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8,468,048 | 24 | 25 | 24. The computer-implemented method of claim 14 wherein determining a set of one or more taxonomy categories using at least one of the one or more media properties includes determining a set of one or more semantic clusters using the accepted one or more media properties, and determining a set of one or more taxonomy categories using at least some of the one or more semantic clusters. | 24. The computer-implemented method of claim 14 wherein determining a set of one or more taxonomy categories using at least one of the one or more media properties includes determining a set of one or more semantic clusters using the accepted one or more media properties, and determining a set of one or more taxonomy categories using at least some of the one or more semantic clusters. 25. The computer-implemented method of claim 24 wherein the semantic clusters are term co-occurrence clusters. | 0.5 |
9,996,620 | 2 | 6 | 2. The method of claim 1 , further comprising: displaying content, for a third topic interface, based, at least in part, on a user selection of a third topic. | 2. The method of claim 1 , further comprising: displaying content, for a third topic interface, based, at least in part, on a user selection of a third topic. 6. The method of claim 2 , wherein the content comprises search results from a search engine query based, at least in part, on the third topic. | 0.677928 |
8,862,609 | 19 | 25 | 19. A computer system for generating a query by modifying a query template, the computer system comprising: one or more computer processors; one or more computer-readable tangible storage devices; program instructions, stored on at least one of the one or more computer-readable tangible storage devices for execution by at least one of the one or more computer processors, the program instructions comprising: program instructions to identify one or more words in a natural language query that have previously-established associations with stored data upon which the query will be executed, wherein the program instructions to identify the one or more words in the natural language query further comprise: program instructions to receive the natural language query, the natural language query being formatted to a query template, wherein the query template includes at least one first component and at least one second component, the first component including a predefined action, the second component being modifiable based, at least in part, on the one or more words; program instructions to determine at least one link between the one or more words in the natural language query and the at least one second component; and program instructions to modify the at least one second component of the query template based, at least in part, on the at least one link, wherein modifying the at least one second component includes adding to the query template a rule that limits possible values for the at least one second component and a name of a database table that includes the stored data upon which the query will be executed; program instructions to generate a new query by adding one or more values to the query template, wherein the one or more values are added to the query template based, at least in part, on the modification of the at least one second component of the query template and the one or more words in the natural language query; and program instructions to execute a search based on the new query. | 19. A computer system for generating a query by modifying a query template, the computer system comprising: one or more computer processors; one or more computer-readable tangible storage devices; program instructions, stored on at least one of the one or more computer-readable tangible storage devices for execution by at least one of the one or more computer processors, the program instructions comprising: program instructions to identify one or more words in a natural language query that have previously-established associations with stored data upon which the query will be executed, wherein the program instructions to identify the one or more words in the natural language query further comprise: program instructions to receive the natural language query, the natural language query being formatted to a query template, wherein the query template includes at least one first component and at least one second component, the first component including a predefined action, the second component being modifiable based, at least in part, on the one or more words; program instructions to determine at least one link between the one or more words in the natural language query and the at least one second component; and program instructions to modify the at least one second component of the query template based, at least in part, on the at least one link, wherein modifying the at least one second component includes adding to the query template a rule that limits possible values for the at least one second component and a name of a database table that includes the stored data upon which the query will be executed; program instructions to generate a new query by adding one or more values to the query template, wherein the one or more values are added to the query template based, at least in part, on the modification of the at least one second component of the query template and the one or more words in the natural language query; and program instructions to execute a search based on the new query. 25. The computer system of claim 19 , wherein program instructions to determine, by operation of one or more computer processors, at least one link between the one or more words in the natural language query and the at least one second component further comprises: program instructions to determine whether the one or more words in the natural language query is related to the at least one first component of the query template; and responsive to program instructions to determine that the one or more words in the natural language query is not related to the at least one first component of the query template, program instructions to determine whether the one or more words is related to the at least one second component of the query template. | 0.587845 |
8,626,742 | 1 | 8 | 1. A method of processing correlated keywords, the method comprising: providing a web page having a first input box and an embedded window object that provides a frame for a second input box and a keyword list container; receiving a primary keyword inputted by a user into the first input box of the web page; establishing a link between the embedded window object in the web page and a server and obtaining, from the server via the link, candidate related keywords of the primary keyword; creating a keyword list to present the candidate related keywords in the web page by the embedded window object; presenting, in the keyword list container of the frame of the embedded window object in the web page, the keyword list to the user; obtaining at least one related keyword from the keyword list, wherein the at least one related keyword corresponds to a respective candidate related keyword selected by the user; adding the at least one related keyword into the second input box of the frame of the embedded window object in the web page; and presenting, the second input box of the frame of the embedded window object in the web page to the user, wherein the first input box with the primary keyword therein, the second input box with the at least one related keyword therein and the keyword list container with the candidate related keywords therein are concurrently presented, and the concurrent presentation further comprises determining whether a number of the at least one related keyword in the second input box being greater than or equal to a predetermined threshold, and closing the keyword list if affirmative. | 1. A method of processing correlated keywords, the method comprising: providing a web page having a first input box and an embedded window object that provides a frame for a second input box and a keyword list container; receiving a primary keyword inputted by a user into the first input box of the web page; establishing a link between the embedded window object in the web page and a server and obtaining, from the server via the link, candidate related keywords of the primary keyword; creating a keyword list to present the candidate related keywords in the web page by the embedded window object; presenting, in the keyword list container of the frame of the embedded window object in the web page, the keyword list to the user; obtaining at least one related keyword from the keyword list, wherein the at least one related keyword corresponds to a respective candidate related keyword selected by the user; adding the at least one related keyword into the second input box of the frame of the embedded window object in the web page; and presenting, the second input box of the frame of the embedded window object in the web page to the user, wherein the first input box with the primary keyword therein, the second input box with the at least one related keyword therein and the keyword list container with the candidate related keywords therein are concurrently presented, and the concurrent presentation further comprises determining whether a number of the at least one related keyword in the second input box being greater than or equal to a predetermined threshold, and closing the keyword list if affirmative. 8. The method as recited in claim 1 , the method further comprising: determining whether the second input box already has any related keyword therein, and inserting a separator prior to the selected at least one related keyword to be added if affirmative. | 0.771095 |
7,533,172 | 37 | 40 | 37. The peer node as recited in claim 35 , wherein the program instructions are further executable to: receive a discovery query message including one or more criteria describing a resource, wherein the discovery query message is formatted in accordance with a discovery protocol; and send a response message in response to the discovery query message including one or more advertisements of resources that match the one or more criteria, wherein each of said one or more resource advertisements includes a description of how to access the corresponding resource, wherein the response message is formatted in accordance with the discovery protocol. | 37. The peer node as recited in claim 35 , wherein the program instructions are further executable to: receive a discovery query message including one or more criteria describing a resource, wherein the discovery query message is formatted in accordance with a discovery protocol; and send a response message in response to the discovery query message including one or more advertisements of resources that match the one or more criteria, wherein each of said one or more resource advertisements includes a description of how to access the corresponding resource, wherein the response message is formatted in accordance with the discovery protocol. 40. The peer node as recited in claim 37 , wherein the resource is content. | 0.889053 |
