36 | 45 | 36. A system for presenting additional content for a term presented by a first mobile communication device, the system comprising one or more processors executing instructions configured to: receive, by the first mobile communication device, a first utterance; transmit, by the first mobile communication device, a first identifier of the first mobile communication device and the first utterance to a computing device; receive, by the first mobile communication device from the computing device, text representing a transcription of the first utterance; receive, by the first mobile communication device from the computing device, an indicator that first additional content is available for a term identified within the text by the indicator, wherein the term is associated at the computing device with the first identifier of the first mobile communication device and a second identifier of a second mobile communication device, and wherein the first additional content for the term is associated with the first identifier and the second identifier; present, on the first mobile communication device, the text with an emphasis on the term identified by the indicator; after presenting the text on the first mobile communication device, receive, by the first mobile communication device, a second utterance that includes the term; transmit, by the first mobile communication device, the first identifier and the second utterance to the computing device; receive, by the first mobile communication device from the computing device, in response to transmitting the second utterance and the first identifier, the first additional content; present, on the first mobile communication device, the first additional content for the term; transmit, by the first mobile communication device, the second identifier to the computing device, the computing device configured to send the text as well as the indicator that first additional content is available for the term to the second mobile communication device using the second identifier; and receive, by the first mobile communication device from the computing device, a message including a transcribed third utterance received by the second communication device in response to the text. | 36. A system for presenting additional content for a term presented by a first mobile communication device, the system comprising one or more processors executing instructions configured to: receive, by the first mobile communication device, a first utterance; transmit, by the first mobile communication device, a first identifier of the first mobile communication device and the first utterance to a computing device; receive, by the first mobile communication device from the computing device, text representing a transcription of the first utterance; receive, by the first mobile communication device from the computing device, an indicator that first additional content is available for a term identified within the text by the indicator, wherein the term is associated at the computing device with the first identifier of the first mobile communication device and a second identifier of a second mobile communication device, and wherein the first additional content for the term is associated with the first identifier and the second identifier; present, on the first mobile communication device, the text with an emphasis on the term identified by the indicator; after presenting the text on the first mobile communication device, receive, by the first mobile communication device, a second utterance that includes the term; transmit, by the first mobile communication device, the first identifier and the second utterance to the computing device; receive, by the first mobile communication device from the computing device, in response to transmitting the second utterance and the first identifier, the first additional content; present, on the first mobile communication device, the first additional content for the term; transmit, by the first mobile communication device, the second identifier to the computing device, the computing device configured to send the text as well as the indicator that first additional content is available for the term to the second mobile communication device using the second identifier; and receive, by the first mobile communication device from the computing device, a message including a transcribed third utterance received by the second communication device in response to the text. 45. The system of claim 36 , wherein to receive the text representing a transcription of the first utterance, the one or more processors executing the instructions are configured to receive a plurality of terms associated with the identifier of the first mobile communication device. | 0.720356 |
9,326,116 | 17 | 20 | 17. An electronic book reading device comprising: a pause position manager configured to: receive identification of a current user reading position within an electronic text of an electronic book for a current reading session; and determine candidate pause positions within the electronic text of the electronic book based on the current user reading position and from among a portion of the electronic text extending from the current user reading position and another reading position following the current user reading position; select a suggested pause position from the determined candidate pause positions based on an available reading time and reading speed associated with a user profile of the current user for the current reading session; and a user interface configured to present the suggested pause position to indicate where to pause in reading the electronic text of the electronic book for the current reading session. | 17. An electronic book reading device comprising: a pause position manager configured to: receive identification of a current user reading position within an electronic text of an electronic book for a current reading session; and determine candidate pause positions within the electronic text of the electronic book based on the current user reading position and from among a portion of the electronic text extending from the current user reading position and another reading position following the current user reading position; select a suggested pause position from the determined candidate pause positions based on an available reading time and reading speed associated with a user profile of the current user for the current reading session; and a user interface configured to present the suggested pause position to indicate where to pause in reading the electronic text of the electronic book for the current reading session. 20. The electronic device of claim 17 , further comprising a network interface configured to receive the candidate pause positions from a server via a communications network. | 0.738739 |
9,910,851 | 6 | 7 | 6. An on-line voice translation device, comprising: a first recognition information module, configured to conduct voice recognition on first voice information input by a first user, so as to obtain first recognition information; a confirmation prompt module, configured to prompt the first user to confirm the first recognition information; an information translation module, configured to translate the confirmed first recognition information to obtain and output first translation information; a rectification information extraction module, configured to extract, according to second information which is fed back by a second user, rectification information corresponding to the second information; and an information correction module, configured to correct the first translation information according to the rectification information and output the corrected translation information, wherein the confirmation prompt module comprises: a degree of confidence determination module, configured to determine a degree of confidence of the first recognition information according to a grammar rule in a current dialogue scene, wherein the degree of confidence of the first recognition information is determined to be high when the first recognition information meets the grammar rule in the scene, and the degree of confidence of the first recognition information is determined to be low when the first recognition information does not meet the grammar rule in the scene; and a user confirmation module, configured to prompt the first user to confirm the first recognition information according to the degree of confidence of the first recognition information. | 6. An on-line voice translation device, comprising: a first recognition information module, configured to conduct voice recognition on first voice information input by a first user, so as to obtain first recognition information; a confirmation prompt module, configured to prompt the first user to confirm the first recognition information; an information translation module, configured to translate the confirmed first recognition information to obtain and output first translation information; a rectification information extraction module, configured to extract, according to second information which is fed back by a second user, rectification information corresponding to the second information; and an information correction module, configured to correct the first translation information according to the rectification information and output the corrected translation information, wherein the confirmation prompt module comprises: a degree of confidence determination module, configured to determine a degree of confidence of the first recognition information according to a grammar rule in a current dialogue scene, wherein the degree of confidence of the first recognition information is determined to be high when the first recognition information meets the grammar rule in the scene, and the degree of confidence of the first recognition information is determined to be low when the first recognition information does not meet the grammar rule in the scene; and a user confirmation module, configured to prompt the first user to confirm the first recognition information according to the degree of confidence of the first recognition information. 7. The device of claim 6 , wherein the confirmation prompt module is specifically configured to: if the degree of confidence of the first recognition information is lower than a first preset threshold, display the first recognition information for the first user to confirm; or if the degree of confidence of at least one key word in the first recognition information is lower than a second preset threshold, display the key word for the first user to confirm. | 0.5 |
9,146,711 | 1 | 2 | 1. A method comprising: generating, by a computer processor of a computing system, dictionaries comprising a product taxonomy and associated base entities comprising sub-sets within a plurality of software products; locating, by said computer processor, data entities from unstructured text of said dictionaries, wherein said locating said data entities comprises locating long distance word matches within said unstructured text; populating, by said computer processor executing a parser component, ontologies with said data entities; and determining, by said computer processor executing said parser component, relationships between said data entities; scanning, by said computer processor, license agreements associated with said plurality of software products; extracting, by said computer processor, a group of entities selected from said data entities; and locating, by said computer processor, specified relationships of said relationships, wherein said specified relationships are associated with entitlement rights of said plurality of software products, wherein said specified relationships comprise relationships selected from the group consisting of prerequisites required to run said plurality of software products, optional components of said plurality of software products, foundational components of said plurality of software products, necessary components required for execution of said plurality of software products, and components not required for execution of said plurality of software products. | 1. A method comprising: generating, by a computer processor of a computing system, dictionaries comprising a product taxonomy and associated base entities comprising sub-sets within a plurality of software products; locating, by said computer processor, data entities from unstructured text of said dictionaries, wherein said locating said data entities comprises locating long distance word matches within said unstructured text; populating, by said computer processor executing a parser component, ontologies with said data entities; and determining, by said computer processor executing said parser component, relationships between said data entities; scanning, by said computer processor, license agreements associated with said plurality of software products; extracting, by said computer processor, a group of entities selected from said data entities; and locating, by said computer processor, specified relationships of said relationships, wherein said specified relationships are associated with entitlement rights of said plurality of software products, wherein said specified relationships comprise relationships selected from the group consisting of prerequisites required to run said plurality of software products, optional components of said plurality of software products, foundational components of said plurality of software products, necessary components required for execution of said plurality of software products, and components not required for execution of said plurality of software products. 2. The method of claim 1 , wherein said dictionaries comprise a direct word matching dictionary, wherein said locating said entities comprises locating direct word matches within said unstructured text, wherein a natural language processing process executed by ontology models processes said unstructured text, and wherein said method further comprises: loading, by said computer processor, data of said product taxonomy into a triple store; and validating, by said computer processor executing a specified model, said data entities and said relationships. | 0.5 |
9,317,682 | 6 | 7 | 6. The method as set forth in claim 5 , further comprising an act of causing the data processor to perform an operation of typechecking at least one trusted module, wherein the at least one trusted module comprises labeled values comprising high confidentiality values and high integrity values, and wherein the at least one untrusted module comprises unlabeled values comprising low confidentiality values and low integrity values. | 6. The method as set forth in claim 5 , further comprising an act of causing the data processor to perform an operation of typechecking at least one trusted module, wherein the at least one trusted module comprises labeled values comprising high confidentiality values and high integrity values, and wherein the at least one untrusted module comprises unlabeled values comprising low confidentiality values and low integrity values. 7. The method as set forth in claim 6 , further comprising an act of causing the data processor to perform operations of: allowing the unlabeled values to influence at least one of the high confidentiality values and the low confidentiality values; and preventing the unlabeled values from influencing the high integrity values. | 0.5 |
9,384,187 | 1 | 9 | 1. A method comprising: identifying a document structure instance that includes a plurality of structure components; selecting one structure component from the structure components; selecting by a processor, a pre-configured core ontology hierarchy associated with the selected structure component, the pre-configured core ontology hierarchy including a plurality of classes, and a class definition relationships; generating a document specific ontology hierarchy based on the selected pre-configured core ontology, wherein the document specific ontology includes a subset of the classes included in the pre-configured core ontology hierarchy; inserting into the document specific ontology instance identifiers associated with each respective structure component; and outputting the document specific ontology hierarchy. | 1. A method comprising: identifying a document structure instance that includes a plurality of structure components; selecting one structure component from the structure components; selecting by a processor, a pre-configured core ontology hierarchy associated with the selected structure component, the pre-configured core ontology hierarchy including a plurality of classes, and a class definition relationships; generating a document specific ontology hierarchy based on the selected pre-configured core ontology, wherein the document specific ontology includes a subset of the classes included in the pre-configured core ontology hierarchy; inserting into the document specific ontology instance identifiers associated with each respective structure component; and outputting the document specific ontology hierarchy. 9. The method of claim 1 , where the class definition relationship comprises an affect relationship, a contradict relationship, a dependency relationship, an implement relationship, a similarity relationship, or any combination. | 0.679775 |
8,738,418 | 13 | 14 | 13. The method of claim 9 , wherein the user is a merchant; and the set of financial transaction based statistics includes a first set of statistics of purchases from peers of the merchant. | 13. The method of claim 9 , wherein the user is a merchant; and the set of financial transaction based statistics includes a first set of statistics of purchases from peers of the merchant. 14. The method of claim 13 , wherein the set of financial transaction based statistics further includes a second set of statistics of purchases from the merchant presented in relation to the first set of statistics of purchases from peers of the merchant. | 0.5 |
4,695,977 | 9 | 14 | 9. A method for controlling a real-time telephone process having a pluralty of states and generating a plurality of signals controlled by a computer system executing program scripts written in a nonprocedural language with each of said scripts defining an operation to be performed by said real-time telephone process and each of said program scripts comprises a plurality of groups of instructions each of whose execution is determined solely by said real-time telephone process being in a predefined state and generating a predetermined process signal and said computer system further controlled by execution of a finite state machine program routine, comprising the steps of: maintaining a plurality of identification tables for each process state by said computer system's execution of said finite state machine program routine; storing by said computer system's execution of said finite state machine program routine in each individual table in interscript control preference references identifying the groups of instructions activated by an individual process signal; maintaining present state signals representing the present state of said process by said computer system's execution of said finite state machine program routine; detecting the occurrence of a first one of said process signals by said computer system's execution of said finite state machine program routine; identifying by said computer system's execution of said finite state machine program routine the set of identification tables associated with the present state as determined by the present state signals; finding by said computer system's execution of said finite state machine program routine a first one of said identification tables within said set of identification tables associated with said first process signal; determining by said computer system's execution of said finite state machine program routine a first group of program instructions of a first program script to be executed utilizing said interscript preference; executing by said computer system the determined group of program instructions to perform a first operation in said process; directing by said computer system's execution of said finite state machine program routine the processing of the sequential next signal by said computer system's execution of one of said instructions of said first group of program instructions to block said computer system's execution of the next group of program instructions determined in accordance with said interscript preference; detecting by said system's execution of said finite state machine program routine the occurence of a second one of said process signals; reidentifying by said system's execution of said finite state machine program routine said set of identification tables associated with said present state as designated by said present state signals; finding by said system's execution of said finite state machine program routine a second table within said set of identification tables associated with said second process signal; determining by said system's execution of said finite state machine program routine a third group of program instructions to execute within said other identification table utilizing said interscript preference; executing said third group of program instructions by said computer system to perform a second operation in said process; and allowing said computer system's execution of the next group of program instructions as identified by said interscript preference by said computer system's execution of one of said third group of program instructions. | 9. A method for controlling a real-time telephone process having a pluralty of states and generating a plurality of signals controlled by a computer system executing program scripts written in a nonprocedural language with each of said scripts defining an operation to be performed by said real-time telephone process and each of said program scripts comprises a plurality of groups of instructions each of whose execution is determined solely by said real-time telephone process being in a predefined state and generating a predetermined process signal and said computer system further controlled by execution of a finite state machine program routine, comprising the steps of: maintaining a plurality of identification tables for each process state by said computer system's execution of said finite state machine program routine; storing by said computer system's execution of said finite state machine program routine in each individual table in interscript control preference references identifying the groups of instructions activated by an individual process signal; maintaining present state signals representing the present state of said process by said computer system's execution of said finite state machine program routine; detecting the occurrence of a first one of said process signals by said computer system's execution of said finite state machine program routine; identifying by said computer system's execution of said finite state machine program routine the set of identification tables associated with the present state as determined by the present state signals; finding by said computer system's execution of said finite state machine program routine a first one of said identification tables within said set of identification tables associated with said first process signal; determining by said computer system's execution of said finite state machine program routine a first group of program instructions of a first program script to be executed utilizing said interscript preference; executing by said computer system the determined group of program instructions to perform a first operation in said process; directing by said computer system's execution of said finite state machine program routine the processing of the sequential next signal by said computer system's execution of one of said instructions of said first group of program instructions to block said computer system's execution of the next group of program instructions determined in accordance with said interscript preference; detecting by said system's execution of said finite state machine program routine the occurence of a second one of said process signals; reidentifying by said system's execution of said finite state machine program routine said set of identification tables associated with said present state as designated by said present state signals; finding by said system's execution of said finite state machine program routine a second table within said set of identification tables associated with said second process signal; determining by said system's execution of said finite state machine program routine a third group of program instructions to execute within said other identification table utilizing said interscript preference; executing said third group of program instructions by said computer system to perform a second operation in said process; and allowing said computer system's execution of the next group of program instructions as identified by said interscript preference by said computer system's execution of one of said third group of program instructions. 14. The method of claim 9 wherein said interscript preference is determined by the steps of: identifying for each of said identification tables the groups of program instructions to be referenced by those tables and the corresponding script by said computer system's execution of said finite state machine program routine; and storing by said computer system's execution of said finite state machine program routine the reference to each of said groups of instructions in said identified table in the order in which the scripts are to have preference. | 0.716272 |