9,720,937 | 1 | 13 | 1. A system for identifying a particular data object of a data type in a database comprising data objects of the data type, the system comprises: one or more processors configured to: compose a search query to identify a candidate data object being a candidate for the particular data object, wherein the one or more processors receive as an input an image and compose the search query from the received image to include a feature matrix; identify, based on the search query, the candidate data object in the database by comparing feature matrices of images in a database with the feature matrix of the search query and identifying the candidate image as an image in the database with a feature matrix that is similar to the feature matrix of the search query; present the candidate data object to the user; receive user feedback on the relevance or irrelevance of at least two segments of a plurality of segments of the same candidate data object, wherein the at least two segments of the plurality of segments of the candidate data object includes user defined segments of the candidate data object, wherein at least one of the at least two segments is relevant and the other of the at least two segments is irrelevant, wherein each of the user defined segments is a different defined portion of the image that pertains to a particular feature and the at least one of the at least two segments is deemed relevant in response to the user feedback and the other of the at least two segments is deemed irrelevant in response to the user feedback; and identify an improved candidate data object in response to the received user feedback, wherein the improved candidate data object is an improved candidate for the particular data object. | 1. A system for identifying a particular data object of a data type in a database comprising data objects of the data type, the system comprises: one or more processors configured to: compose a search query to identify a candidate data object being a candidate for the particular data object, wherein the one or more processors receive as an input an image and compose the search query from the received image to include a feature matrix; identify, based on the search query, the candidate data object in the database by comparing feature matrices of images in a database with the feature matrix of the search query and identifying the candidate image as an image in the database with a feature matrix that is similar to the feature matrix of the search query; present the candidate data object to the user; receive user feedback on the relevance or irrelevance of at least two segments of a plurality of segments of the same candidate data object, wherein the at least two segments of the plurality of segments of the candidate data object includes user defined segments of the candidate data object, wherein at least one of the at least two segments is relevant and the other of the at least two segments is irrelevant, wherein each of the user defined segments is a different defined portion of the image that pertains to a particular feature and the at least one of the at least two segments is deemed relevant in response to the user feedback and the other of the at least two segments is deemed irrelevant in response to the user feedback; and identify an improved candidate data object in response to the received user feedback, wherein the improved candidate data object is an improved candidate for the particular data object. 13. The system according to claim 1 , wherein the feature matrix includes columns of feature vectors, each comprising a distribution of different color tones in a R, G and B channel of a part of the image. | 0.770179 |
8,073,893 | 68 | 69 | 68. The apparatus of claim 64 , wherein said computing platform is further adapted to: subtract at least a portion of said first binary string from at least a portion of said second binary string; and determine said relationship based, at least in part, on a result of said subtraction. | 68. The apparatus of claim 64 , wherein said computing platform is further adapted to: subtract at least a portion of said first binary string from at least a portion of said second binary string; and determine said relationship based, at least in part, on a result of said subtraction. 69. The apparatus of claim 68 , wherein said first character expression represents a first quantity and said second character expression represents a second quantity, and wherein said computing platform is further adapted to determine whether said first quantity is greater than said second quantity based, at least in part, on said result of said subtraction. | 0.565217 |
8,364,612 | 15 | 16 | 15. A method as claimed in claim 14 further comprising: providing the factor graph data structure to an inference engine; performing an inference based on the factor graph data structure using data in the probabilistic relational database; and storing results of the inference at the plurality of probabilistic attributes. | 15. A method as claimed in claim 14 further comprising: providing the factor graph data structure to an inference engine; performing an inference based on the factor graph data structure using data in the probabilistic relational database; and storing results of the inference at the plurality of probabilistic attributes. 16. A method as claimed in claim 15 further comprising: identifying missing deterministic attribute values in the one or more relational databases; and filling the missing deterministic attribute values using the results of the inference. | 0.5 |
7,725,526 | 14 | 20 | 14. A client information processing unit coupled via a network with a hub processing unit apparatus for sharing queries comprising: an input for receiving a Uniform Resource Locator (URL) string from a first user; a comparator for determining if the URL string represents a query; an interface for storing the query in an information processing unit memory; an output for forwarding the query to a hub processing unit in the event that the first user selects the query for sharing with a second user connected to the hub processing unit; an accounting database for storing information for awarding the first user for submitting the query for sharing; and an input for receiving, from a second user, a selection for one of the stored queries for sharing. | 14. A client information processing unit coupled via a network with a hub processing unit apparatus for sharing queries comprising: an input for receiving a Uniform Resource Locator (URL) string from a first user; a comparator for determining if the URL string represents a query; an interface for storing the query in an information processing unit memory; an output for forwarding the query to a hub processing unit in the event that the first user selects the query for sharing with a second user connected to the hub processing unit; an accounting database for storing information for awarding the first user for submitting the query for sharing; and an input for receiving, from a second user, a selection for one of the stored queries for sharing. 20. The client information processing unit as defined in claim 14 , wherein the information processing unit memory further includes permanent or temporary memory. | 0.631818 |
8,135,712 | 11 | 15 | 11. Software for identifying related questions comprising computer readable instructions embodied on non-transitory computer-readable media, wherein the computer readable instructions are configured, when executed by a data processing apparatus, to: identify, using at least one hardware processor, a plurality of different previously-submitted search queries; filter, using at least one evaluation file, the plurality of different previously-submitted search queries to remove one or more specified words from the plurality of different previously-submitted search queries to generate a plurality of filtered search queries, wherein the at least one evaluation file includes at least one of instructions or parameters for generating at least one canonical search query form of the plurality of different previously-submitted search queries; modify, using the at least one evaluation file, remaining words in the plurality of filtered search queries to generate a plurality of modified search queries; determine, as search queries that map to a particular canonical search query form, a subset of the plurality of different previously-submitted search queries that are used to generate, as a result of filtering the plurality of different previously-submitted search queries and modifying the plurality of filtered search queries using the at least one evaluation file; rank the search queries that map to the particular canonical search query form based, at least in part, on a frequency of submission of each different previously-submitted search query that maps to the particular canonical search query form; and identify, based on the ranking, a particular one of the different previously-submitted search queries in the ranked search queries that map to the particular canonical search query form as a representative search query of the search queries that map to the particular canonical search query form. | 11. Software for identifying related questions comprising computer readable instructions embodied on non-transitory computer-readable media, wherein the computer readable instructions are configured, when executed by a data processing apparatus, to: identify, using at least one hardware processor, a plurality of different previously-submitted search queries; filter, using at least one evaluation file, the plurality of different previously-submitted search queries to remove one or more specified words from the plurality of different previously-submitted search queries to generate a plurality of filtered search queries, wherein the at least one evaluation file includes at least one of instructions or parameters for generating at least one canonical search query form of the plurality of different previously-submitted search queries; modify, using the at least one evaluation file, remaining words in the plurality of filtered search queries to generate a plurality of modified search queries; determine, as search queries that map to a particular canonical search query form, a subset of the plurality of different previously-submitted search queries that are used to generate, as a result of filtering the plurality of different previously-submitted search queries and modifying the plurality of filtered search queries using the at least one evaluation file; rank the search queries that map to the particular canonical search query form based, at least in part, on a frequency of submission of each different previously-submitted search query that maps to the particular canonical search query form; and identify, based on the ranking, a particular one of the different previously-submitted search queries in the ranked search queries that map to the particular canonical search query form as a representative search query of the search queries that map to the particular canonical search query form. 15. The software of claim 11 , wherein the computer readable instructions are further configured, when executed by the data processing apparatus, to: rank words included in the plurality of different previously-submitted search queries based, at least in part, on a frequency that each word occurs in the different previously-submitted search queries; and determine the one or more words to remove from the plurality of different previously-submitted search queries based, at least in part, on the ranking of the words. | 0.5 |
9,866,645 | 32 | 34 | 32. The non-transitory computer-readable medium of claim 19 , wherein the user device includes an app associated with the third party server. | 32. The non-transitory computer-readable medium of claim 19 , wherein the user device includes an app associated with the third party server. 34. The non-transitory computer-readable medium of claim 32 , wherein a user selection of one of the one or more user interface components corresponding to the one or more actionable options causes a corresponding action request to be processed by the app. | 0.539568 |
9,799,375 | 14 | 15 | 14. The device according to claim 9 , wherein the adjusting component comprises: a first determining element, configured to determine a caption sequence number of the caption content according to the found caption content; a second determining element, configured to acquire a playback time period corresponding to the found caption content according to the caption sequence number, and determine initial playback time point corresponding to the found caption content within the playback time period; and an adjusting element, configured to adjust the playback progress according to the playback time point. | 14. The device according to claim 9 , wherein the adjusting component comprises: a first determining element, configured to determine a caption sequence number of the caption content according to the found caption content; a second determining element, configured to acquire a playback time period corresponding to the found caption content according to the caption sequence number, and determine initial playback time point corresponding to the found caption content within the playback time period; and an adjusting element, configured to adjust the playback progress according to the playback time point. 15. The device according to claim 14 , wherein the receiving component comprises: a first receiving element, configured to receive input text information; and a second receiving component, configured to receive audio data, and convert the audio data into the text information. | 0.5 |
7,698,080 | 39 | 40 | 39. A system, comprising: a radiation source configured to direct incident radiation to a sample; a detector configured to measure radiation from the sample; and an electronic processor configured to: determine spectral information for the sample from the measured radiation; determine, based on the spectral information, probabilities that the sample corresponds to each of a plurality of candidates in a library of reference information; determine, based on the probabilities, whether sample identity information can be obtained by comparing the measured spectral information to the reference information; when the sample identity information cannot be obtained, adjust a characteristic of the system to increase a magnitude of one of the probabilities relative to the other probabilities, and repeat the steps of measuring radiation, determining spectral information, determining the probabilities, and determining whether sample identity information can be obtained, until the sample identity information can be obtained; and compare the spectral information to the reference information to determine the sample identity information. | 39. A system, comprising: a radiation source configured to direct incident radiation to a sample; a detector configured to measure radiation from the sample; and an electronic processor configured to: determine spectral information for the sample from the measured radiation; determine, based on the spectral information, probabilities that the sample corresponds to each of a plurality of candidates in a library of reference information; determine, based on the probabilities, whether sample identity information can be obtained by comparing the measured spectral information to the reference information; when the sample identity information cannot be obtained, adjust a characteristic of the system to increase a magnitude of one of the probabilities relative to the other probabilities, and repeat the steps of measuring radiation, determining spectral information, determining the probabilities, and determining whether sample identity information can be obtained, until the sample identity information can be obtained; and compare the spectral information to the reference information to determine the sample identity information. 40. The system of claim 39 , wherein the electronic processor is further configured to determine, for each of the plurality of candidates, an overlap between a range of expected values of the spectral information based on the reference information, and a range of measurement values based on the spectral information and an estimate of a variability of the spectral information. | 0.5 |
8,781,228 | 1 | 11 | 1. A method comprising: accessing a data structure stored on a computer memory device, the data structure storing data representing a plurality of keyword-condition pairs and, for each keyword-condition pair, an associated action; receiving a sequence of characters captured by a capture device; determining that the received sequence of characters includes a keyword that matches a first keyword of a first keyword-condition pair in the data structure, the first keyword-condition pair comprising the first keyword and a first condition, and determining that the first condition is satisfied; and performing a first action associated with the first keyword-condition pair in the data structure. | 1. A method comprising: accessing a data structure stored on a computer memory device, the data structure storing data representing a plurality of keyword-condition pairs and, for each keyword-condition pair, an associated action; receiving a sequence of characters captured by a capture device; determining that the received sequence of characters includes a keyword that matches a first keyword of a first keyword-condition pair in the data structure, the first keyword-condition pair comprising the first keyword and a first condition, and determining that the first condition is satisfied; and performing a first action associated with the first keyword-condition pair in the data structure. 11. The method of claim 1 , wherein each of the actions associated with each of the keyword-condition pairs comprises a respective action type selected from the group consisting of (i) sending an e-mail, (ii) displaying a hypertext link, and (iii) going to a web page. | 0.563518 |
7,546,280 | 14 | 19 | 14. A system for identifying keywords, the system comprising: a processor; a memory coupled to the processor; computer code loaded into the memory for executing on the processor, for implementing the following functionality: a plurality of neurons arranged into a bidirectional neural network comprising a plurality of layers, the layers including words and documents, the neurons associated with words forming a first layer, and the neurons associated with documents forming a second layer, each neuron having multiple inputs from other neurons, a single output connecting the neuron to other neurons, and a threshold function at the single output, wherein at least some of the neurons of the first layer have connections between them and represent keywords in the documents; a plurality of connections between the neurons, wherein each neuron is connected to only some of the other neurons, such that at least some of the word neurons have connections between them; wherein, in response to an input query, the neural network outputs a plurality of keywords associated with documents that are contextually relevant to the input query. | 14. A system for identifying keywords, the system comprising: a processor; a memory coupled to the processor; computer code loaded into the memory for executing on the processor, for implementing the following functionality: a plurality of neurons arranged into a bidirectional neural network comprising a plurality of layers, the layers including words and documents, the neurons associated with words forming a first layer, and the neurons associated with documents forming a second layer, each neuron having multiple inputs from other neurons, a single output connecting the neuron to other neurons, and a threshold function at the single output, wherein at least some of the neurons of the first layer have connections between them and represent keywords in the documents; a plurality of connections between the neurons, wherein each neuron is connected to only some of the other neurons, such that at least some of the word neurons have connections between them; wherein, in response to an input query, the neural network outputs a plurality of keywords associated with documents that are contextually relevant to the input query. 19. The system of claim 14 , wherein the neural network is excited by a query that comprises words selected by a user. | 0.771318 |