9,836,520 | 6 | 9 | 6. A system comprising: a processor; a data bus coupled to the processor; and a non-transitory computer-usable medium embodying computer program code, the computer-usable medium being coupled to the data bus, the computer program code used for enhancing a classification operation and comprising instructions executable by the processor and configured for: performing a classification operation on a plurality of data objects to provide a plurality of classified data objects; performing a classification validation operation on a plurality of classified data objects, the classification validation operation automatically validating whether each of the plurality of classified data objects were correctly classified, the classification validation operation automatically identifying outliers, the outliers corresponding to data objects which are likely to require reclassification, the automatically identifying outliers being performed using a similarity matrix operation, the similarity matrix operation making use of a plurality of similarity matrices, each of the plurality of similarity matrices corresponding to a given category of data objects; identifying misclassified data objects based upon the classification validation operation; performing a reclassification operation on the misclassified data objects, the reclassification operation using information derived from the classification validation operation to correctly classify the misclassified data objects; and wherein the similarity matrix operation comprises calculating a similarity value for each data object against all other data objects in a given category to determine elements of the similarity matrix; and, a similarity measure is used to generate the similarity value for the plurality of data objects, the similarity measure comprising at least one of a cosine similarity measure and a Levenshtein distance measure. | 6. A system comprising: a processor; a data bus coupled to the processor; and a non-transitory computer-usable medium embodying computer program code, the computer-usable medium being coupled to the data bus, the computer program code used for enhancing a classification operation and comprising instructions executable by the processor and configured for: performing a classification operation on a plurality of data objects to provide a plurality of classified data objects; performing a classification validation operation on a plurality of classified data objects, the classification validation operation automatically validating whether each of the plurality of classified data objects were correctly classified, the classification validation operation automatically identifying outliers, the outliers corresponding to data objects which are likely to require reclassification, the automatically identifying outliers being performed using a similarity matrix operation, the similarity matrix operation making use of a plurality of similarity matrices, each of the plurality of similarity matrices corresponding to a given category of data objects; identifying misclassified data objects based upon the classification validation operation; performing a reclassification operation on the misclassified data objects, the reclassification operation using information derived from the classification validation operation to correctly classify the misclassified data objects; and wherein the similarity matrix operation comprises calculating a similarity value for each data object against all other data objects in a given category to determine elements of the similarity matrix; and, a similarity measure is used to generate the similarity value for the plurality of data objects, the similarity measure comprising at least one of a cosine similarity measure and a Levenshtein distance measure. 9. The system of claim 6 , wherein: the classification validation operation automatically provides outliers in each category to the classification system to be reclassified using context associated with the outliers. | 0.610108 |
9,971,979 | 11 | 18 | 11. A computer-implemented method for generating suggestions integrated into business applications, the method comprising: storing, in a dynamic database stored on a storage device, parameters for generating suggestions relating to a business application; generating at least one suggestion relating to the business application, the at least one suggestion generated using the parameters stored in the dynamic database; integrating the at least one suggestion into a user interface of the business application; monitoring input of the user into the business application, including input reflecting whether the at least one suggestion has been actioned by the user; and updating the parameters stored in the dynamic database based on the monitored input, the updated parameters used to refine future suggestions when the future suggestions are generated. | 11. A computer-implemented method for generating suggestions integrated into business applications, the method comprising: storing, in a dynamic database stored on a storage device, parameters for generating suggestions relating to a business application; generating at least one suggestion relating to the business application, the at least one suggestion generated using the parameters stored in the dynamic database; integrating the at least one suggestion into a user interface of the business application; monitoring input of the user into the business application, including input reflecting whether the at least one suggestion has been actioned by the user; and updating the parameters stored in the dynamic database based on the monitored input, the updated parameters used to refine future suggestions when the future suggestions are generated. 18. The method of claim 11 , wherein the at least one suggestion is generated using an artificial intelligence layer coupled to the dynamic database. | 0.662896 |
8,436,755 | 10 | 15 | 10. A method for decoding an encoded bitstream to obtain a sequence of symbols, the symbols belonging to a finite alphabet, wherein a context model specifies a predefined probability set, and wherein each symbol of the sequence of symbols is associated with a probability from the predefined probability set on the basis of the context model, the method comprising: reading, from the bitstream, information identifying a new probability set, wherein the new probability set is not identical to the predefined probability set; assigning, to each of the symbols of the sequence of symbols, a respective probability from the new probability set based upon a mapping, wherein the mapping maps each of the probabilities of the predefined probability set to a respective one of the probabilities from the new probability set; and entropy decoding the encoded bitstream on the basis of their respective assigned probabilities from the new probability set. | 10. A method for decoding an encoded bitstream to obtain a sequence of symbols, the symbols belonging to a finite alphabet, wherein a context model specifies a predefined probability set, and wherein each symbol of the sequence of symbols is associated with a probability from the predefined probability set on the basis of the context model, the method comprising: reading, from the bitstream, information identifying a new probability set, wherein the new probability set is not identical to the predefined probability set; assigning, to each of the symbols of the sequence of symbols, a respective probability from the new probability set based upon a mapping, wherein the mapping maps each of the probabilities of the predefined probability set to a respective one of the probabilities from the new probability set; and entropy decoding the encoded bitstream on the basis of their respective assigned probabilities from the new probability set. 15. The method claimed in claim 10 , wherein the information identifying the new probability set specifies the probabilities from the predefined probability set that are included in the new probability set. | 0.52093 |
8,429,092 | 4 | 5 | 4. The method of claim 1 wherein the one or more electronic questioners include question related to a plurality of internal and external dimension factors. | 4. The method of claim 1 wherein the one or more electronic questioners include question related to a plurality of internal and external dimension factors. 5. The method of claim 4 wherein the internal dimension factors include mental and perceptual biases, emotional expression, verbal and social expression, personal values and moral codes, physicality including levels of physical energy and ways of expression, personal resiliency and social skills. | 0.5 |
9,183,294 | 5 | 6 | 5. The method of claim 1 , wherein the meta-ontology includes information about how to generate queries that are used to retrieve information associated with the high-level properties from the ontologies. | 5. The method of claim 1 , wherein the meta-ontology includes information about how to generate queries that are used to retrieve information associated with the high-level properties from the ontologies. 6. The method of claim 5 , wherein the queries include SPARQL queries. | 0.5 |
7,668,825 | 8 | 9 | 8. The system of claim 1 , wherein the query engine further comprises a Boolean term parsing engine that parses a query containing one or more Boolean terms in the query. | 8. The system of claim 1 , wherein the query engine further comprises a Boolean term parsing engine that parses a query containing one or more Boolean terms in the query. 9. The system of claim 8 , wherein the Boolean terms further comprises AND, OR, NOT and ANDNOT. | 0.5 |
9,971,834 | 8 | 11 | 8. A recommendation method for search input, comprising: A: acquiring a search keyword according to a user input; B: querying a search tree storage unit according to the search keyword to acquire address information of recommended word(s); wherein the search tree storage unit is configured to store Chinese characters in a tree data structure, wherein each node in the tree stores one Chinese character and the address information of the recommended word(s) containing the Chinese character; C: querying a recommended word database according to the address information of the recommended word(s) to acquire the recommended word(s) and then suggesting the recommended word(s) to the user, wherein the recommended word database is configured to store the recommended word(s); wherein the user input is Pinyin input or Chinese character(s) input; wherein when the user input is the Chinese character(s) input, the step A further comprises: A1: receiving Chinese character(s) input by the user and using the input Chinese character(s) directly as a search keyword; the step B further comprises: B1: querying the search tree storage unit to find address information of primary recommended word(s) with the input Chinese character(s) as a prefix at a node corresponding to the last character of the input Chinese character(s); wherein, after the step B1, the method also comprises: B2: judging whether the number of corresponding primary recommended word(s) is bigger than or equal to a preset threshold value according to the address information of the primary recommended word(s); and if yes, executing the step C; otherwise, querying the recommended word database based on the address information of the primary recommended word(s) to acquire the primary recommended word(s) and executing a step B3; B3: extending the primary recommended word(s) to acquire address information of the extended recommended word(s). | 8. A recommendation method for search input, comprising: A: acquiring a search keyword according to a user input; B: querying a search tree storage unit according to the search keyword to acquire address information of recommended word(s); wherein the search tree storage unit is configured to store Chinese characters in a tree data structure, wherein each node in the tree stores one Chinese character and the address information of the recommended word(s) containing the Chinese character; C: querying a recommended word database according to the address information of the recommended word(s) to acquire the recommended word(s) and then suggesting the recommended word(s) to the user, wherein the recommended word database is configured to store the recommended word(s); wherein the user input is Pinyin input or Chinese character(s) input; wherein when the user input is the Chinese character(s) input, the step A further comprises: A1: receiving Chinese character(s) input by the user and using the input Chinese character(s) directly as a search keyword; the step B further comprises: B1: querying the search tree storage unit to find address information of primary recommended word(s) with the input Chinese character(s) as a prefix at a node corresponding to the last character of the input Chinese character(s); wherein, after the step B1, the method also comprises: B2: judging whether the number of corresponding primary recommended word(s) is bigger than or equal to a preset threshold value according to the address information of the primary recommended word(s); and if yes, executing the step C; otherwise, querying the recommended word database based on the address information of the primary recommended word(s) to acquire the primary recommended word(s) and executing a step B3; B3: extending the primary recommended word(s) to acquire address information of the extended recommended word(s). 11. The method according to claim 8 , wherein, when the user input is the Pinyin input, the step A specifically comprises: A1′: receiving the Pinyin input by the user, querying the recommended word database according to input Pinyin to acquire guiding Chinese character(s) corresponding to the input Pinyin and using the guiding Chinese character(s) as a search keyword. | 0.734195 |
8,874,432 | 1 | 13 | 1. A method to perform relation extraction in text, comprising: applying a convolution strategy to determine a kernel between sentences; deriving an unweighted undirected graph G D (S) for a sentence S from a set of dependency relations supplemented by a linear-order structure, where the set is denoted by D(S) and V(S) is the set of vertices, with each v i εV(S) representing a certain word
G D ( S )=( V ( S ), E ( S )); determining a single path p from a dependency graph G D (S) composed from a sequence of words and their associated dependencies
p =( w i ,d i,j ,w j , . . . ,w p ,d p,q ,w q ) where word w i and w j are connected by the dependency edge d i,j ; determining a convolution kernel K G as a sum of kernels on paths (K p ): K G ( G D ( S ) , G D ( S ′ ) ) = ∑ p ∈ P n ( G D ( S ) ) ∑ p ′ ∈ P ″ ( G D ( S ′ ) ) K p ( p , p ′ ) Pr ( p G D ( S ) ) Pr ( p ′ G D ( S ′ ) ) where Pr(p|G D (S)) is a probability that single path p happens in the graph G D (S) and calculated as a ratio of path count over sum of path counts; applying one or more semi-supervised strategies to the kernel to encode syntactic and semantic information to recover a relational pattern of interest; and applying a classifier to the kernel to identify the relational pattern of interest in the text in response to a query. | 1. A method to perform relation extraction in text, comprising: applying a convolution strategy to determine a kernel between sentences; deriving an unweighted undirected graph G D (S) for a sentence S from a set of dependency relations supplemented by a linear-order structure, where the set is denoted by D(S) and V(S) is the set of vertices, with each v i εV(S) representing a certain word
G D ( S )=( V ( S ), E ( S )); determining a single path p from a dependency graph G D (S) composed from a sequence of words and their associated dependencies
p =( w i ,d i,j ,w j , . . . ,w p ,d p,q ,w q ) where word w i and w j are connected by the dependency edge d i,j ; determining a convolution kernel K G as a sum of kernels on paths (K p ): K G ( G D ( S ) , G D ( S ′ ) ) = ∑ p ∈ P n ( G D ( S ) ) ∑ p ′ ∈ P ″ ( G D ( S ′ ) ) K p ( p , p ′ ) Pr ( p G D ( S ) ) Pr ( p ′ G D ( S ′ ) ) where Pr(p|G D (S)) is a probability that single path p happens in the graph G D (S) and calculated as a ratio of path count over sum of path counts; applying one or more semi-supervised strategies to the kernel to encode syntactic and semantic information to recover a relational pattern of interest; and applying a classifier to the kernel to identify the relational pattern of interest in the text in response to a query. 13. The method of claim 1 , wherein the semi-supervised strategies include strategies on word embedding, dependency similarity and pseudo positive sentences. | 0.753918 |
9,779,081 | 14 | 19 | 14. One or more hardware computer-storage media having embodied thereon computer-usable instructions that, when executed, facilitate a method of feature completion for machine learning, the method comprising: storing a first set of data items, wherein each data item includes a text stream of words; providing a dictionary, wherein the dictionary includes a list of words that define a concept usable as an input feature for training a machine-learning model to score data items with a probability of being a positive example or a negative example of a particular class of data item; providing a feature that is trained to calculate a first probability of a presence, within a stream of one or more words, of a disjunction of one or more n-grams that correspond semantically to the concept defined by the words in the dictionary; utilizing the feature to determine the first probability of the presence, within a stream of one or more words, of a disjunction of one or more n-grams that correspond semantically to the concept defined by the words in the dictionary at a given word position in the data item; providing a machine-learning model that is trainable to calculate a second probability of the presence, within the stream of one or more words at the given word position, of the disjunction of the one or more n-grams that correspond semantically to the concept defined by the words in the dictionary, based on one or more words in the data item not utilized by the feature to determine the first probability; utilizing the machine-learning model to determine the second probability of the presence, within the stream of one or more words at the given word position, of a disjunction of one or more n-grams that correspond semantically to the concept defined by the words in the dictionary, based on the one or more words in the data item not utilized by the feature to determine the first probability; determining an actual presence or absence, at the given word position, of the disjunction of the one or more n-grams that correspond semantically to the concept defined by the words in the dictionary; and modifying the machine-learning model to adjust the second probability in a positive or negative direction based on the determined actual presence or absence of the disjunction of the one or more n-grams that correspond semantically to the concept defined by the words in the dictionary; wherein the feature determines one or more of: whether any words from a given list appear at the center of a window of text around the given word position in which center words in the window of text have been removed, a presence or absence of a verb in the window, a presence or absence of a noun followed by an adjective, or a number of occurrences of a given word in the window. | 14. One or more hardware computer-storage media having embodied thereon computer-usable instructions that, when executed, facilitate a method of feature completion for machine learning, the method comprising: storing a first set of data items, wherein each data item includes a text stream of words; providing a dictionary, wherein the dictionary includes a list of words that define a concept usable as an input feature for training a machine-learning model to score data items with a probability of being a positive example or a negative example of a particular class of data item; providing a feature that is trained to calculate a first probability of a presence, within a stream of one or more words, of a disjunction of one or more n-grams that correspond semantically to the concept defined by the words in the dictionary; utilizing the feature to determine the first probability of the presence, within a stream of one or more words, of a disjunction of one or more n-grams that correspond semantically to the concept defined by the words in the dictionary at a given word position in the data item; providing a machine-learning model that is trainable to calculate a second probability of the presence, within the stream of one or more words at the given word position, of the disjunction of the one or more n-grams that correspond semantically to the concept defined by the words in the dictionary, based on one or more words in the data item not utilized by the feature to determine the first probability; utilizing the machine-learning model to determine the second probability of the presence, within the stream of one or more words at the given word position, of a disjunction of one or more n-grams that correspond semantically to the concept defined by the words in the dictionary, based on the one or more words in the data item not utilized by the feature to determine the first probability; determining an actual presence or absence, at the given word position, of the disjunction of the one or more n-grams that correspond semantically to the concept defined by the words in the dictionary; and modifying the machine-learning model to adjust the second probability in a positive or negative direction based on the determined actual presence or absence of the disjunction of the one or more n-grams that correspond semantically to the concept defined by the words in the dictionary; wherein the feature determines one or more of: whether any words from a given list appear at the center of a window of text around the given word position in which center words in the window of text have been removed, a presence or absence of a verb in the window, a presence or absence of a noun followed by an adjective, or a number of occurrences of a given word in the window. 19. The media of claim 14 , wherein utilizing the one or more words in the data item not utilized by the feature includes utilizing a text window that includes a number of words immediately preceding a given word position and a number of words immediately following the given word position. | 0.584527 |