7,567,896 | 1 | 4 | 1. A speech synthesis system for producing synthesized speech comprising: a large speech segment database referencing speech segments and accessed by segment designators, each segment designator being associated with a sequence of one or more speech segments; a segmental transcription database referencing segmental transcriptions associated with sequences of one or more segment designators and accessed by message designators, each message designator being associated with a fixed message; a speech segment selector for selecting a sequence of speech segments referenced by the large speech segment database and representative of a sequence of segment designators corresponding to a segmental transcription generated responsive to a message designator input; and a speech segment concatenator in communication with the large speech segment database for concatenating the sequence of speech segments selected by the speech segment selector to produce a speech signal output corresponding to the message designator input. | 1. A speech synthesis system for producing synthesized speech comprising: a large speech segment database referencing speech segments and accessed by segment designators, each segment designator being associated with a sequence of one or more speech segments; a segmental transcription database referencing segmental transcriptions associated with sequences of one or more segment designators and accessed by message designators, each message designator being associated with a fixed message; a speech segment selector for selecting a sequence of speech segments referenced by the large speech segment database and representative of a sequence of segment designators corresponding to a segmental transcription generated responsive to a message designator input; and a speech segment concatenator in communication with the large speech segment database for concatenating the sequence of speech segments selected by the speech segment selector to produce a speech signal output corresponding to the message designator input. 4. A speech synthesis system according to claim 1 , in which the speech segment concatenator smoothes energy at concatenation boundaries of the speech segments when concatenating the sequence of speech segments. | 0.636207 |
8,911,277 | 11 | 12 | 11. An interactive toy for responding to audible sounds corresponding to words read aloud from a book that form part of a story told in the book, the words including at least one non-triggering phrase, at least one triggering phrase, and a plurality of other words that are neither non-triggering phrases nor triggering phrases, the at least one triggering phrase being selectively placed among the plurality of other words that are neither non-triggering phrases nor triggering phrases and the at least one non-triggering phrase to assist the toy in differentiating the at least one triggering phrase from the other words, the toy comprising: a body having an interior cavity; a speech recognition unit at least partially positioned within the interior cavity of the body, the speech recognition unit configured to receive the audible sounds corresponding to the words read aloud from the book and to identify a combination of the at least one non-triggering phrase and the at least one triggering phrase in the audible sounds corresponding to the literary work; a memory coupled with the speech recognition unit and having stored therein the at least one non-triggering phrase and the at least one triggering phrase; a sound module at least partially positioned within the interior cavity of the body, the sound module having a controller with at least one audio message stored therein for selective playback via a speaker, wherein the sound module plays back the at least one message once the combination of the at least one non-triggering phrase and the at least one triggering phrase are received and identified by the speech recognition unit; and a user engagable switch for powering on the interactive toy. | 11. An interactive toy for responding to audible sounds corresponding to words read aloud from a book that form part of a story told in the book, the words including at least one non-triggering phrase, at least one triggering phrase, and a plurality of other words that are neither non-triggering phrases nor triggering phrases, the at least one triggering phrase being selectively placed among the plurality of other words that are neither non-triggering phrases nor triggering phrases and the at least one non-triggering phrase to assist the toy in differentiating the at least one triggering phrase from the other words, the toy comprising: a body having an interior cavity; a speech recognition unit at least partially positioned within the interior cavity of the body, the speech recognition unit configured to receive the audible sounds corresponding to the words read aloud from the book and to identify a combination of the at least one non-triggering phrase and the at least one triggering phrase in the audible sounds corresponding to the literary work; a memory coupled with the speech recognition unit and having stored therein the at least one non-triggering phrase and the at least one triggering phrase; a sound module at least partially positioned within the interior cavity of the body, the sound module having a controller with at least one audio message stored therein for selective playback via a speaker, wherein the sound module plays back the at least one message once the combination of the at least one non-triggering phrase and the at least one triggering phrase are received and identified by the speech recognition unit; and a user engagable switch for powering on the interactive toy. 12. The interactive toy of claim 11 , wherein the sound module only plays back the at least one message upon receipt and identification of the at least on triggering phrase after previous receipt and identification of the at least one non-triggering phrase. | 0.5 |
7,640,006 | 10 | 11 | 10. The method of claim 1 , wherein determining a contact preference comprises the information assistance application querying a virtual customer database system. | 10. The method of claim 1 , wherein determining a contact preference comprises the information assistance application querying a virtual customer database system. 11. The method of claim 10 , wherein, querying a virtual customer database system comprises accessing a customer proprietary information record having customer contact data that includes a contact preference, the customer proprietary information record associated with the subscriber terminal. | 0.5 |
9,792,367 | 1 | 3 | 1. A system implemented in hardware, comprising: a computer system comprising a processor executing a translation engine that is configured to: receive a search query containing one or more transliterated words; determine a source language corresponding to a particular transliterated word of the one or more transliterated words, wherein the determining the source language is based solely on the particular transliterated word, wherein the determining the source language comprises: determining a weighted score for each one of a plurality of candidate languages, and designating the candidate language with the highest weighted score as the source language; convert the particular transliterated word to a word in the source language; translate the word in the source language to a word in a target language; submit the word in the target language to an Internet search engine; receive search results in the target language, wherein the search results are based on the submitting the word in the target language to the Internet search engine; translate the search results, received from the Internet search engine, in the target language to search results in the source language; and displaying the search results in the source language. | 1. A system implemented in hardware, comprising: a computer system comprising a processor executing a translation engine that is configured to: receive a search query containing one or more transliterated words; determine a source language corresponding to a particular transliterated word of the one or more transliterated words, wherein the determining the source language is based solely on the particular transliterated word, wherein the determining the source language comprises: determining a weighted score for each one of a plurality of candidate languages, and designating the candidate language with the highest weighted score as the source language; convert the particular transliterated word to a word in the source language; translate the word in the source language to a word in a target language; submit the word in the target language to an Internet search engine; receive search results in the target language, wherein the search results are based on the submitting the word in the target language to the Internet search engine; translate the search results, received from the Internet search engine, in the target language to search results in the source language; and displaying the search results in the source language. 3. The system of claim 1 , wherein the weighted score for each one of the plurality of candidate languages is based on: correlating user-entered words against language-specific dictionaries; and environmental variables. | 0.640984 |