9,542,477 | 6 | 9 | 6. A system comprising: a first computer comprising a processor executing a master topic model (MTM) computer module, the first computer configured to generate a first term vector identifying a first topic in a plurality of documents in a document corpus; a second computer comprising a processor executing a periodic new model (PNM) computer module, the second computer configured to generate a second term vector identifying a second topic in the plurality of documents in the document corpus; and a third computer comprising a processor executing a change detection computer module, the third computer configured to: (a) link each of the first and second topics across the plurality of documents in the document corpus by matching the first and second topics across the plurality of documents in the document corpus, a link indicating a tag associated with metadata that the first and second topics are each identified in at least one document in the document corpus, (b) assign a relatedness score weight to each of the linked first and second topics based on co-occurrence of each of the linked first and second topics across the plurality of documents in the document corpus, (c) determine whether the first and second linked topics are related across the plurality of documents in the document corpus based at least in part on the relatedness score weight; (d) execute a master topic computer model based on a multi-component extension of latent Dirichlet allocation having a first set of model parameters; and the second computer's processor further executing a periodic new topic module to detect a new topic, the new topic module configuring the second computer to perform a new topic model based on the multi-component extension of latent Dirichlet allocation having a second set of model parameters different from the first set of model parameters. | 6. A system comprising: a first computer comprising a processor executing a master topic model (MTM) computer module, the first computer configured to generate a first term vector identifying a first topic in a plurality of documents in a document corpus; a second computer comprising a processor executing a periodic new model (PNM) computer module, the second computer configured to generate a second term vector identifying a second topic in the plurality of documents in the document corpus; and a third computer comprising a processor executing a change detection computer module, the third computer configured to: (a) link each of the first and second topics across the plurality of documents in the document corpus by matching the first and second topics across the plurality of documents in the document corpus, a link indicating a tag associated with metadata that the first and second topics are each identified in at least one document in the document corpus, (b) assign a relatedness score weight to each of the linked first and second topics based on co-occurrence of each of the linked first and second topics across the plurality of documents in the document corpus, (c) determine whether the first and second linked topics are related across the plurality of documents in the document corpus based at least in part on the relatedness score weight; (d) execute a master topic computer model based on a multi-component extension of latent Dirichlet allocation having a first set of model parameters; and the second computer's processor further executing a periodic new topic module to detect a new topic, the new topic module configuring the second computer to perform a new topic model based on the multi-component extension of latent Dirichlet allocation having a second set of model parameters different from the first set of model parameters. 9. The system of claim 6 , wherein the first computer and the second computer are further configured to execute respective first and second topic computer models having one or more parameters selected from the group consisting of a multi-document component, a vocabulary size, and a parameter setting for a prior Dirichlet distribution on a topic term. | 0.5 |
9,367,434 | 1 | 3 | 1. A method comprising: receiving, by a processor of a system, a policy-based Extensible Markup Language (XML) workflow comprising multiple policy nodes and multiple condition nodes, the multiple policy nodes and multiple condition nodes accessible through a common input point in the first policy-based XML workflow; parsing, by the processor, the policy-based XML workflow to construct a policy control flow graph for the policy-based XML workflow; identifying, by the processor, multiple workflow subpaths in the policy control flow graph; identifying subpath constraints for traversing the workflow subpaths in the policy control flow graph; determining, by the processor, path constraints for a selected path in the policy control flow graph by: determining constituent subpaths from among the workflow subpaths that together traverse the selected path in the policy control flow graph; and determining the path constraints for the selected path by collecting those subpath constraints for traversing the constituent subpaths that traverse the selected path; generating, with the processor, a set of test inputs for the policy-based XML workflow responsive to the path constraints for the selected path, where the set of test inputs, when input into the policy-based XML workflow, cause the policy-based XML workflow to traverse the selected path in the policy control flow graph; and storing, by the processor, the generated set of test inputs in a memory of the system. | 1. A method comprising: receiving, by a processor of a system, a policy-based Extensible Markup Language (XML) workflow comprising multiple policy nodes and multiple condition nodes, the multiple policy nodes and multiple condition nodes accessible through a common input point in the first policy-based XML workflow; parsing, by the processor, the policy-based XML workflow to construct a policy control flow graph for the policy-based XML workflow; identifying, by the processor, multiple workflow subpaths in the policy control flow graph; identifying subpath constraints for traversing the workflow subpaths in the policy control flow graph; determining, by the processor, path constraints for a selected path in the policy control flow graph by: determining constituent subpaths from among the workflow subpaths that together traverse the selected path in the policy control flow graph; and determining the path constraints for the selected path by collecting those subpath constraints for traversing the constituent subpaths that traverse the selected path; generating, with the processor, a set of test inputs for the policy-based XML workflow responsive to the path constraints for the selected path, where the set of test inputs, when input into the policy-based XML workflow, cause the policy-based XML workflow to traverse the selected path in the policy control flow graph; and storing, by the processor, the generated set of test inputs in a memory of the system. 3. The method of claim 1 , where identifying the selected path comprises identifying a specific feasible path in the policy control flow graph without identifying any non-feasible path in the policy control flow graph. | 0.778905 |
7,676,521 | 7 | 9 | 7. A method for forecasting keyword search volume performed by a computing device having a processor and memory, the method comprising: determining a seasonal correlation value for a plurality of directly forecastable keywords by calculating a Pearson correlation value, wherein the seasonal correlation value is a measure of a correlation between search volumes associated with the plurality of directly forecastable keywords and a seasonal trend in search volume; associating a non-directly forecastable keyword with the plurality of directly forecastable keywords; generating a first forecast of keyword search volume for the non-directly forecastable keyword utilizing a first forecast type when the seasonal correlation value for the plurality of directly forecastable keywords has a value greater than or equal to a predetermined threshold; and generating a second forecast of keyword search volume for the non-directly forecastable keyword utilizing a second forecast type when the seasonal correlation value for the plurality of directly forecastable keywords has value below the predetermined threshold. | 7. A method for forecasting keyword search volume performed by a computing device having a processor and memory, the method comprising: determining a seasonal correlation value for a plurality of directly forecastable keywords by calculating a Pearson correlation value, wherein the seasonal correlation value is a measure of a correlation between search volumes associated with the plurality of directly forecastable keywords and a seasonal trend in search volume; associating a non-directly forecastable keyword with the plurality of directly forecastable keywords; generating a first forecast of keyword search volume for the non-directly forecastable keyword utilizing a first forecast type when the seasonal correlation value for the plurality of directly forecastable keywords has a value greater than or equal to a predetermined threshold; and generating a second forecast of keyword search volume for the non-directly forecastable keyword utilizing a second forecast type when the seasonal correlation value for the plurality of directly forecastable keywords has value below the predetermined threshold. 9. The method of claim 7 , wherein the predetermined threshold is 0.8. | 0.768212 |
9,836,301 | 1 | 4 | 1. A method for component discovery from source code, the method performed by a processor and comprising: receiving source code; determining business classes by determining a component identification boundary in the source code; extracting features from the business classes by extracting packaging information for each of the business classes, wherein extracting packaging information for each of the business classes includes extracting concept words embedded in business class names, extracting a packaging hierarchy as a string, and extracting a substring that describes the packaging hierarchy; estimating similarity for business class pairs based on the extracted features; clustering the business classes based on the similarity, wherein clusters generated by the clustering represent components of the source code; and determining interfaces for the components based on the clustering. | 1. A method for component discovery from source code, the method performed by a processor and comprising: receiving source code; determining business classes by determining a component identification boundary in the source code; extracting features from the business classes by extracting packaging information for each of the business classes, wherein extracting packaging information for each of the business classes includes extracting concept words embedded in business class names, extracting a packaging hierarchy as a string, and extracting a substring that describes the packaging hierarchy; estimating similarity for business class pairs based on the extracted features; clustering the business classes based on the similarity, wherein clusters generated by the clustering represent components of the source code; and determining interfaces for the components based on the clustering. 4. The method of claim 1 , wherein estimating similarity for business class pairs based on the extracted features further comprises: determining, based on the extracted packaging information, packaging based similarity for the business class pairs. | 0.709602 |
8,527,517 | 1 | 26 | 1. A knowledge base system comprising: memory which stores a knowledge base and a knowledge base management system which includes instructions for at least one of adding relations to the knowledge base and querying the knowledge base, the knowledge base comprising: a relations table and a plurality of linked tables, a first of the linked tables comprising a predicate table, the relations table including slots which, for each of a plurality of rows of the relations table, respectively store a relation identifier, a predicate identifier, and first and second arguments of a respective relation; wherein each predicate identifier in the respective predicate identifier slot is composed of: a first part which encodes a key for a respective entry in the predicate table, a second part which encodes a first argument type for the first of the arguments stored in the respective row of the relations table, and a third part which encodes a second argument type for the second of the arguments stored in the respective row of the relations table; wherein the first and second argument types are selected from a predefined set of argument types, one of the argument types in the set being associated with a second of the linked tables, another of the argument types in the set being associated with a third of the linked tables or with the predicate table, one of the first and second arguments in a row of the relations table serving as a key to an entry in the second linked table when the respective part of the predicate identifier for that row encodes the associated argument type for the second linked table and serving as a key to an entry in the third linked table or the predicate table when the respective part of the predicate identifier for that row encodes the associated argument type for the third linked table or the predicate table; and a processor which executes the instructions. | 1. A knowledge base system comprising: memory which stores a knowledge base and a knowledge base management system which includes instructions for at least one of adding relations to the knowledge base and querying the knowledge base, the knowledge base comprising: a relations table and a plurality of linked tables, a first of the linked tables comprising a predicate table, the relations table including slots which, for each of a plurality of rows of the relations table, respectively store a relation identifier, a predicate identifier, and first and second arguments of a respective relation; wherein each predicate identifier in the respective predicate identifier slot is composed of: a first part which encodes a key for a respective entry in the predicate table, a second part which encodes a first argument type for the first of the arguments stored in the respective row of the relations table, and a third part which encodes a second argument type for the second of the arguments stored in the respective row of the relations table; wherein the first and second argument types are selected from a predefined set of argument types, one of the argument types in the set being associated with a second of the linked tables, another of the argument types in the set being associated with a third of the linked tables or with the predicate table, one of the first and second arguments in a row of the relations table serving as a key to an entry in the second linked table when the respective part of the predicate identifier for that row encodes the associated argument type for the second linked table and serving as a key to an entry in the third linked table or the predicate table when the respective part of the predicate identifier for that row encodes the associated argument type for the third linked table or the predicate table; and a processor which executes the instructions. 26. The system of claim 1 , wherein the predicate identifier is an integer and the first second and third parts are integers. | 0.927326 |
7,805,673 | 27 | 28 | 27. The method of claim 1 , further comprising: upon receiving a redaction edit, determining whether the document being redacted has been produced; and alerting a user that the document has been previously produced. | 27. The method of claim 1 , further comprising: upon receiving a redaction edit, determining whether the document being redacted has been produced; and alerting a user that the document has been previously produced. 28. The method of claim 27 , further comprising: when an agreement regarding clawback exists, replacing the previously produced document with an incorrect redaction with a newly, correctly redacted document. | 0.5 |
10,146,748 | 1 | 9 | 1. A method comprising: identifying, using natural language processing (NLP) techniques, a location discussed by users in a media collaboration, the media collaboration comprising a composite media stream generated from combining media streams transmitted from devices of the users; determining a location context of at least one user of the users, the location context comprising a geographic location of a device of the at least one user; identifying, based on the identified location and the location context, location information comprising a map corresponding to the identified location; generating, without user intervention, a preview of the location information identified using the NLP techniques, the preview comprising the map and a user interface (UI) element, the UI element to confirm sharing of the location information within the media collaboration; providing, without user intervention, the preview to the at least one user via a graphical user interface (GUI) of the media collaboration, the preview provided in a conversation portion of the GUI of the media collaboration without being visible to other users in the conversation portion of the GUI of the media collaboration; responsive to receiving an indication to share the location information via the UI element of the preview, providing, by a processing device to the other users, the location information comprising the map within the media collaboration; and responsive to receiving an indication that sharing of the location information is declined, removing the preview without sharing the location information in the media collaboration. | 1. A method comprising: identifying, using natural language processing (NLP) techniques, a location discussed by users in a media collaboration, the media collaboration comprising a composite media stream generated from combining media streams transmitted from devices of the users; determining a location context of at least one user of the users, the location context comprising a geographic location of a device of the at least one user; identifying, based on the identified location and the location context, location information comprising a map corresponding to the identified location; generating, without user intervention, a preview of the location information identified using the NLP techniques, the preview comprising the map and a user interface (UI) element, the UI element to confirm sharing of the location information within the media collaboration; providing, without user intervention, the preview to the at least one user via a graphical user interface (GUI) of the media collaboration, the preview provided in a conversation portion of the GUI of the media collaboration without being visible to other users in the conversation portion of the GUI of the media collaboration; responsive to receiving an indication to share the location information via the UI element of the preview, providing, by a processing device to the other users, the location information comprising the map within the media collaboration; and responsive to receiving an indication that sharing of the location information is declined, removing the preview without sharing the location information in the media collaboration. 9. The method of claim 1 , wherein the media collaboration comprises at least one of a live video recording, a pre-recorded video, a video chat, or a text-based chat. | 0.733974 |
9,507,853 | 8 | 11 | 8. A non-transitory computer storage medium encoded with instructions that when executed by one or more computers cause the one or more computers to perform operations comprising: determining a first search results score associated with first search results obtained for a search query, wherein the first search results score is based on (i) a respective rank of each first search result, and (ii) a respective first popularity score associated with each first search result; revising the search query using a query revision rule to determine a revised search query; determining a second search results score associated with second search results obtained for the revised search query, wherein the second search results score is based on (i) a respective rank of each second search result, and (ii) a respective second popularity score associated with each second search result; comparing the first search results score with the second search results score; based on the comparison of the first search results score with the second search results score, storing the query revision rule in a collection of rules that are used to revise future search queries; and using one or more rules in the collection of rules, which includes the query revision rule, to revise a new search query. | 8. A non-transitory computer storage medium encoded with instructions that when executed by one or more computers cause the one or more computers to perform operations comprising: determining a first search results score associated with first search results obtained for a search query, wherein the first search results score is based on (i) a respective rank of each first search result, and (ii) a respective first popularity score associated with each first search result; revising the search query using a query revision rule to determine a revised search query; determining a second search results score associated with second search results obtained for the revised search query, wherein the second search results score is based on (i) a respective rank of each second search result, and (ii) a respective second popularity score associated with each second search result; comparing the first search results score with the second search results score; based on the comparison of the first search results score with the second search results score, storing the query revision rule in a collection of rules that are used to revise future search queries; and using one or more rules in the collection of rules, which includes the query revision rule, to revise a new search query. 11. The non-transitory computer storage medium of claim 8 , wherein the operations further comprise: obtaining ranking information associated with the second search results; and obtaining second popularity scores associated with the second search results, wherein a second popularity score reflects the popularity of a search result in connection with the revised search query. | 0.553318 |
8,719,024 | 17 | 20 | 17. A computer-implemented method comprising: receiving audio data and a textual transcript of the audio data to be aligned with the audio data; generating, from the textual transcript, a language model that represents a set of particular substrings of the textual transcript, the language model comprising allowed states of the language model and one or more transitions that link the allowed states; generating a speech hypothesis of one or more language elements included in the audio using a speech recognizer, wherein the speech hypothesis includes time stamps associated with an occurrence of each of the one or more language elements; comparing the one or more language elements of the speech hypothesis to the substrings generated by the language model the language model to identify times at which particular ones of the substrings occur in the audio data; and associating at least a portion of the textual transcript with the time stamps based on the comparison. | 17. A computer-implemented method comprising: receiving audio data and a textual transcript of the audio data to be aligned with the audio data; generating, from the textual transcript, a language model that represents a set of particular substrings of the textual transcript, the language model comprising allowed states of the language model and one or more transitions that link the allowed states; generating a speech hypothesis of one or more language elements included in the audio using a speech recognizer, wherein the speech hypothesis includes time stamps associated with an occurrence of each of the one or more language elements; comparing the one or more language elements of the speech hypothesis to the substrings generated by the language model the language model to identify times at which particular ones of the substrings occur in the audio data; and associating at least a portion of the textual transcript with the time stamps based on the comparison. 20. The method of claim 17 , wherein the received textual transcript is based on closed captions, lyrics, or books. | 0.695767 |
8,122,371 | 20 | 22 | 20. A system for enabling a user to provide criterion-specific feedback for an item, comprising: a processor; and a memory device including instructions that, when executed by the processor, cause the processor to: provide for display a representation of the item and information regarding the item; and provide a user with the ability to provide feedback for the item, including: enable the user to select an existing response to an existing question or statement regarding at least one existing criterion for the item; enable the user to specify a new response to an existing question or statement regarding the at least one existing criterion for the item; enabling the user to select an add feedback element when the at least one existing criterion does not substantially represent the feedback the user wishes to provide for the item; in response to receiving a selection of the add feedback element, enabling the user to input a new criterion that represents the feedback the user wishes to provide for the item and one or more new values for the new criterion, by enabling the user to input a new question corresponding the new criterion and one or more new responses to the new question, the one or more new responses corresponding to the one or more new values; and aggregate feedback provided by the user with existing feedback for the item, the aggregated feedback able to be subsequently provided for display with the representation of the item and information regarding the item. | 20. A system for enabling a user to provide criterion-specific feedback for an item, comprising: a processor; and a memory device including instructions that, when executed by the processor, cause the processor to: provide for display a representation of the item and information regarding the item; and provide a user with the ability to provide feedback for the item, including: enable the user to select an existing response to an existing question or statement regarding at least one existing criterion for the item; enable the user to specify a new response to an existing question or statement regarding the at least one existing criterion for the item; enabling the user to select an add feedback element when the at least one existing criterion does not substantially represent the feedback the user wishes to provide for the item; in response to receiving a selection of the add feedback element, enabling the user to input a new criterion that represents the feedback the user wishes to provide for the item and one or more new values for the new criterion, by enabling the user to input a new question corresponding the new criterion and one or more new responses to the new question, the one or more new responses corresponding to the one or more new values; and aggregate feedback provided by the user with existing feedback for the item, the aggregated feedback able to be subsequently provided for display with the representation of the item and information regarding the item. 22. A system according to claim 20 , wherein the memory device further includes instructions that, when executed by the processor, cause the processor to: store the selected existing response or input new response for use in suggesting additional items to the user. | 0.652231 |
7,636,768 | 13 | 15 | 13. The system of claim 10 wherein the multiple different types of edges further comprise ignition edges defined as a dependency edge from a node that has the activated status, delivered status, or the skipped status. | 13. The system of claim 10 wherein the multiple different types of edges further comprise ignition edges defined as a dependency edge from a node that has the activated status, delivered status, or the skipped status. 15. The system claim 13 wherein nodes that satisfy an AND condition have the activable status, the AND condition comprising nodes wherein all input edges are ignition edges. | 0.529891 |
8,135,728 | 1 | 5 | 1. A system comprising: a user interface for displaying a web document to a user; a processor in communication with the user interface and coupled to computer readable storage media storing instructions adapted to be executed by the processor; a scanning component implemented by the processor to receive the web document for display on the user interface and scan content of the web document to select candidate phrases comprising at least one word in the web document; an analysis component implemented by the processor to: access at least one of a search engine query log file or a search engine cache containing a plurality of search queries received from a plurality of different users of a search engine; identify, from the plurality of search queries received from the plurality of different users, query frequency information comprising a list of words frequently submitted as the search queries to the search engine by the plurality of different users of the search engine over a defined period of time; extract at least one phrase comprising at least one word from the web document based, at least in part, on a comparison of the candidate phrases with the list of words frequently submitted as the search queries to the search engine by the plurality of different users; generate at least one query based upon the at least one phrase extracted; and provide the at least one query based upon the at least one phrase to an advertising system in communication with the processor; and a display component to display at least one advertisement on the user interface in conjunction with display of the content of the web document, the at least one advertisement being received by the processor from the advertising system in response to the at least one query provided to the advertising system. | 1. A system comprising: a user interface for displaying a web document to a user; a processor in communication with the user interface and coupled to computer readable storage media storing instructions adapted to be executed by the processor; a scanning component implemented by the processor to receive the web document for display on the user interface and scan content of the web document to select candidate phrases comprising at least one word in the web document; an analysis component implemented by the processor to: access at least one of a search engine query log file or a search engine cache containing a plurality of search queries received from a plurality of different users of a search engine; identify, from the plurality of search queries received from the plurality of different users, query frequency information comprising a list of words frequently submitted as the search queries to the search engine by the plurality of different users of the search engine over a defined period of time; extract at least one phrase comprising at least one word from the web document based, at least in part, on a comparison of the candidate phrases with the list of words frequently submitted as the search queries to the search engine by the plurality of different users; generate at least one query based upon the at least one phrase extracted; and provide the at least one query based upon the at least one phrase to an advertising system in communication with the processor; and a display component to display at least one advertisement on the user interface in conjunction with display of the content of the web document, the at least one advertisement being received by the processor from the advertising system in response to the at least one query provided to the advertising system. 5. The system according to claim 1 , wherein the analysis component utilizes whether a candidate phrase is part of an address of the web document for extracting the at least one phrase from the web document for generating the at least one query. | 0.742647 |
8,377,067 | 4 | 5 | 4. In a bone screw receiver adapted to join with a tool for use in conjunction with the receiver, the improvement wherein: a) the receiver includes an outward facing and at least partially circumferential horizontal groove adapted to receive a projection from the tool to secure the tool to the receiver and wherein the tool is rotated relative to the receiver so that the projection rotates relative to the groove for disconnecting the projection from the groove. | 4. In a bone screw receiver adapted to join with a tool for use in conjunction with the receiver, the improvement wherein: a) the receiver includes an outward facing and at least partially circumferential horizontal groove adapted to receive a projection from the tool to secure the tool to the receiver and wherein the tool is rotated relative to the receiver so that the projection rotates relative to the groove for disconnecting the projection from the groove. 5. The receiver according to claim 4 wherein the groove includes an upper and inner recess adapted to receive a hook joined to the projection of the tool to resist splaying of the tool relative to the receiver during usage. | 0.630795 |
9,858,265 | 1 | 5 | 1. A method for determining a type of conversation continuity in a natural language conversation comprising a first query and a second query to refine search results in response to the first query and the second query based on the type of conversation continuity, the method comprising: receiving, via a user input device, the first query from a user; retrieving, from a database, a first search result for the first query; generating for display, using control circuitry, the first search result; receiving, via the user input device, the second query from the user; determining, using control circuitry, a first token in the first query; determining, using the control circuitry, a second token in the second query; identifying, using the control circuitry, first entity data for the first token, wherein the first entity data includes: a first entity type for the first token, a first probability that the first entity type corresponds to the first token, a second entity type for the first token, and a second probability that the second entity type corresponds to the first token; identifying, using the control circuitry, second entity data for the second token, wherein the second entity data includes: a third entity type for the second token, a third probability that the third entity type corresponds to the second token, a fourth entity type for the second token, and a fourth probability that the fourth entity type corresponds to the second token; transmitting a request including an indication of the first entity data and the second entity data for connections between the first entity data and the second entity data; in response to the transmitted request, receiving one or more graph connections between the first entity data and the second entity data obtained by a search of a knowledge graph, the search based on the indication of the first entity data and the second entity data; applying, using the control circuitry, the first token, the second token, the first entity data, the second entity data, and the one or more graph connections as inputs to an artificial neural network; determining, using the control circuitry, an output from the artificial neural network that indicates the type of conversation continuity between the first query and the second query; updating, using the control circuitry, the second query based on the type of conversation continuity; retrieving, from the database, a second search result for the updated second query; and generating for display, using control circuitry, the second search result. | 1. A method for determining a type of conversation continuity in a natural language conversation comprising a first query and a second query to refine search results in response to the first query and the second query based on the type of conversation continuity, the method comprising: receiving, via a user input device, the first query from a user; retrieving, from a database, a first search result for the first query; generating for display, using control circuitry, the first search result; receiving, via the user input device, the second query from the user; determining, using control circuitry, a first token in the first query; determining, using the control circuitry, a second token in the second query; identifying, using the control circuitry, first entity data for the first token, wherein the first entity data includes: a first entity type for the first token, a first probability that the first entity type corresponds to the first token, a second entity type for the first token, and a second probability that the second entity type corresponds to the first token; identifying, using the control circuitry, second entity data for the second token, wherein the second entity data includes: a third entity type for the second token, a third probability that the third entity type corresponds to the second token, a fourth entity type for the second token, and a fourth probability that the fourth entity type corresponds to the second token; transmitting a request including an indication of the first entity data and the second entity data for connections between the first entity data and the second entity data; in response to the transmitted request, receiving one or more graph connections between the first entity data and the second entity data obtained by a search of a knowledge graph, the search based on the indication of the first entity data and the second entity data; applying, using the control circuitry, the first token, the second token, the first entity data, the second entity data, and the one or more graph connections as inputs to an artificial neural network; determining, using the control circuitry, an output from the artificial neural network that indicates the type of conversation continuity between the first query and the second query; updating, using the control circuitry, the second query based on the type of conversation continuity; retrieving, from the database, a second search result for the updated second query; and generating for display, using control circuitry, the second search result. 5. The method of claim 1 , wherein determining the output from the artificial neural network that indicates the type of conversation continuity between the first query and the second query comprises: multiplying, using the control circuitry, one or more inputs to a hidden layer in the artificial neural network with corresponding one or more weights in the hidden layer; and adding, using the control circuitry, resulting values from the multiplying to determine the output value. | 0.766278 |
4,485,439 | 18 | 20 | 18. An interface according to claim 13 wherein digital signals entering the scratchpad memory either (a) wraparound or (b) do not wraparound once the scratchpad memory is filled, the wraparound mode being established by the executive means in response to one corresponding instruction from the host computer and the not wraparound mode being established by the executive means in response to a differing corresponding instruction from the host computer. | 18. An interface according to claim 13 wherein digital signals entering the scratchpad memory either (a) wraparound or (b) do not wraparound once the scratchpad memory is filled, the wraparound mode being established by the executive means in response to one corresponding instruction from the host computer and the not wraparound mode being established by the executive means in response to a differing corresponding instruction from the host computer. 20. An interface according to claim 18 wherein each record in the scratchpad memory has an identifier applied thereto by the microprocessor in response to a corresponding instruction from the host computer. | 0.5 |
9,135,228 | 4 | 7 | 4. The method of claim 1 , wherein the morphing of the electronic document further includes: disabling editing of the electronic document; displaying a visual effect of the electronic document changing from the first state to the second state; enabling editing of the electronic document. | 4. The method of claim 1 , wherein the morphing of the electronic document further includes: disabling editing of the electronic document; displaying a visual effect of the electronic document changing from the first state to the second state; enabling editing of the electronic document. 7. The method of claim 4 , further including: discerning at least one portion of the electronic document affected by the visual effect; and automatically scrolling the electronic document to make the at least one portion visible to the user. | 0.639222 |