8,566,789 | 11 | 12 | 11. One or more non-transitory computer-readable storage media having computer-executable instructions for performing a method, the method comprising: receiving a query for software source code, wherein the query comprises one or more queried domain concept names, wherein at least one of the one or more domain concept names is indicated as prohibited; processing the query, wherein the processing comprises: expanding the query to include one or more query terms with relationships to the one or more queried domain concept names based on an ontology, wherein at least one of the one or more query terms is indicated as prohibited at least based on at least one of the relationships to the one or more queried domain concept names, the at least one of the relationships being with the at least one of the one or more domain concept names indicated as prohibited; assigning weights to the one or more query terms based on the relationships to the one or more queried domain concept names based on the ontology; if two domain concept names of the one or more queried domain concept names are indicated as both prohibited and required due to the expansion, the domain concept name with the greater weight is favored; if the two domain concept names have equal weights, the domain concept name indicated as prohibited is favored; finding where, within the software source code, one or more source code element names mapped to the one or more queried domain concept names appear; and displaying results of the query. | 11. One or more non-transitory computer-readable storage media having computer-executable instructions for performing a method, the method comprising: receiving a query for software source code, wherein the query comprises one or more queried domain concept names, wherein at least one of the one or more domain concept names is indicated as prohibited; processing the query, wherein the processing comprises: expanding the query to include one or more query terms with relationships to the one or more queried domain concept names based on an ontology, wherein at least one of the one or more query terms is indicated as prohibited at least based on at least one of the relationships to the one or more queried domain concept names, the at least one of the relationships being with the at least one of the one or more domain concept names indicated as prohibited; assigning weights to the one or more query terms based on the relationships to the one or more queried domain concept names based on the ontology; if two domain concept names of the one or more queried domain concept names are indicated as both prohibited and required due to the expansion, the domain concept name with the greater weight is favored; if the two domain concept names have equal weights, the domain concept name indicated as prohibited is favored; finding where, within the software source code, one or more source code element names mapped to the one or more queried domain concept names appear; and displaying results of the query. 12. The one or more computer-readable storage media of claim 11 further having encoded thereon a data structure comprising: a description of operations performed, within a unit of source code, on respective source code elements mapped to domain concept names, wherein the description uses respective domain concept names in place of names of the source code elements; a link to a location within the source code indicating where, within the source code, the source code unit within which the operations are performed appears, the link comprising a file name in which the source code unit appears and a starting line number of the source code unit. | 0.5 |
8,169,433 | 10 | 12 | 10. The three-dimensional model retrieval method according to claim 7 , wherein in said model describing operation, an image descriptor is generated for each of said two-dimensional images, and a collection of the image descriptors of all two-dimensional images is set as the model descriptor of the three-dimensional model. | 10. The three-dimensional model retrieval method according to claim 7 , wherein in said model describing operation, an image descriptor is generated for each of said two-dimensional images, and a collection of the image descriptors of all two-dimensional images is set as the model descriptor of the three-dimensional model. 12. The three-dimensional model retrieval method according to claim 10 , wherein said three-dimensional model retrieval method takes a two-dimensional image as the query, and in said retrieving, a distance between the two-dimensional image serving as the query and the three-dimensional model in the model database is calculated based on the model descriptor of the three-dimensional model in the model database to perform the retrieval based on the distance. | 0.538229 |
7,831,614 | 1 | 2 | 1. A method for generating a structured query language (SQL) script based on a template, the method comprises: selecting one object from a plurality of objects in a data model; automatically selecting, without user input, at least one first instruction based, at least in part, on a type of the selected object; selecting a first template string based on the selected at least one first instruction; selecting a second object from the plurality of objects in the data model; selecting at least one second instruction based, at least in part, on a type of the second object; selecting a second template string based on the selected at least one second instruction; using a script generator to automatically, and without user input, sort and concatenate the first and second template strings from the selected objects, the script generator automatically sorting and concatenating the first and second template strings in an order identified by the first and second instructions and based on the types of the first and second objects; and using one or more processors to automatically generate at least a portion of the SQL script based on the sorted and concatenated first and second template strings of the order identified by the first and second instructions. | 1. A method for generating a structured query language (SQL) script based on a template, the method comprises: selecting one object from a plurality of objects in a data model; automatically selecting, without user input, at least one first instruction based, at least in part, on a type of the selected object; selecting a first template string based on the selected at least one first instruction; selecting a second object from the plurality of objects in the data model; selecting at least one second instruction based, at least in part, on a type of the second object; selecting a second template string based on the selected at least one second instruction; using a script generator to automatically, and without user input, sort and concatenate the first and second template strings from the selected objects, the script generator automatically sorting and concatenating the first and second template strings in an order identified by the first and second instructions and based on the types of the first and second objects; and using one or more processors to automatically generate at least a portion of the SQL script based on the sorted and concatenated first and second template strings of the order identified by the first and second instructions. 2. The method of claim 1 , one or more of the plurality of objects comprising a user-defined object. | 0.842271 |
8,326,860 | 19 | 23 | 19. A system for processing a search query, comprising: a processor; logic for receiving a search query containing one or more terms; logic for processing the query to add one or more bi-words as terms to the query; logic for searching a search index embodied on a non-transitory computer-readable storage medium having product identifiers and logical parts of the product identifiers indexed into different fields in the index; logic for generating a score based on at least some of the terms matching the product identifiers and the individual logical parts of the product identifiers in the different fields in the index, wherein bi-words are weighted higher than the terms having only one word when matching in the index and individual terms are weighted higher when matching in the product identifier fields of the index as compared to matching the fields associated with the individual logical parts of the product identifiers; and logic for selecting and outputting an indicator of product identifiers ranked by their scores. | 19. A system for processing a search query, comprising: a processor; logic for receiving a search query containing one or more terms; logic for processing the query to add one or more bi-words as terms to the query; logic for searching a search index embodied on a non-transitory computer-readable storage medium having product identifiers and logical parts of the product identifiers indexed into different fields in the index; logic for generating a score based on at least some of the terms matching the product identifiers and the individual logical parts of the product identifiers in the different fields in the index, wherein bi-words are weighted higher than the terms having only one word when matching in the index and individual terms are weighted higher when matching in the product identifier fields of the index as compared to matching the fields associated with the individual logical parts of the product identifiers; and logic for selecting and outputting an indicator of product identifiers ranked by their scores. 23. The system as recited in claim 19 , wherein one or more fields of a context part of the index is given higher weight than other fields of the context part of the index. | 0.704467 |
8,977,904 | 9 | 10 | 9. The non-transitory computer readable medium of claim 7 , wherein the method further comprises replaying the replayable testing script to test the tested application. | 9. The non-transitory computer readable medium of claim 7 , wherein the method further comprises replaying the replayable testing script to test the tested application. 10. The non-transitory computer readable medium of claim 9 , wherein the method further comprises retrieving an assigned value of the dynamic data item obtained in a request or response in a current replay session and using that value in a consequent response or request in that session. | 0.5 |
8,952,796 | 7 | 11 | 7. A method comprising: receiving a first data from a first data input; receiving a second data from a second data input, the second data input different from the first data input; converting the first data into a first sensory input; converting the second data into a second sensory input; and providing a user with the first sensory input and the second sensory input via at least one sensory input device designed to be worn by the user, where the first sensory input and the second sensory input can be perceived by the user without the user having to distinguish between the first sensory input and the second sensory input and where said first sensory input and said second sensory input use different human senses. | 7. A method comprising: receiving a first data from a first data input; receiving a second data from a second data input, the second data input different from the first data input; converting the first data into a first sensory input; converting the second data into a second sensory input; and providing a user with the first sensory input and the second sensory input via at least one sensory input device designed to be worn by the user, where the first sensory input and the second sensory input can be perceived by the user without the user having to distinguish between the first sensory input and the second sensory input and where said first sensory input and said second sensory input use different human senses. 11. A method according to claim 7 , wherein receiving a first data and a second data includes receiving the first data and the second data, the first data and the second data dependent on a relative location of a sensor device. | 0.504367 |