7,856,472 | 70 | 182 | 70. A computer program product embodied on a non-transitory computer readable medium, comprising: computer code for displaying at least one window in connection with a website; computer code for displaying, utilizing the at least one window, a stock-related field; computer code for receiving a plurality of characters of text from a user as the user is typing the text utilizing the stock-related field; computer code for dynamically determining, after the user types each character in the received text, whether the characters typed so far match one or more text strings in one of a plurality of n-tuples including at least two text strings, each of the plurality of n-tuples including first text representing a stock ticker symbol and second text representing a company name corresponding to the stock ticker symbol; computer code for indicating to the user that a match has been found, utilizing the at least one window, if it is determined that the characters typed so far match the one or more text strings in the one of the plurality of n-tuples; computer code for displaying, utilizing the at least one window, a plurality of message summaries; wherein the plurality of message summaries comprise first information derived from a first message of a plurality of first messages and second information derived from a second message of a plurality of second messages associated with at least one online forum; computer code for displaying, utilizing the at least one window, a first set of representations; computer code for receiving first input from the user indicating a selection of one of the first set of representations; computer code for displaying a second set of representations representing a set of hyperlinks, utilizing the at least one window, in response to receiving the first input; computer code for receiving second input from the user indicating a selection of one of the second set of representations; and computer code for navigating to a destination specified by the selected one of the second set of representations, in response to receiving the second input. | 70. A computer program product embodied on a non-transitory computer readable medium, comprising: computer code for displaying at least one window in connection with a website; computer code for displaying, utilizing the at least one window, a stock-related field; computer code for receiving a plurality of characters of text from a user as the user is typing the text utilizing the stock-related field; computer code for dynamically determining, after the user types each character in the received text, whether the characters typed so far match one or more text strings in one of a plurality of n-tuples including at least two text strings, each of the plurality of n-tuples including first text representing a stock ticker symbol and second text representing a company name corresponding to the stock ticker symbol; computer code for indicating to the user that a match has been found, utilizing the at least one window, if it is determined that the characters typed so far match the one or more text strings in the one of the plurality of n-tuples; computer code for displaying, utilizing the at least one window, a plurality of message summaries; wherein the plurality of message summaries comprise first information derived from a first message of a plurality of first messages and second information derived from a second message of a plurality of second messages associated with at least one online forum; computer code for displaying, utilizing the at least one window, a first set of representations; computer code for receiving first input from the user indicating a selection of one of the first set of representations; computer code for displaying a second set of representations representing a set of hyperlinks, utilizing the at least one window, in response to receiving the first input; computer code for receiving second input from the user indicating a selection of one of the second set of representations; and computer code for navigating to a destination specified by the selected one of the second set of representations, in response to receiving the second input. 182. The computer program product of claim 70 , wherein the computer program product is configured such that the displaying the second set of representations comprises displaying the second set of representations of the hyperlinks substantially immediately after receiving the first input. | 0.633249 |
7,805,289 | 8 | 10 | 8. A computer implemented method of identifying parallel, bilingual data in first and second documents, the first document being in a first language and the second document being in a second language, comprising: obtaining, with a computer processor, first and second document tree structures representative of a layout of the first and second documents, respectively, the document tree structures including nodes corresponding to text and hyperlinks in the first and second documents; aligning, with the computer processor, the first and second document tree structures by aligning nodes in the tree structures with one another while preserving a sequence and hierarchy of the nodes in the document tree structures, such that when a first node in the first document tree structure is aligned with a second node in the second document tree structure, nodes that descend from the second node in the second document tree structure are either deleted or aligned with nodes that descend from the first node in the first document tree structure and nodes that descend from the first node in the first document tree structure are either deleted or aligned with nodes that descend from the second node in the second document tree structure; and identifying, for use in machine translation, parallel, bilingual text segments as text segments corresponding to aligned nodes in the first and second document tree structures. | 8. A computer implemented method of identifying parallel, bilingual data in first and second documents, the first document being in a first language and the second document being in a second language, comprising: obtaining, with a computer processor, first and second document tree structures representative of a layout of the first and second documents, respectively, the document tree structures including nodes corresponding to text and hyperlinks in the first and second documents; aligning, with the computer processor, the first and second document tree structures by aligning nodes in the tree structures with one another while preserving a sequence and hierarchy of the nodes in the document tree structures, such that when a first node in the first document tree structure is aligned with a second node in the second document tree structure, nodes that descend from the second node in the second document tree structure are either deleted or aligned with nodes that descend from the first node in the first document tree structure and nodes that descend from the first node in the first document tree structure are either deleted or aligned with nodes that descend from the second node in the second document tree structure; and identifying, for use in machine translation, parallel, bilingual text segments as text segments corresponding to aligned nodes in the first and second document tree structures. 10. The method of claim 8 wherein each of the first and second document tree structures includes a plurality of subtree structures, and wherein aligning the first and second document tree structures comprises: aligning nodes in the subtree structures to obtain subtree alignments; and aligning the plurality of subtree structures, to align the first and second document tree structures, based on the subtree alignments. | 0.5 |
9,092,673 | 1 | 2 | 1. In a computing environment, a method comprising: receiving input comprising a tagged image collection, including a plurality of images with one or more associated text labels; identifying a first set of homogeneous relationships within the plurality of images to provide an image-to-image data structure, wherein the first set of homogeneous relationships represent pair-wise similarities between one or more images based on features of each image; identifying a second set of homogeneous relationships within words of the one or more associated text labels to provide a word-to-word data structure, wherein the second set of homogeneous relationships represent pair-wise similarities between the words of the one or more associated text labels; identifying a third set of heterogeneous relationships between the plurality of images and the words of the one or more associated text labels to provide an image-to-word data structure, wherein the third set of heterogeneous relationships define one or more associations between each of the plurality of images and each of the words of the one or more associated text labels; and processing the image-to-image data structure, the word-to-word data structure, and the image-to-word data structure simultaneously to identify both visual and textual exemplars for the tagged image collection and output at least one of a visual summary of the input or a textual summary of the input using the visual and textual exemplars. | 1. In a computing environment, a method comprising: receiving input comprising a tagged image collection, including a plurality of images with one or more associated text labels; identifying a first set of homogeneous relationships within the plurality of images to provide an image-to-image data structure, wherein the first set of homogeneous relationships represent pair-wise similarities between one or more images based on features of each image; identifying a second set of homogeneous relationships within words of the one or more associated text labels to provide a word-to-word data structure, wherein the second set of homogeneous relationships represent pair-wise similarities between the words of the one or more associated text labels; identifying a third set of heterogeneous relationships between the plurality of images and the words of the one or more associated text labels to provide an image-to-word data structure, wherein the third set of heterogeneous relationships define one or more associations between each of the plurality of images and each of the words of the one or more associated text labels; and processing the image-to-image data structure, the word-to-word data structure, and the image-to-word data structure simultaneously to identify both visual and textual exemplars for the tagged image collection and output at least one of a visual summary of the input or a textual summary of the input using the visual and textual exemplars. 2. The method of claim 1 wherein finding the first set of relationships between images comprises selecting pairs of images, and determining a similarity for each selected pair based upon features extracted from each image. | 0.614583 |
8,650,094 | 1 | 11 | 1. A method, implemented at least in part by a computing device, comprising: defining a vocabulary for emotions; extracting descriptions for one or more songs from web-based information that describes the one or more songs; generating one or more first distributions for the one or more songs in an emotion space based at least in part on the vocabulary and the extracted descriptions; extracting one or more salient words from a document; generating a second distribution for the document in the emotion space based at least in part on the vocabulary and the extracted salient words; and at the computing device, comparing the second distribution for the document to at least one of the first distributions for the one or more songs to provide at least one song recommendation corresponding to the second distribution for the document in the emotion space. | 1. A method, implemented at least in part by a computing device, comprising: defining a vocabulary for emotions; extracting descriptions for one or more songs from web-based information that describes the one or more songs; generating one or more first distributions for the one or more songs in an emotion space based at least in part on the vocabulary and the extracted descriptions; extracting one or more salient words from a document; generating a second distribution for the document in the emotion space based at least in part on the vocabulary and the extracted salient words; and at the computing device, comparing the second distribution for the document to at least one of the first distributions for the one or more songs to provide at least one song recommendation corresponding to the second distribution for the document in the emotion space. 11. The method of claim 1 wherein the document comprises a web document. | 0.797753 |
9,571,870 | 1 | 2 | 1. A method comprising: receiving, by a server computer, a request from a client computer specifying particular content for a particular account, wherein the particular content is associated with an original audio language; in response to receiving the request, selecting, by the server computer, a preferred audio language and a preferred subtitle language for the particular content based on a particular record of a preference database that maps the original audio language and the particular account to the preferred audio language and the preferred subtitle language; wherein the preference database includes a plurality of records for the particular account, wherein each record of the plurality of records maps a different original audio language to a respective preferred audio language and a respective preferred subtitle language; wherein a first record of the plurality of records maps a first original audio language to a first combination of preferred audio language and preferred subtitle language and a second record of the plurality of records maps a second original audio language to a second combination of preferred audio language and preferred subtitle language, wherein the first original audio language is different than the second original audio language and the first combination is different than the second combination; providing, from the server computer to the client computer, asset identifying data for an asset associated with the particular content, the preferred audio language, and the preferred subtitle language; in response to receiving a message at the server computer from the client computer representing input which specifies a new audio language or a new subtitle language, providing different asset identifying data associated with the particular content and one or more of: the new audio language or the new subtitle language; receiving, by the server computer, one or more messages from the client computer that identify a presented audio language and a presented subtitle language that were presented to a particular user of the particular account in relation to the particular content; in response to a determination that one or more of: the presented audio language differs from the preferred audio language or that the presented subtitle language differs from the preferred subtitle language, the server computer updating the particular record in the preference database to specify that one or more of: the preferred audio language is the presented audio language or the preferred subtitle language is the presented subtitle language; receiving, by the server computer, a second request from the client computer specifying second particular content for the particular account, wherein the second particular content is associated with the original audio language; in response to receiving the request, selecting, by the server computer, a second preferred audio language and a second preferred subtitle language for the second particular content based on the particular record of the preference database for the particular account and the original language, wherein one or more of: the second preferred audio language is the presented audio language of the particular content or the second preferred subtitle language is the presented subtitle language for the particular content; providing, from the server computer to the client computer, second asset identifying data for a second asset associated with the second particular content, the second preferred audio language, and the second preferred subtitle language. | 1. A method comprising: receiving, by a server computer, a request from a client computer specifying particular content for a particular account, wherein the particular content is associated with an original audio language; in response to receiving the request, selecting, by the server computer, a preferred audio language and a preferred subtitle language for the particular content based on a particular record of a preference database that maps the original audio language and the particular account to the preferred audio language and the preferred subtitle language; wherein the preference database includes a plurality of records for the particular account, wherein each record of the plurality of records maps a different original audio language to a respective preferred audio language and a respective preferred subtitle language; wherein a first record of the plurality of records maps a first original audio language to a first combination of preferred audio language and preferred subtitle language and a second record of the plurality of records maps a second original audio language to a second combination of preferred audio language and preferred subtitle language, wherein the first original audio language is different than the second original audio language and the first combination is different than the second combination; providing, from the server computer to the client computer, asset identifying data for an asset associated with the particular content, the preferred audio language, and the preferred subtitle language; in response to receiving a message at the server computer from the client computer representing input which specifies a new audio language or a new subtitle language, providing different asset identifying data associated with the particular content and one or more of: the new audio language or the new subtitle language; receiving, by the server computer, one or more messages from the client computer that identify a presented audio language and a presented subtitle language that were presented to a particular user of the particular account in relation to the particular content; in response to a determination that one or more of: the presented audio language differs from the preferred audio language or that the presented subtitle language differs from the preferred subtitle language, the server computer updating the particular record in the preference database to specify that one or more of: the preferred audio language is the presented audio language or the preferred subtitle language is the presented subtitle language; receiving, by the server computer, a second request from the client computer specifying second particular content for the particular account, wherein the second particular content is associated with the original audio language; in response to receiving the request, selecting, by the server computer, a second preferred audio language and a second preferred subtitle language for the second particular content based on the particular record of the preference database for the particular account and the original language, wherein one or more of: the second preferred audio language is the presented audio language of the particular content or the second preferred subtitle language is the presented subtitle language for the particular content; providing, from the server computer to the client computer, second asset identifying data for a second asset associated with the second particular content, the second preferred audio language, and the second preferred subtitle language. 2. The method of claim 1 , further comprising the server computer updating the particular record in response to a determination that the presented audio language was presented for more than a particular threshold period of time or a percentage of time, or a determination that the presented subtitle language was presented for more than a second particular threshold period of time or a second percentage of time. | 0.5 |
7,814,127 | 13 | 14 | 13. The method of claim 12 , wherein which language expression is used to display the portion of the data abstraction model is based on user parameters. | 13. The method of claim 12 , wherein which language expression is used to display the portion of the data abstraction model is based on user parameters. 14. The method of claim 13 , wherein the user parameters describe a context of the user. | 0.5 |
9,630,090 | 1 | 6 | 1. A method comprising: automatically identifying an object depicted in an image; automatically determining one or more characteristics of the object to compose a first question about the object; causing to be presented, on a first device, the first question about the object; receiving, from the first device, a first answer to the first question; converting, on a system that is remote relative to the first device, at least a portion of the first answer into searchable information; fact checking, on the system, the first answer by comparing the searchable information with information from one or more sources to determine factual correctness of the first answer to the first question; causing to be presented, on the first device, a second question about the object in accordance with fact checking results of the first answer; wherein the method is performed by one or more computing devices. | 1. A method comprising: automatically identifying an object depicted in an image; automatically determining one or more characteristics of the object to compose a first question about the object; causing to be presented, on a first device, the first question about the object; receiving, from the first device, a first answer to the first question; converting, on a system that is remote relative to the first device, at least a portion of the first answer into searchable information; fact checking, on the system, the first answer by comparing the searchable information with information from one or more sources to determine factual correctness of the first answer to the first question; causing to be presented, on the first device, a second question about the object in accordance with fact checking results of the first answer; wherein the method is performed by one or more computing devices. 6. The method of claim 1 , wherein the second question that is responsive to a factual incorrectness of the first answer is different from the second question that is responsive to a factual correctness of the first answer. | 0.510965 |
8,380,698 | 2 | 3 | 2. The method of claim 1 , wherein the aspect is utilized to describe the first data item that is offered by a seller on the network-based marketplace, wherein the first query is received from a user searching data items on the network-based marketplace. | 2. The method of claim 1 , wherein the aspect is utilized to describe the first data item that is offered by a seller on the network-based marketplace, wherein the first query is received from a user searching data items on the network-based marketplace. 3. The method of claim 2 , wherein the sample of data items is selected from a group of samples consisting of a current sample and an historical sample. | 0.5 |
8,131,768 | 7 | 10 | 7. The method of claim 1 , comprising simplifying ite-terms by using equational axioms, semantic rules and rules for presburger arithmetic. | 7. The method of claim 1 , comprising simplifying ite-terms by using equational axioms, semantic rules and rules for presburger arithmetic. 10. The method of claim 7 , comprising computing a bounded parameterized term by generalizing from individual terms obtained during symbolic analysis at loop head. | 0.5 |