10,002,526 | 8 | 9 | 8. The method of claim 1 , further comprising: generating, by the communication module, an appliance command based on the appliance-specific data, the appliance command controlling at least one function of the appliance; and transmitting, by the communication module through the data communication subsystem to the component of the appliance, the appliance command. | 8. The method of claim 1 , further comprising: generating, by the communication module, an appliance command based on the appliance-specific data, the appliance command controlling at least one function of the appliance; and transmitting, by the communication module through the data communication subsystem to the component of the appliance, the appliance command. 9. The method of claim 8 , wherein: the communication module cannot perform the steps of generating and transmitting the appliance command until after performing the steps of receiving the appliance message, transmitting the appliance data, and receiving the appliance-specific data. | 0.702731 |
8,700,547 | 6 | 7 | 6. A method for uncovering hidden structures in data embodied on a storage device, comprising executing operations implementing a spectral clustering algorithm on at least one automated computer, the operations comprising: characterizing clustering of data of at least first, second, and third types using first, second and third tentative cluster characterization matrices, respectively; and iteratively improving each tentative cluster characterization matrix using linear combinations of other matrices, the other matrices characterizing relationships between data of different types, wherein iteratively improving comprises calculating a matrix M (P) , which is a function of at least: at least one relation matrix representing respective relations between distinct members of m sets to be clustered into k p disjoint clusters, where p is an index running from 1 to m; at least one feature matrix where each element denotes a feature value for an associated data object; the tentative cluster characterization matrices; and a set of weights for different types of relations and features. | 6. A method for uncovering hidden structures in data embodied on a storage device, comprising executing operations implementing a spectral clustering algorithm on at least one automated computer, the operations comprising: characterizing clustering of data of at least first, second, and third types using first, second and third tentative cluster characterization matrices, respectively; and iteratively improving each tentative cluster characterization matrix using linear combinations of other matrices, the other matrices characterizing relationships between data of different types, wherein iteratively improving comprises calculating a matrix M (P) , which is a function of at least: at least one relation matrix representing respective relations between distinct members of m sets to be clustered into k p disjoint clusters, where p is an index running from 1 to m; at least one feature matrix where each element denotes a feature value for an associated data object; the tentative cluster characterization matrices; and a set of weights for different types of relations and features. 7. The method of claim 6 , wherein during each iteration, a tentative cluster matrix is updated as the k leading eigenvectors of the linear combinations. | 0.653846 |
8,812,480 | 34 | 35 | 34. The method of claim 33 , wherein the pipelined engines comprise: a deterministic finite automaton (DFA) engine; and a non-deterministic finite automaton (NFA) engine. | 34. The method of claim 33 , wherein the pipelined engines comprise: a deterministic finite automaton (DFA) engine; and a non-deterministic finite automaton (NFA) engine. 35. The method of claim 34 , wherein the pipelined engines further comprise: a token stitcher configured to combine partial match results from the DFA engine and the NFA engine to generate a match result for the filtered input string. | 0.5 |
7,836,002 | 11 | 12 | 11. One or more computer storage media storing computer-executable instructions for performing a method of applying a recognition system in accordance with a non-search activity a user is attempting to accomplish by using a computing device, the method comprising: accessing information related to a non-search activity a user is attempting to accomplish, wherein the accessed information comprises information related to at least one of the user, the user's environment, or the activity; inferring the non-search activity the user is attempting to accomplish from the accessed information; determining a domain comprising resources including one or more applications and at least one of one or more files, web links, functionalities, services, or views; scoping the domain into a plurality of sub-domains to facilitate accomplishment of the activity by: identifying domain resources, including one or more applications, to limit access to or functionality of for each sub-domain based on the activity, and limiting the access to or functionality of applications and other identified resources for each sub-domain to provide a focused workspace; for each sub-domain: narrowing a grammar and lexicon available for recognition in natural language and speech processing to provide more efficient processing by limiting the number of words and grammatical constructs against which a language or speech input is compared during processing, and maintaining the narrowed grammar and lexicon in the sub-domain; and interpreting a user input based at least in part upon a subset of the plurality of sub-domains. | 11. One or more computer storage media storing computer-executable instructions for performing a method of applying a recognition system in accordance with a non-search activity a user is attempting to accomplish by using a computing device, the method comprising: accessing information related to a non-search activity a user is attempting to accomplish, wherein the accessed information comprises information related to at least one of the user, the user's environment, or the activity; inferring the non-search activity the user is attempting to accomplish from the accessed information; determining a domain comprising resources including one or more applications and at least one of one or more files, web links, functionalities, services, or views; scoping the domain into a plurality of sub-domains to facilitate accomplishment of the activity by: identifying domain resources, including one or more applications, to limit access to or functionality of for each sub-domain based on the activity, and limiting the access to or functionality of applications and other identified resources for each sub-domain to provide a focused workspace; for each sub-domain: narrowing a grammar and lexicon available for recognition in natural language and speech processing to provide more efficient processing by limiting the number of words and grammatical constructs against which a language or speech input is compared during processing, and maintaining the narrowed grammar and lexicon in the sub-domain; and interpreting a user input based at least in part upon a subset of the plurality of sub-domains. 12. The computer storage media of claim 11 , wherein the method further comprises: gathering context information; and employing the context information in scoping the domain into the plurality of sub-domains. | 0.5 |
9,430,519 | 14 | 15 | 14. A system, comprising: a data store storing a dataset associated with one or more types of performance measures; and one or more processors that interact with the data store and execute instructions that cause the one or more data processing apparatus to perform operations comprising: receiving one or more requests for a first type of performance measure; identifying, based on the one or more requests and from a dataset, a proper subset of data used to determine a value for the first type of performance measure; determining the value based on the proper subset of data; determining a first latency specifying an amount of time that was required to determine the value using the dataset; defining a pre-aggregated dataset that includes the proper subset of data from the dataset; calculating a benefit score for the pre-aggregated dataset based on a difference between the first latency and a second latency specifying an amount of time required to respond to the request using the pre-aggregated dataset; adjusting the benefit score as a rate of received queries for the first type of performance measure changes over time; determining, at a given time, that the adjusted benefit score meets a threshold value; and storing the pre-aggregated dataset in response to determining that the benefit score meets the threshold value. | 14. A system, comprising: a data store storing a dataset associated with one or more types of performance measures; and one or more processors that interact with the data store and execute instructions that cause the one or more data processing apparatus to perform operations comprising: receiving one or more requests for a first type of performance measure; identifying, based on the one or more requests and from a dataset, a proper subset of data used to determine a value for the first type of performance measure; determining the value based on the proper subset of data; determining a first latency specifying an amount of time that was required to determine the value using the dataset; defining a pre-aggregated dataset that includes the proper subset of data from the dataset; calculating a benefit score for the pre-aggregated dataset based on a difference between the first latency and a second latency specifying an amount of time required to respond to the request using the pre-aggregated dataset; adjusting the benefit score as a rate of received queries for the first type of performance measure changes over time; determining, at a given time, that the adjusted benefit score meets a threshold value; and storing the pre-aggregated dataset in response to determining that the benefit score meets the threshold value. 15. The system of claim 14 , wherein the instructions cause the one or more data processing apparatus to perform operations comprising: receiving a second request for a second type of performance measure; determining that using the pre-aggregated dataset to determine a value for the second type of performance measure lowers a latency for responding to the second request relative to a latency for responding to the second request using the dataset to determine the value for the second type of performance measure; and increasing the benefit score for the pre-aggregated dataset in response to determining that using the pre-aggregated dataset to determine the value for the second type of performance measure lowers the latency for responding to the second request. | 0.542857 |
8,510,795 | 9 | 32 | 9. A computer implemented method for distinguishing a human user from a computer software agent in an online application, the method comprising: selecting a video test from a video tests database, wherein the video test includes a video segment and one of a plurality of queries associated with the video segment, each query having a set of correct answers, and wherein the video test is generated based on the association of the video segment, the plurality of queries and the set of correct answers; modifying the video segment of the video test such that the modified video segment is visually similar to the unmodified video segment and a hash value generated from the modified video segment is different from the hash value generated from the unmodified video segment to prevent identifying the selected video clip based on the hash value of the unmodified video segment, wherein modifying the video segment comprises: selecting one or more video frames from a video of the video clips database; and re-encoding the selected video segment with the selected video frames; displaying the modified video test; receiving a response to the displayed query from the user; and responsive to the received response matching one of the correct answers, determining the user to be human user. | 9. A computer implemented method for distinguishing a human user from a computer software agent in an online application, the method comprising: selecting a video test from a video tests database, wherein the video test includes a video segment and one of a plurality of queries associated with the video segment, each query having a set of correct answers, and wherein the video test is generated based on the association of the video segment, the plurality of queries and the set of correct answers; modifying the video segment of the video test such that the modified video segment is visually similar to the unmodified video segment and a hash value generated from the modified video segment is different from the hash value generated from the unmodified video segment to prevent identifying the selected video clip based on the hash value of the unmodified video segment, wherein modifying the video segment comprises: selecting one or more video frames from a video of the video clips database; and re-encoding the selected video segment with the selected video frames; displaying the modified video test; receiving a response to the displayed query from the user; and responsive to the received response matching one of the correct answers, determining the user to be human user. 32. The method of claim 9 , wherein modifying the selected video segment such that the modified video segment is visually similar to the unmodified video segment comprises: selecting one or more video frames from a video of the video clips database; and inserting the selected video frames to the selected video segment. | 0.5 |
7,756,915 | 1 | 21 | 1. A digital music library builder comprising: a receiver to receive broadcast audio from a first broadcast station, and to receive a broadcast image from a second broadcast station; a song extractor, coupled to the receiver, to extract a song from the received broadcast audio, comprising an audio parser to mark the start and end of a song within the received broadcast audio; a meta-data generator, coupled to the receiver, to identify meta-data for the extracted song from the received broadcast image, comprising a luminance extractor to remove color burst noise from the received broadcast image; and a memory, coupled to the song extractor and the meta-data generator, wherein a memory manager automatically stores the extracted song in a digital music library in the memory and automatically associates the identified meta-data with the stored song, within the digital music library. | 1. A digital music library builder comprising: a receiver to receive broadcast audio from a first broadcast station, and to receive a broadcast image from a second broadcast station; a song extractor, coupled to the receiver, to extract a song from the received broadcast audio, comprising an audio parser to mark the start and end of a song within the received broadcast audio; a meta-data generator, coupled to the receiver, to identify meta-data for the extracted song from the received broadcast image, comprising a luminance extractor to remove color burst noise from the received broadcast image; and a memory, coupled to the song extractor and the meta-data generator, wherein a memory manager automatically stores the extracted song in a digital music library in the memory and automatically associates the identified meta-data with the stored song, within the digital music library. 21. The digital music library builder of claim 1 wherein the first broadcast station is a cable or a satellite broadcast station. | 0.749027 |
9,479,839 | 11 | 12 | 11. A non-transitory computer-readable storage media having computer readable instructions stored thereon which when executed by at least one processor, cause the at least one processor to: generate a representative phrase based on a correlation between broadcast information and a popular keyword from a content page, the generated representative phrase indicating information about at least one program that is associated with the broadcast information and the popular keyword, the popular keyword being at least a keyword input entered into a search engine a desired number of times over a desired period of time; calculate a time score for determining the association of the popular keyword to a program of the broadcast information, the calculating based on a display time of the popular keyword on the content page and a broadcast time of the program, the time score based on a desired margin of error and the broadcast time of the program; determine the program associated with the popular keyword, the determining based on at least the calculated time score; generate the representative phrase by combining at least a portion of the broadcast information with a determined airtime of the determined program; generate a display representative phrase by combining the generated representative phrase with the popular keyword; provide the display representative phrase in the content page for display in association with the determined program, the providing including displaying the display representative phrase on the content page; and determine the program of the broadcast information associated with the popular keyword. | 11. A non-transitory computer-readable storage media having computer readable instructions stored thereon which when executed by at least one processor, cause the at least one processor to: generate a representative phrase based on a correlation between broadcast information and a popular keyword from a content page, the generated representative phrase indicating information about at least one program that is associated with the broadcast information and the popular keyword, the popular keyword being at least a keyword input entered into a search engine a desired number of times over a desired period of time; calculate a time score for determining the association of the popular keyword to a program of the broadcast information, the calculating based on a display time of the popular keyword on the content page and a broadcast time of the program, the time score based on a desired margin of error and the broadcast time of the program; determine the program associated with the popular keyword, the determining based on at least the calculated time score; generate the representative phrase by combining at least a portion of the broadcast information with a determined airtime of the determined program; generate a display representative phrase by combining the generated representative phrase with the popular keyword; provide the display representative phrase in the content page for display in association with the determined program, the providing including displaying the display representative phrase on the content page; and determine the program of the broadcast information associated with the popular keyword. 12. The non-transitory computer-readable storage media of claim 11 , wherein the computer readable instructions, when executed, causes the at least one processor to: select the determined program based on a broadcast start time and a broadcast end time being closest to a time when the popular keyword is obtained, if multiple programs are determined. | 0.548843 |
8,712,832 | 12 | 14 | 12. A computer program product for allocating an advertising budget among a fixed set of keywords, each keyword having a bid, a bid intensity, and a utility associated therewith, the computer program product comprising at least one non-transitory computer readable medium having computer program instructions stored therein operable when executed by at least one computing device to: raise the bid intensities associated with selected ones of the keywords such that the advertising budget is not exceeded, the selected keywords having the highest utilities among the fixed set of keywords; and when the bid intensities associated with the selected keywords reach maximum values, raise the bids associated with first ones of the selected keywords such that the advertising budget is not exceeded, the first selected keywords having the highest utilities among the selected keywords. | 12. A computer program product for allocating an advertising budget among a fixed set of keywords, each keyword having a bid, a bid intensity, and a utility associated therewith, the computer program product comprising at least one non-transitory computer readable medium having computer program instructions stored therein operable when executed by at least one computing device to: raise the bid intensities associated with selected ones of the keywords such that the advertising budget is not exceeded, the selected keywords having the highest utilities among the fixed set of keywords; and when the bid intensities associated with the selected keywords reach maximum values, raise the bids associated with first ones of the selected keywords such that the advertising budget is not exceeded, the first selected keywords having the highest utilities among the selected keywords. 14. The computer program product of claim 12 wherein the computer program instructions are further operable when executed by at least one computing device to initially set the bid associated with each keyword at a minimum bid which guarantees appearance of a link for the associated keyword among sponsored search links associated with search results. | 0.516529 |