8,533,579 | 17 | 19 | 17. A non-transitory computer readable storage medium that provides instructions, which when executed on a processing system cause the processing system to perform a method comprising: identifying, by a computer system, a keyword included in a data loss prevention (DLP) policy, the keyword comprising a plurality of characters; generating, by the computer system and in response to the identifying, a plurality of permutations of the characters of the keyword, wherein up to a specified maximum number of permutable characters in the keyword are permuted to generate the plurality of permutations; adding, by the computer system, the plurality of permutations to the DLP policy; and causing information content to be searched for the keyword and the plurality of permutations to detect a violation of the DLP policy in the information content. | 17. A non-transitory computer readable storage medium that provides instructions, which when executed on a processing system cause the processing system to perform a method comprising: identifying, by a computer system, a keyword included in a data loss prevention (DLP) policy, the keyword comprising a plurality of characters; generating, by the computer system and in response to the identifying, a plurality of permutations of the characters of the keyword, wherein up to a specified maximum number of permutable characters in the keyword are permuted to generate the plurality of permutations; adding, by the computer system, the plurality of permutations to the DLP policy; and causing information content to be searched for the keyword and the plurality of permutations to detect a violation of the DLP policy in the information content. 19. The non-transitory computer readable storage medium of claim 17 , wherein the maximum number of permutable characters comprises a maximum number of last characters of the keyword to be permuted. | 0.739474 |
7,929,805 | 1 | 2 | 1. A method of generating a Completely Automated Public test to Tell Computers and Humans Apart (CAPTCHA), comprising the steps of: storing a database of images and annotations corresponding to the images, each image depicting a concept or object intended for human recognition; distorting at least one of the images, and presenting the distorted image on a computer display along with a separate list of words, one of which best annotates the distorted image; and assuming that the user of the computer is a human as opposed to a machine if the correct word is selected to annotate the distorted image. | 1. A method of generating a Completely Automated Public test to Tell Computers and Humans Apart (CAPTCHA), comprising the steps of: storing a database of images and annotations corresponding to the images, each image depicting a concept or object intended for human recognition; distorting at least one of the images, and presenting the distorted image on a computer display along with a separate list of words, one of which best annotates the distorted image; and assuming that the user of the computer is a human as opposed to a machine if the correct word is selected to annotate the distorted image. 2. The method of claim 1 , wherein the image is distorted using one or more of the following: dithering, partitioning, quantization, noise addition, color re-mapping, and selective cut-and-resize. | 0.703927 |
8,930,192 | 12 | 15 | 12. A computer system comprising: a computer-based touchscreen to present a plurality of grapheme-labeled regions; a timing module to measure an amount of time that a pointing device maintains contact with each of the one or more grapheme-labeled regions; an audio speaker to produce one or more phonemes, each phoneme based on an associated one of the grapheme-labeled regions such that each grapheme touched by the pointing device represents a single phoneme; and a phoneme setting module to set an audible duration for a particular one of the phonemes produced at the audio speaker based on the measured amount of time that the pointing device maintains contact with a particular one of the grapheme-labeled regions. | 12. A computer system comprising: a computer-based touchscreen to present a plurality of grapheme-labeled regions; a timing module to measure an amount of time that a pointing device maintains contact with each of the one or more grapheme-labeled regions; an audio speaker to produce one or more phonemes, each phoneme based on an associated one of the grapheme-labeled regions such that each grapheme touched by the pointing device represents a single phoneme; and a phoneme setting module to set an audible duration for a particular one of the phonemes produced at the audio speaker based on the measured amount of time that the pointing device maintains contact with a particular one of the grapheme-labeled regions. 15. The computer system of claim 12 wherein the particular one of the phonemes whose audible duration is set corresponds to a grapheme that appears on one of the grapheme-labeled regions other than the particular one of the grapheme-labeled regions if the particular one of the phonemes is not sustainable. | 0.665208 |
6,163,768 | 8 | 10 | 8. The method of claim 1, further comprising designating an active portion of the enrollment text, wherein analyzing acoustic content of data corresponding to a user utterance comprises analyzing the data relative to the active portion of the enrollment text. | 8. The method of claim 1, further comprising designating an active portion of the enrollment text, wherein analyzing acoustic content of data corresponding to a user utterance comprises analyzing the data relative to the active portion of the enrollment text. 10. The method of claim 8, wherein analyzing the data relative to the active portion of the enrollment text comprises using an enrollment grammar corresponding to the active portion of the enrollment text. | 0.608779 |
9,767,262 | 4 | 9 | 4. A system for providing a security credential, comprising: at least one remote computing device; and a security credential manager executable in the at least one remote computing device, wherein, when executed, the security credential manager causes the at least one remote computing device to at least: automatically generate at least one security credential according to a security credential specification received from a network site at a standardized location; store the at least one security credential in association with a user account for the network site; provide a plurality of dynamically generated knowledge-based questions to a user at a client computing device and a request for a master security credential in response to a request for the at least one security credential received from the client computing device; generate a score based at least in part on a plurality of answers to the plurality of dynamically generated knowledge-based questions, the plurality of answers being received from the user via the client computing device, and individual answers of the plurality of answers being weighted with a respective different weight based at least in part on a respective knowledge-based question of the plurality of dynamically generated knowledge-based questions; and provide the at least one security credential to the client computing device in response to the score meeting or exceeding a predefined threshold and a determination that the master security credential received from the client computing device is valid. | 4. A system for providing a security credential, comprising: at least one remote computing device; and a security credential manager executable in the at least one remote computing device, wherein, when executed, the security credential manager causes the at least one remote computing device to at least: automatically generate at least one security credential according to a security credential specification received from a network site at a standardized location; store the at least one security credential in association with a user account for the network site; provide a plurality of dynamically generated knowledge-based questions to a user at a client computing device and a request for a master security credential in response to a request for the at least one security credential received from the client computing device; generate a score based at least in part on a plurality of answers to the plurality of dynamically generated knowledge-based questions, the plurality of answers being received from the user via the client computing device, and individual answers of the plurality of answers being weighted with a respective different weight based at least in part on a respective knowledge-based question of the plurality of dynamically generated knowledge-based questions; and provide the at least one security credential to the client computing device in response to the score meeting or exceeding a predefined threshold and a determination that the master security credential received from the client computing device is valid. 9. The system of claim 4 , wherein, when executed, the security credential manager further causes the at least one remote computing device to at least: automatically regenerate the at least one security credential according to another security credential specification received from the network site, wherein the at least one security credential that has been regenerated replaces the at least one security credential that was previously generated. | 0.538144 |
9,471,668 | 16 | 17 | 16. The computer program product set forth in claim 15 , wherein the second generation subsystem includes a compare process configured to omit words from the set of keywords that are found in the set of synonyms. | 16. The computer program product set forth in claim 15 , wherein the second generation subsystem includes a compare process configured to omit words from the set of keywords that are found in the set of synonyms. 17. The computer program product set forth in claim 16 , wherein a corpus taxonomy service is applied to the set of keywords after omitting common words to the set of synonyms. | 0.5 |
7,519,217 | 15 | 17 | 15. The method of claim 14 wherein the relationship between scenes is based on time between scenes and initial classification of the scenes. | 15. The method of claim 14 wherein the relationship between scenes is based on time between scenes and initial classification of the scenes. 17. The method of claim 15 including adjusting the final classifications using a majority-based windowing technique. | 0.759336 |
9,396,724 | 19 | 20 | 19. The computer-readable medium of claim 18 , wherein building the speech to text decoder according to the previously obtained acoustic model, the respective template-based language model, the respective class-based language model and the respective lexicon-based language model for the any given field, and the data samples, further comprises: performing weighted finite state transduction on the respective class-based language model in the given field, to obtain a respective class-based language model WFST (Weighted Finite State Transducer); performing weighted finite state transduction on the respective lexicon-based language model in the given field, to obtain a respective class vocabulary WFST; performing weighted finite state transduction on the respective class-based language model WFST and the respective class vocabulary WFST in the given field, to obtain a fused language model WFST; performing weighted finite state transduction on the data samples, to obtain a respective vocabulary WFST; performing weighted finite state transduction on the vocabulary WF ST and the fused language model WFST, to obtain a vocabulary language WFST; performing weighted finite state transduction on the acoustic model, to obtain an acoustic model WFST; performing weighted finite state transduction on the acoustic model WF ST and the vocabulary language WFST, to obtain an ultimate WFST; and taking the ultimate WFST as the speech-to-text decoder in the given field. | 19. The computer-readable medium of claim 18 , wherein building the speech to text decoder according to the previously obtained acoustic model, the respective template-based language model, the respective class-based language model and the respective lexicon-based language model for the any given field, and the data samples, further comprises: performing weighted finite state transduction on the respective class-based language model in the given field, to obtain a respective class-based language model WFST (Weighted Finite State Transducer); performing weighted finite state transduction on the respective lexicon-based language model in the given field, to obtain a respective class vocabulary WFST; performing weighted finite state transduction on the respective class-based language model WFST and the respective class vocabulary WFST in the given field, to obtain a fused language model WFST; performing weighted finite state transduction on the data samples, to obtain a respective vocabulary WFST; performing weighted finite state transduction on the vocabulary WF ST and the fused language model WFST, to obtain a vocabulary language WFST; performing weighted finite state transduction on the acoustic model, to obtain an acoustic model WFST; performing weighted finite state transduction on the acoustic model WF ST and the vocabulary language WFST, to obtain an ultimate WFST; and taking the ultimate WFST as the speech-to-text decoder in the given field. 20. The computer-readable medium of claim 19 , wherein after performing weighted finite state transduction on the acoustic model WF ST and the vocabulary language WF ST to obtain an ultimate WFST, and taking the ultimate WF ST as the speech-to-text decoder in the given field, the operations further comprise: obtaining speech features of speech data; inputting the speech features into respective speech-to-text decoders for multiple fields in parallel, to obtain respective speech recognition results in the multiple fields; calculating respective confidence scores of the acoustic model in the multiple fields, respective confidence scores of the respective class-based language models in the multiple fields and respective category scores corresponding to the multiple fields, according to the respective speech recognition results in the multiple fields; based on the respective confidence scores of the acoustic model in the multiple fields, the respective confidence scores of the respective class-based language models in the multiple fields, and the respective category scores corresponding to the multiple fields, acquiring respective integrated scores for the respective speech recognition results in the multiple fields; and selecting one speech recognition result from the respective speech recognition results in the multiple fields as an ultimate speech recognition result according to the respective integrated scores for the respective speech recognition results in the multiple fields. | 0.5 |
9,800,416 | 1 | 9 | 1. A method to validate a digital signature for an electronic document, comprising: receiving, at a validation server and from a computing device, a request including the digital signature and a second digest, the digital signature including a first digest of the electronic document, the second digest being generated from a representation of the electronic document at the computing device; comparing, by the validation server, the first digest of the electronic document with the second digest; generating, by the validation server, a validation result for the digital signature based on the comparing, the validation result being generated independent of the electronic document being available to the validation server; and sending, by the validation server, the validation result for the digital signature to the computing device. | 1. A method to validate a digital signature for an electronic document, comprising: receiving, at a validation server and from a computing device, a request including the digital signature and a second digest, the digital signature including a first digest of the electronic document, the second digest being generated from a representation of the electronic document at the computing device; comparing, by the validation server, the first digest of the electronic document with the second digest; generating, by the validation server, a validation result for the digital signature based on the comparing, the validation result being generated independent of the electronic document being available to the validation server; and sending, by the validation server, the validation result for the digital signature to the computing device. 9. The method of claim 1 , wherein the first digest of the electronic document and the second digest are smaller than the electronic document. | 0.828916 |
7,587,319 | 23 | 40 | 23. A speech recognition circuit comprising: an input buffer for receiving processed speech parameters; lexical memory containing lexical data for word recognition, said lexical data comprising a plurality of lexical tree data structures, each lexical tree data structure comprising a model of words having common prefix components, the initial component of each lexical tree data structure being unique; a plurality of lexical tree processors connected in parallel to said input buffer for processing the speech parameters in parallel to perform parallel lexical tree processing for word recognition by accessing said lexical data in said lexical memory; results memory connected to said lexical tree processors for storing processing results from said lexical tree processors and lexical tree identifiers to identify lexical trees to be processed by said lexical tree processors; and a control processor for controlling said lexical tree processors to process lexical trees identified in said results memory by performing parallel processing on a plurality of said lexical tree data structures. | 23. A speech recognition circuit comprising: an input buffer for receiving processed speech parameters; lexical memory containing lexical data for word recognition, said lexical data comprising a plurality of lexical tree data structures, each lexical tree data structure comprising a model of words having common prefix components, the initial component of each lexical tree data structure being unique; a plurality of lexical tree processors connected in parallel to said input buffer for processing the speech parameters in parallel to perform parallel lexical tree processing for word recognition by accessing said lexical data in said lexical memory; results memory connected to said lexical tree processors for storing processing results from said lexical tree processors and lexical tree identifiers to identify lexical trees to be processed by said lexical tree processors; and a control processor for controlling said lexical tree processors to process lexical trees identified in said results memory by performing parallel processing on a plurality of said lexical tree data structures. 40. A speech recognition circuit according to claim 23 , wherein said input buffer receives said speech parameters as feature vectors. | 0.884682 |
8,463,592 | 1 | 4 | 1. A method of converting strings to a common language comprising: determining a user-selected target language; associating a user-selected primary input language and at least one user-selected secondary input language with the target language, wherein the target language is common to, and different from, the primary input language and the at least one secondary input language; obtaining a string of at least one character; converting the obtained string from the primary input language to the target language if the obtained string corresponds to a valid string in the primary input language by using a phonetic lexicon to interpret the obtained string in the target language based upon a phonetic correspondence of the obtained string in the primary input language; converting the obtained string from at least one secondary input language to the target language if the obtained string corresponds to a valid string in the corresponding secondary input language and is not a valid string in the primary input language by using a translation lexicon to translate the obtained string from the secondary language to the target language; and outputting the converted string in the target language to an output device. | 1. A method of converting strings to a common language comprising: determining a user-selected target language; associating a user-selected primary input language and at least one user-selected secondary input language with the target language, wherein the target language is common to, and different from, the primary input language and the at least one secondary input language; obtaining a string of at least one character; converting the obtained string from the primary input language to the target language if the obtained string corresponds to a valid string in the primary input language by using a phonetic lexicon to interpret the obtained string in the target language based upon a phonetic correspondence of the obtained string in the primary input language; converting the obtained string from at least one secondary input language to the target language if the obtained string corresponds to a valid string in the corresponding secondary input language and is not a valid string in the primary input language by using a translation lexicon to translate the obtained string from the secondary language to the target language; and outputting the converted string in the target language to an output device. 4. The method according to claim 1 , wherein: converting the obtained string from the primary input language to the target language if the obtained string corresponds to a valid string in the primary input language, further comprises: determining that the string is valid by detecting that a user has selected an interpretation from a plurality of options presented to the user that each represent a conversion from the primary input language to the target language; and determining that the string is invalid by detecting that a user has not selected an interpretation from at least one presented option, even though at least one presented option includes a literal match of the string for conversion from the primary input language to the target language. | 0.504581 |
8,539,350 | 8 | 11 | 8. A method of choosing a language to be used by a text disambiguation function executed by an electronic device, the electronic device being operable to send messages to and receive messages from at least two recipients, the method comprising: determining that a message being drafted is an original message; examining language tags associated with each of at least two of the recipients; and automatically selecting a language for the original message based on the examined language tags, regardless of the number of languages represented by the examined language tags. | 8. A method of choosing a language to be used by a text disambiguation function executed by an electronic device, the electronic device being operable to send messages to and receive messages from at least two recipients, the method comprising: determining that a message being drafted is an original message; examining language tags associated with each of at least two of the recipients; and automatically selecting a language for the original message based on the examined language tags, regardless of the number of languages represented by the examined language tags. 11. The method of claim 8 , wherein when a predetermined percentage of the at least two recipients of the message do not share a common preferred language tag or a common secondary language tag, the selected language is a default language. | 0.637879 |
9,292,489 | 1 | 4 | 1. A method performed by a data processing apparatus, the method comprising: accessing a word level pronunciation lexicon and a word level training text corpus for a natural language; segmenting, using a word decomposition system, the word level training text corpus into sub-lexical units; training an n-gram language model over the sub-lexical units to produce a sub-lexical language model; constructing, using the word decomposition system, a word to sub-lexical unit mapping transducer; constructing a word level language model by: obtaining a result of composing the mapping transducer with the sub-lexical language model, and performing a projection on the result of the composition of the mapping transducer and the sub-lexical language model; constructing a speech decoding network at least by composing a context dependency model with the word level pronunciation lexicon and with the word level language model; receiving an audio stream from a user; and recognizing the audio stream, using the speech decoding network. | 1. A method performed by a data processing apparatus, the method comprising: accessing a word level pronunciation lexicon and a word level training text corpus for a natural language; segmenting, using a word decomposition system, the word level training text corpus into sub-lexical units; training an n-gram language model over the sub-lexical units to produce a sub-lexical language model; constructing, using the word decomposition system, a word to sub-lexical unit mapping transducer; constructing a word level language model by: obtaining a result of composing the mapping transducer with the sub-lexical language model, and performing a projection on the result of the composition of the mapping transducer and the sub-lexical language model; constructing a speech decoding network at least by composing a context dependency model with the word level pronunciation lexicon and with the word level language model; receiving an audio stream from a user; and recognizing the audio stream, using the speech decoding network. 4. The method of claim 1 , further comprising: detecting ambiguous outputs from the word decomposition system; and obtaining a single segmentation, using a disambiguation mechanism, for each of the ambiguous outputs. | 0.751724 |
9,529,937 | 1 | 17 | 1. A method to extract data in a data store comprising: performing, by a computer system programmed with code stored in a memory and executing by a processor of the computer system that transforms the computer system into a machine: receiving a first query specifying one or more resource description framework (RDF) triples to be identified in a data store, where the first query is either schema-less or reflects a second storage schema; extracting data, represented by one or more tokens in the first query and included within context-specific grammar events represented by the one or more tokens, from the context-specific grammar events to generate a second query that specifies one or more RDF triples to be identified in the data store responsive to the first query and that reflects the first storage schema, wherein the events represent any of a declaration and a constraint specified in the first query; where the second query comprises: a single SQL SELECT statement with a WHERE clause containing three or more logical conditions limiting triples to be considered for retrieval from the RDF triples data store, where each logical condition specifies one or more RDF triples according to a Boolean condition; and applying the single SQL SELECT statement second query to the data store for identification of the one or more specified RDF triples responsive to the first query. | 1. A method to extract data in a data store comprising: performing, by a computer system programmed with code stored in a memory and executing by a processor of the computer system that transforms the computer system into a machine: receiving a first query specifying one or more resource description framework (RDF) triples to be identified in a data store, where the first query is either schema-less or reflects a second storage schema; extracting data, represented by one or more tokens in the first query and included within context-specific grammar events represented by the one or more tokens, from the context-specific grammar events to generate a second query that specifies one or more RDF triples to be identified in the data store responsive to the first query and that reflects the first storage schema, wherein the events represent any of a declaration and a constraint specified in the first query; where the second query comprises: a single SQL SELECT statement with a WHERE clause containing three or more logical conditions limiting triples to be considered for retrieval from the RDF triples data store, where each logical condition specifies one or more RDF triples according to a Boolean condition; and applying the single SQL SELECT statement second query to the data store for identification of the one or more specified RDF triples responsive to the first query. 17. The method of claim 1 , wherein the RDF triples are stored in a hashed with origin schema. | 0.959795 |
9,075,846 | 5 | 7 | 5. The computer-implemented method according to claim 1 , further comprising preprocessing steps adapted for enhancing efficiency of said circular polar grid features extraction step. | 5. The computer-implemented method according to claim 1 , further comprising preprocessing steps adapted for enhancing efficiency of said circular polar grid features extraction step. 7. The computer-implemented method according to claim 5 , wherein said preprocessing steps include a smoothing and noise removal procedure comprising the steps of: accepting as input binary versions of said Arabic historical manuscript images; providing as output said binary versions of said Arabic historical manuscript images processed according to rules characterized by the relation if P 0 =0 then: 0 = { 0 if ∑ i = 1 8 P i > T 1 otherwise else : 0 = { 1 if p i + p i + 1 = 2 for at least one i = 1 , 2 , … , 8 0 otherwise where P 0 is the current pixel value, {acute over (P)} 0 the new pixel value and T is the threshold. | 0.554521 |
7,634,406 | 25 | 28 | 25. The system of claim 24 wherein the clustering component comprises: a language model-based clustering component configured to initialize clusters based on lexical items in the speech recognition results, assign the speech recognition results to the initialized clusters, generate a language model for each cluster based on the speech recognition results assigned to each cluster, and refine assignment of the speech recognition results to the clusters based on probabilities generated by the language models. | 25. The system of claim 24 wherein the clustering component comprises: a language model-based clustering component configured to initialize clusters based on lexical items in the speech recognition results, assign the speech recognition results to the initialized clusters, generate a language model for each cluster based on the speech recognition results assigned to each cluster, and refine assignment of the speech recognition results to the clusters based on probabilities generated by the language models. 28. The system of claim 25 wherein the clustering component is configured to merge similar clusters by computing a distance between a pair of clusters, merging the pair of clusters into one cluster if the computed distance meets a threshold distance, and performing the steps of computing and merging for a plurality of pairs of clusters to obtain a refined set of clusters. | 0.5 |
9,530,100 | 13 | 14 | 13. The engine of claim 1 , further comprising an individual-specific knowledge base coupled with the medical inference server. | 13. The engine of claim 1 , further comprising an individual-specific knowledge base coupled with the medical inference server. 14. The engine of claim 13 , wherein the medical inference server is further configured to establish the at least one outcome hypothesis as a function of information within the individual-specific knowledge base. | 0.5 |
8,600,100 | 1 | 2 | 1. A method of assessing an individual through facial muscle activity and expressions, the method comprising: (a) receiving a recording stored on a computer-readable medium of an individual's response to a stimulus, the recording including a non-verbal response comprising facial expressions of the individual; (b) accessing the computer-readable medium for detecting and recording expressional repositioning of each of a plurality of selected facial features by conducting a computerized comparison of the facial position of each selected facial feature through sequential facial images; (c) coding contemporaneously detected and recorded expressional repositionings to at least one of an action unit, a combination of action units, or at least one emotion; and (d) analyzing the at least one of an action unit, a combination of action units, or at least one emotion to assess one or more characteristics of the individual to develop a profile of the individual's personality in relation to the objective for which the individual is being assessed, wherein analyzing the at least one of an action unit, a combination of action units, or at least one emotion comprises: identifying moments of the recording that elicited emotion based on the at least one of an action unit, a combination of action units, or at least one emotion; and developing the profile of the individual's personality based on a percentage of positive versus negative emotions and the specific emotions shown during the stimulus. | 1. A method of assessing an individual through facial muscle activity and expressions, the method comprising: (a) receiving a recording stored on a computer-readable medium of an individual's response to a stimulus, the recording including a non-verbal response comprising facial expressions of the individual; (b) accessing the computer-readable medium for detecting and recording expressional repositioning of each of a plurality of selected facial features by conducting a computerized comparison of the facial position of each selected facial feature through sequential facial images; (c) coding contemporaneously detected and recorded expressional repositionings to at least one of an action unit, a combination of action units, or at least one emotion; and (d) analyzing the at least one of an action unit, a combination of action units, or at least one emotion to assess one or more characteristics of the individual to develop a profile of the individual's personality in relation to the objective for which the individual is being assessed, wherein analyzing the at least one of an action unit, a combination of action units, or at least one emotion comprises: identifying moments of the recording that elicited emotion based on the at least one of an action unit, a combination of action units, or at least one emotion; and developing the profile of the individual's personality based on a percentage of positive versus negative emotions and the specific emotions shown during the stimulus. 2. The method of claim 1 , wherein the received recording comprises a verbal response to the stimulus, and wherein analyzing the at least one of an action unit, a combination of action units, or at least one emotion comprises assessing the at least one emotion against a portion of the individual's verbal response to assess one or more characteristics of the individual with respect to the individual's verbal response. | 0.534368 |
8,523,572 | 65 | 68 | 65. A system according to claim 41 , wherein said at least one device further comprises means for transmitting information about at least one of an aggression group, a neutral group, and a pleasant group to at least one finger of at least one hand of said handicapped person. | 65. A system according to claim 41 , wherein said at least one device further comprises means for transmitting information about at least one of an aggression group, a neutral group, and a pleasant group to at least one finger of at least one hand of said handicapped person. 68. A system according to claim 65 , wherein said information about said pleasant group is transmitted to a fourth finger of a hand. | 0.503759 |
8,010,361 | 12 | 14 | 12. The tangible, non-transitory computer-readable medium of claim 1 , wherein a relationship between the detected morphemes and the one or more task objectives includes a salience measure of one of the detected morphemes with respect to a specified one of a plurality of predetermined task objectives. | 12. The tangible, non-transitory computer-readable medium of claim 1 , wherein a relationship between the detected morphemes and the one or more task objectives includes a salience measure of one of the detected morphemes with respect to a specified one of a plurality of predetermined task objectives. 14. The tangible, non-transitory computer-readable medium of claim 12 , wherein each of the plurality of detected morphemes has a salience measure exceeding a predetermined threshold. | 0.777372 |
9,715,542 | 1 | 17 | 1. A computerized method of determining relevancies of multiple objects to a search query, comprising: associating one or more of the multiple objects with user-entered tags as a result of user input, thereby defining one or more corresponding tag-object pairs, wherein each tag comprises one or more tag terms, each tag term comprising text, and wherein each association of an object with a tag comprises a tag object pair; associating an object with each tag term from the one or more tag terms, thereby defining one or more corresponding tag term object pairs, wherein at least one of the one or more tags comprises a string of multiple terms entered by a user; determining for each tag term object pair a tag term score indicating a degree of relevance between the tag term and the object, wherein a tag term score for an object X and a term A is a function of a combination comprising a total number of different terms in a tag database storing the tags, a total number of tags present in the tag database, a frequency with which the term A is present in the tag database, a number of different terms with which the object X has been tagged, a total number of tags associated with the object X, and a number of different objects that have been tagged with the term A; determining for one or more objects a term relevance score comprising combining the tag term scores from the tag term object pairs for each tag associated with each object; and determining a relevance score for each of the multiple objects for the search query, wherein the relevance score is influenced by tags associated with objects as a result of user input. | 1. A computerized method of determining relevancies of multiple objects to a search query, comprising: associating one or more of the multiple objects with user-entered tags as a result of user input, thereby defining one or more corresponding tag-object pairs, wherein each tag comprises one or more tag terms, each tag term comprising text, and wherein each association of an object with a tag comprises a tag object pair; associating an object with each tag term from the one or more tag terms, thereby defining one or more corresponding tag term object pairs, wherein at least one of the one or more tags comprises a string of multiple terms entered by a user; determining for each tag term object pair a tag term score indicating a degree of relevance between the tag term and the object, wherein a tag term score for an object X and a term A is a function of a combination comprising a total number of different terms in a tag database storing the tags, a total number of tags present in the tag database, a frequency with which the term A is present in the tag database, a number of different terms with which the object X has been tagged, a total number of tags associated with the object X, and a number of different objects that have been tagged with the term A; determining for one or more objects a term relevance score comprising combining the tag term scores from the tag term object pairs for each tag associated with each object; and determining a relevance score for each of the multiple objects for the search query, wherein the relevance score is influenced by tags associated with objects as a result of user input. 17. The method of claim 1 , wherein the multiple objects comprise hyperlinks or groups of hyperlinks to text, images, photographs, tags, groups of tags, subject areas, concepts, user profiles, answers, audio files, video files, software, or any combination of these. | 0.752328 |
7,716,161 | 12 | 15 | 12. The computer implemented method of claim 3 , wherein the target document is a target web page. | 12. The computer implemented method of claim 3 , wherein the target document is a target web page. 15. The computer implemented method of claim 12 , wherein analyzing the content further comprises analyzing terms within a title of the target web page and including them in the set of one or more topics if the frequency with which they appear in the title exceeds a threshold value. | 0.615489 |
8,296,651 | 1 | 4 | 1. A computer-implemented method for selecting one or more terms for a glossary in a document processing system, said method comprising: a processor executing program code that performs the functions of: receiving, from a spell checker, a selected term from within a document that has been rejected, where said term is one of a misspelled word or a misspelled phrase; applying a set of one or more rules to said term, each of said rule being arranged to assign a result value to said term as at least one of a Boolean or ordinal value, wherein said result value is an output score of the term when processed by a rule; applying a function to said term, said function being arranged to combine said assigned result values to produce an overall probability value relating to the likelihood of said term being a candidate for inclusion in a glossary associated with said document; presenting: said term as a candidate term for inclusion in said glossary, and said assigned result values; in response to receiving an approval to include said candidate term in the glossary, storing said candidate term to said glossary with a record of one or more of said result values assigned by said set of one or more rules; in response to receiving a denial to include said candidate term in said glossary, discarding said candidate term; and providing an indication of whether said term was subsequently included in said glossary. | 1. A computer-implemented method for selecting one or more terms for a glossary in a document processing system, said method comprising: a processor executing program code that performs the functions of: receiving, from a spell checker, a selected term from within a document that has been rejected, where said term is one of a misspelled word or a misspelled phrase; applying a set of one or more rules to said term, each of said rule being arranged to assign a result value to said term as at least one of a Boolean or ordinal value, wherein said result value is an output score of the term when processed by a rule; applying a function to said term, said function being arranged to combine said assigned result values to produce an overall probability value relating to the likelihood of said term being a candidate for inclusion in a glossary associated with said document; presenting: said term as a candidate term for inclusion in said glossary, and said assigned result values; in response to receiving an approval to include said candidate term in the glossary, storing said candidate term to said glossary with a record of one or more of said result values assigned by said set of one or more rules; in response to receiving a denial to include said candidate term in said glossary, discarding said candidate term; and providing an indication of whether said term was subsequently included in said glossary. 4. The method according to claim 1 , wherein said one or more rules produce said result values based on the context of said term. | 0.945753 |
9,477,715 | 1 | 2 | 1. A method comprising: creating, by one or more processors, news content from a plurality of news items; receiving, by the one or more processors, a request to remove a news item, of the plurality of news items, from the news content; removing, by the one or more processors, the news item and one or more similar news items, of the plurality of news items, with content similar to content of the news item from the news content; identifying, by the one or more processors, replacement news items to replace the news item and the one or more similar news items; creating, by one or more processors, updated news content by replacing the news item and the one or more similar news items with the replacement news items; and providing, by the one or more processors, the updated news content. | 1. A method comprising: creating, by one or more processors, news content from a plurality of news items; receiving, by the one or more processors, a request to remove a news item, of the plurality of news items, from the news content; removing, by the one or more processors, the news item and one or more similar news items, of the plurality of news items, with content similar to content of the news item from the news content; identifying, by the one or more processors, replacement news items to replace the news item and the one or more similar news items; creating, by one or more processors, updated news content by replacing the news item and the one or more similar news items with the replacement news items; and providing, by the one or more processors, the updated news content. 2. The method of claim 1 , where creating the new content comprises: receiving a request to access the news content, and creating the news content based on the request. | 0.58209 |
8,301,613 | 12 | 19 | 12. The system of claim 11 , where when performance of either the first search or the second search does searches lo not succeed, or when a match is not found during performance of the third search, the at least one data processor is configured to obtain input from a dependency tree for the business service configuration items and when no related service configuration items for the service category are identified from the dependency tree to conclude attempting to correlate the service ticket, otherwise when at least one related service configuration item for the service category is identified from the dependency tree to perform a fourth of search found incident resource tickets ordered by time using any found related service configuration items. | 12. The system of claim 11 , where when performance of either the first search or the second search does searches lo not succeed, or when a match is not found during performance of the third search, the at least one data processor is configured to obtain input from a dependency tree for the business service configuration items and when no related service configuration items for the service category are identified from the dependency tree to conclude attempting to correlate the service ticket, otherwise when at least one related service configuration item for the service category is identified from the dependency tree to perform a fourth of search found incident resource tickets ordered by time using any found related service configuration items. 19. The system of claim 12 , where the dependency tree comprises monitored system topology modeled by configuration management database relationships. | 0.819277 |
7,640,498 | 30 | 32 | 30. A computer-readable storage medium that includes data and instructions for enabling actions to be performed on a remote platform, comprising: a first component for enabling a request for a document from the remote platform, a type of the platform including a type of an operating system and type of a native browser that are employed to display the document; a second component for enabling a determination of the type of the remote platform and selectively associating a size ratio between a plurality of font sizes with the determined type of the remote platform, the enabling the determination of the type of the remote platform comprising enabling a determination of the type of the operating system and the determination of the type of the native browser, and the selective association being based on the determination of the type of the operating system and the determination of the type of the native browser; a third component for enabling the document with a plurality of font sizes for the document to be provided to the remote platform for display, the document is being locally scaled for display by the operation of the native browser with the size ratio for the plurality of font sizes; and a fourth component for enabling the plurality of font sizes to be changed by an execution of a script included with the document using at least one control of the native browser operating on the remote platform, the size ratio between the plurality of font sizes being determined by the native browser and maintained for a change to a size of at least one font displayed in the document at the remote platform, the execution changes the at least one font for at least one markup language text element displayed within the document. | 30. A computer-readable storage medium that includes data and instructions for enabling actions to be performed on a remote platform, comprising: a first component for enabling a request for a document from the remote platform, a type of the platform including a type of an operating system and type of a native browser that are employed to display the document; a second component for enabling a determination of the type of the remote platform and selectively associating a size ratio between a plurality of font sizes with the determined type of the remote platform, the enabling the determination of the type of the remote platform comprising enabling a determination of the type of the operating system and the determination of the type of the native browser, and the selective association being based on the determination of the type of the operating system and the determination of the type of the native browser; a third component for enabling the document with a plurality of font sizes for the document to be provided to the remote platform for display, the document is being locally scaled for display by the operation of the native browser with the size ratio for the plurality of font sizes; and a fourth component for enabling the plurality of font sizes to be changed by an execution of a script included with the document using at least one control of the native browser operating on the remote platform, the size ratio between the plurality of font sizes being determined by the native browser and maintained for a change to a size of at least one font displayed in the document at the remote platform, the execution changes the at least one font for at least one markup language text element displayed within the document. 32. The computer-readable storage medium of claim 30 , further comprising a component for enabling a determination of a font color for the plurality of fonts, wherein the font color is provided with the document to the remote platform. | 0.699488 |
8,788,262 | 15 | 25 | 15. A computer program product comprising a non-transitory computer-readable storage medium containing computer program code for: receiving an abstract phrase comprising a text phrase and a variable at a particular position in the text phrase; receiving a text value for the variable; combining the text phrase of the abstract phrase and the text value according to the particular position of the variable; applying an integration rule at a boundary of the text phrase of the abstract phrase and the text value to produce an integrated phrase, the integration rule based on a language rule, wherein the integration rule changes a portion of a word of the text phrase of the abstract phrase or a portion of a word of the text value so that the text phrase and the text value comply with the integration rule. | 15. A computer program product comprising a non-transitory computer-readable storage medium containing computer program code for: receiving an abstract phrase comprising a text phrase and a variable at a particular position in the text phrase; receiving a text value for the variable; combining the text phrase of the abstract phrase and the text value according to the particular position of the variable; applying an integration rule at a boundary of the text phrase of the abstract phrase and the text value to produce an integrated phrase, the integration rule based on a language rule, wherein the integration rule changes a portion of a word of the text phrase of the abstract phrase or a portion of a word of the text value so that the text phrase and the text value comply with the integration rule. 25. The computer program product of claim 15 , wherein multiple rules are applied, and wherein a subsequent rule is applied to text modified by a previously applied rule. | 0.899764 |
8,154,769 | 18 | 37 | 18. A device for processing paper-based and electronic documents, the device comprising: a processor; a recognition module stored on a memory and executable by the processor, the recognition module for identifying an identification number associated with an input evolutionary document based at least part on image pattern matching, the input evolutionary document having the identification number and a user input field that includes user input; the recognition module determining a first state for the input evolutionary document based at least in part on a query performed on a document database; the recognition module determining an action path based at least in part on the identification number, the action path including at least one of an action, multiple parallel actions and conditional actions conditioned on at least one of the user input, the first state and data retrieved from the database; the recognition module determining, from the document database, that the action path is to extract the user input field; the recognition module having an input and an output, the input of the recognition module coupled to receive an image of the input evolutionary document; a document processing module stored on the memory and executable by the processor, the document processing module having an input and an output, the document processing module for extracting the user input field from the input evolutionary document, retrieving a form having a first code configured to identify a location on the form for adding the user input field; and an evolutionary document creation module coupled to the recognition module, the evolutionary document creation module for receiving the form having the code configured to identify the location on the form to add the user input field, for determining, based at least in part on the code, the location on the form to add the user input field and for creating a new document by combining the form with the user input field at the location, wherein the recognition module determines a second state for the new document and tracks, from the document database, the state of the new document through its lifecycle from creation to destruction including alerting a user of copying the new document. | 18. A device for processing paper-based and electronic documents, the device comprising: a processor; a recognition module stored on a memory and executable by the processor, the recognition module for identifying an identification number associated with an input evolutionary document based at least part on image pattern matching, the input evolutionary document having the identification number and a user input field that includes user input; the recognition module determining a first state for the input evolutionary document based at least in part on a query performed on a document database; the recognition module determining an action path based at least in part on the identification number, the action path including at least one of an action, multiple parallel actions and conditional actions conditioned on at least one of the user input, the first state and data retrieved from the database; the recognition module determining, from the document database, that the action path is to extract the user input field; the recognition module having an input and an output, the input of the recognition module coupled to receive an image of the input evolutionary document; a document processing module stored on the memory and executable by the processor, the document processing module having an input and an output, the document processing module for extracting the user input field from the input evolutionary document, retrieving a form having a first code configured to identify a location on the form for adding the user input field; and an evolutionary document creation module coupled to the recognition module, the evolutionary document creation module for receiving the form having the code configured to identify the location on the form to add the user input field, for determining, based at least in part on the code, the location on the form to add the user input field and for creating a new document by combining the form with the user input field at the location, wherein the recognition module determines a second state for the new document and tracks, from the document database, the state of the new document through its lifecycle from creation to destruction including alerting a user of copying the new document. 37. The device of claim 18 further comprising a document destruction device having a document identification module, the document destruction device for destroying any document input, the document identification module identifying the document provided to the document destruction device prior to destruction, the document identification module coupled to the document processing module for specifying which documents have been destroyed. | 0.631933 |
8,417,659 | 8 | 10 | 8. An apparatus comprising: a storage device to store a plurality of rules, wherein a rule of the plurality of rules requires multiple calculations to be executed for a set of facts; and a processing device coupled to the storage device, to compile the plurality of rules to build a network to evaluate facts against the plurality of rules and to create a single multi-result set calculation node for the rule, the multi-result set calculation node to generate a set of results and to add the set of results to a tuple to be propagated to a second node connected to an output of the multi-result set calculation node. | 8. An apparatus comprising: a storage device to store a plurality of rules, wherein a rule of the plurality of rules requires multiple calculations to be executed for a set of facts; and a processing device coupled to the storage device, to compile the plurality of rules to build a network to evaluate facts against the plurality of rules and to create a single multi-result set calculation node for the rule, the multi-result set calculation node to generate a set of results and to add the set of results to a tuple to be propagated to a second node connected to an output of the multi-result set calculation node. 10. The apparatus of claim 8 , wherein an element of the tuple comprises a tree structure having a plurality of leaf nodes, and the plurality of leaf nodes contain the set of results. | 0.5 |
7,941,437 | 3 | 4 | 3. The method of claim 1 wherein determining a percentage of a maximum correction value utilized during Bayesian filtering of the at least one document further comprises: calculating the maximum correction value for the at least one document as a function of a zero count value and a number of words in the at least one document; and calculating a percentage of the maximum correction value utilized during Bayesian filtering of the at least one document as a function of a correction value utilized during Bayesian filtering of the at least one document and the maximum correction value for the at least one document. | 3. The method of claim 1 wherein determining a percentage of a maximum correction value utilized during Bayesian filtering of the at least one document further comprises: calculating the maximum correction value for the at least one document as a function of a zero count value and a number of words in the at least one document; and calculating a percentage of the maximum correction value utilized during Bayesian filtering of the at least one document as a function of a correction value utilized during Bayesian filtering of the at least one document and the maximum correction value for the at least one document. 4. The method of claim 3 wherein the zero count value further comprises a special value to use during Bayesian filtering for words not encountered during Bayesian filter training, the method further comprising: calculating the zero count value as a natural logarithm of one divided by the number of words in the training data set multiplied by a constant. | 0.5 |
7,533,107 | 23 | 28 | 23. A computer program for integrating a plurality of different data sources, the computer program comprising computer executable instructions stored in a computer readable medium that when executed cause the computer to: obtain semantic information from each of the plurality of data sources; create a conceptual model for each of the plurality of data sources using said semantic information; access a secondary knowledge source having information relating the data sources to one another; and create an integrated semantic model using said conceptual models and said secondary knowledge source; wherein said semantic information comprises characterization of at least one of constraints that hold for subsets of data in the plurality of data sources and relationships that hold between the data; wherein said semantic information further comprises information expressing properties of the data that have not been explicitly encoded in an alphanumeric representation of the data or in a syntactic structure that holds together different data elements. | 23. A computer program for integrating a plurality of different data sources, the computer program comprising computer executable instructions stored in a computer readable medium that when executed cause the computer to: obtain semantic information from each of the plurality of data sources; create a conceptual model for each of the plurality of data sources using said semantic information; access a secondary knowledge source having information relating the data sources to one another; and create an integrated semantic model using said conceptual models and said secondary knowledge source; wherein said semantic information comprises characterization of at least one of constraints that hold for subsets of data in the plurality of data sources and relationships that hold between the data; wherein said semantic information further comprises information expressing properties of the data that have not been explicitly encoded in an alphanumeric representation of the data or in a syntactic structure that holds together different data elements. 28. A computer program as in claim 23 wherein said semantic information comprises class schema and relationship schema. | 0.858333 |
10,067,992 | 16 | 17 | 16. The computer program product of claim 13 , further comprising accessing, by the hardware processor, a constrained natural language dictionary comprising the vocabularies, wherein the vocabularies are configured to reference pre-built data mappings, the pre-built data mappings being configured to enable user access to data subsets of the data source using the vocabularies and computer generated metadata of the vocabularies to augment the constrained natural language dictionary. | 16. The computer program product of claim 13 , further comprising accessing, by the hardware processor, a constrained natural language dictionary comprising the vocabularies, wherein the vocabularies are configured to reference pre-built data mappings, the pre-built data mappings being configured to enable user access to data subsets of the data source using the vocabularies and computer generated metadata of the vocabularies to augment the constrained natural language dictionary. 17. The computer program product of claim 16 , further comprising prompting the user with additional categories of nearest semantic proximity for further querying of the data source. | 0.5 |
7,577,739 | 11 | 14 | 11. The method of claim 1 , further comprising a software portion configured to output a report relating to the presence of said at least one preselected criterion. | 11. The method of claim 1 , further comprising a software portion configured to output a report relating to the presence of said at least one preselected criterion. 14. The method of claim 11 , wherein said report provides the text of all communications that match said preselected criterion. | 0.748016 |
9,323,826 | 6 | 8 | 6. The method of claim 5 , wherein the examining step further includes identifying metadata associated with each micro-blog message contained in the plurality of received micro-blog messages. | 6. The method of claim 5 , wherein the examining step further includes identifying metadata associated with each micro-blog message contained in the plurality of received micro-blog messages. 8. The method of claim 6 , wherein the examining step further includes developing one or more classifiers from one or more training sets and applying the one or more classifiers to the word vectors and the metadata associated each micro-blog message contained in the plurality of received micro-blog messages. | 0.503215 |
7,890,533 | 7 | 19 | 7. A method for visually modeling information sought from a set of documents implemented using a processor and a display, comprising: identifying a set of documents; applying a filter to the set of documents to produce raw text; analyzing the raw text using a lexica module and a POS (part of speech) tagger by operation of the processor; presenting the analysis of the raw text to a user; creating a plurality of concepts based on the analysis of the raw text; creating a visual model comprising visual elements corresponding to the plurality of concepts; presenting the visual model to the user on the display; enabling the user to add a new visual element to the visual model, the new visual element corresponding to a new concept; enabling the user to add a new relation between visual elements in the visual model, the new relation between visual elements representing a new relation between concepts corresponding to the visual elements; receiving a definition of a concept from the user via selection of a visual model corresponding to the concept; generating extractors, each extractor corresponding to one of the visual elements or the relations between the visual elements in the visual model; based on a user selection of one of the visual elements or the relations, extracting a document from the set of documents using the corresponding extractor, the extracted document containing information related to the concept corresponding to the selected visual element or the selected relation; customizing the visual model based on user input in response to the extracted documents; and exporting the customized model. | 7. A method for visually modeling information sought from a set of documents implemented using a processor and a display, comprising: identifying a set of documents; applying a filter to the set of documents to produce raw text; analyzing the raw text using a lexica module and a POS (part of speech) tagger by operation of the processor; presenting the analysis of the raw text to a user; creating a plurality of concepts based on the analysis of the raw text; creating a visual model comprising visual elements corresponding to the plurality of concepts; presenting the visual model to the user on the display; enabling the user to add a new visual element to the visual model, the new visual element corresponding to a new concept; enabling the user to add a new relation between visual elements in the visual model, the new relation between visual elements representing a new relation between concepts corresponding to the visual elements; receiving a definition of a concept from the user via selection of a visual model corresponding to the concept; generating extractors, each extractor corresponding to one of the visual elements or the relations between the visual elements in the visual model; based on a user selection of one of the visual elements or the relations, extracting a document from the set of documents using the corresponding extractor, the extracted document containing information related to the concept corresponding to the selected visual element or the selected relation; customizing the visual model based on user input in response to the extracted documents; and exporting the customized model. 19. The method of claim 7 , further comprising: exporting the set of documents together with the customized model. | 0.853093 |
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