8,078,468 | 1 | 2 | 1. A method comprising: monitoring a speech signal produced from utterances received via a microphone of a client device; identifying, by the client device, a plurality of terms contained in the speech signal, retaining, by the client device, one or more of the identified terms by comparing the identified terms to a set of filtering terms; storing, by the client device, corresponding information for each of the retained terms including: an indication of a number of the utterances of the retained term, chronological information recorded for each of the utterances, and geographical information determined for each of the utterances; sending, by the client device based on a request received from a remote device, a subset of the retained terms, based on and the corresponding information associated with the subset of the retained terms, to one or more components of the remote device; and receiving, by the client device, messages, related to one or more of the subset of the retained terms and related to the corresponding information associated with the one or more of the subset of the retained terms, from the one or more components of the remote device. | 1. A method comprising: monitoring a speech signal produced from utterances received via a microphone of a client device; identifying, by the client device, a plurality of terms contained in the speech signal, retaining, by the client device, one or more of the identified terms by comparing the identified terms to a set of filtering terms; storing, by the client device, corresponding information for each of the retained terms including: an indication of a number of the utterances of the retained term, chronological information recorded for each of the utterances, and geographical information determined for each of the utterances; sending, by the client device based on a request received from a remote device, a subset of the retained terms, based on and the corresponding information associated with the subset of the retained terms, to one or more components of the remote device; and receiving, by the client device, messages, related to one or more of the subset of the retained terms and related to the corresponding information associated with the one or more of the subset of the retained terms, from the one or more components of the remote device. 2. The method of claim 1 , wherein retaining one or more of the identified terms includes: eliminating, from the plurality of terms, a word contained in the set of filtering terms. | 0.800443 |
8,428,241 | 3 | 4 | 3. The method of claim 1 , further comprising the steps of: associating the recorded speech that represents the request by the caller for the destination with the recorded destination identifying information; and updating a destination map using the recorded speech that is associated with the recorded destination identifying information. | 3. The method of claim 1 , further comprising the steps of: associating the recorded speech that represents the request by the caller for the destination with the recorded destination identifying information; and updating a destination map using the recorded speech that is associated with the recorded destination identifying information. 4. The method of claim 3 , further comprising the step of: associating recognized speech from a subsequent caller with a corresponding destination using the updated destination map. | 0.5 |
7,849,071 | 2 | 11 | 2. The method of claim 1 further comprising utilising one or more processes selected from the group consisting of: returning a list of probable locations in response to a search term comprising a non-geographical search term associated with a geographical location name; determining a geographical distance between a geographical location derived from the search term and a derived geographical location of a user; the user submitting criteria including a locality name, identifying from the criteria the locality name and returning, in dependence on the locality name, a probability that the locality name is associated with a geographical location; categorising search terms in a query into a local activity or a remote activity; analysing the order in which words appear in a search query string, in combination with a search in a locations database, to return a likelihood of a search term relating to a list of probable associated locations; and inferring a point of interest associated with the geographical location derived from the search term. | 2. The method of claim 1 further comprising utilising one or more processes selected from the group consisting of: returning a list of probable locations in response to a search term comprising a non-geographical search term associated with a geographical location name; determining a geographical distance between a geographical location derived from the search term and a derived geographical location of a user; the user submitting criteria including a locality name, identifying from the criteria the locality name and returning, in dependence on the locality name, a probability that the locality name is associated with a geographical location; categorising search terms in a query into a local activity or a remote activity; analysing the order in which words appear in a search query string, in combination with a search in a locations database, to return a likelihood of a search term relating to a list of probable associated locations; and inferring a point of interest associated with the geographical location derived from the search term. 11. The method according to claim 2 , wherein derived geographical information concerning the location of the user is used in combination with a local activity (“near”) or a remote activity (“far”) categorisation to determine whether a “near” or a “far” geographical location to the user location is sought by the search term. | 0.678501 |
8,516,458 | 12 | 13 | 12. A code-portion-handling tool for operating on a code portion, the code portion being an instance of a second data structure of an implementation generated by a computer-programming tool, for generating an implementation of a first data structure, the first data structure representing at least a portion of a computer-programming language, the language comprising a plurality of syntactical elements satisfying a set of syntax rules and the first data structure comprising a plurality of linked nodes, such nodes comprising a root node, a number of first-tier nodes linked directly to the root node, and a number of subsequent-tier nodes linked indirectly to the root node via one or more other said nodes, the nodes representing such syntactical elements of the language and a pattern of links between the nodes representing paths of inheritance of substitutability but not implementation and interface between those nodes, the tool comprising: memory operable to store one of said first data structure and a description thereof; and at least one processor configured to generate, from one said first data structure one of a second data structure and a description thereof, corresponding to said first data structure, the second data structure comprising nodes corresponding to the nodes of the first data structure with all nodes of the second data structure other than its root node being linked directly to the root node of the second data structure, wherein the links between the nodes of the second data structure are representative of paths of inheritance of substitutability, implementation and interface between those nodes, the processor being further operable to generate, based on the pattern of links of said one said first data structure, implementation rules which define the rules of substitutability of the first data structure to be enforced in relation to nodes of the second data structure during a subsequent processing operation which utilizes said second data structure in order to establish compliance with the inheritance of substitutability represented by the first data structure, wherein the second data structure and the implementation rules form one said implementation of the first data structure, the code portion being expressed in the computer-programming language, the code-portion-handling tool comprising: memory for storing said implementation; and at least one processor configured for operating on a candidate code portion in dependence upon the implementation, the candidate code portion comprising instance nodes corresponding to nodes of the second data structure. | 12. A code-portion-handling tool for operating on a code portion, the code portion being an instance of a second data structure of an implementation generated by a computer-programming tool, for generating an implementation of a first data structure, the first data structure representing at least a portion of a computer-programming language, the language comprising a plurality of syntactical elements satisfying a set of syntax rules and the first data structure comprising a plurality of linked nodes, such nodes comprising a root node, a number of first-tier nodes linked directly to the root node, and a number of subsequent-tier nodes linked indirectly to the root node via one or more other said nodes, the nodes representing such syntactical elements of the language and a pattern of links between the nodes representing paths of inheritance of substitutability but not implementation and interface between those nodes, the tool comprising: memory operable to store one of said first data structure and a description thereof; and at least one processor configured to generate, from one said first data structure one of a second data structure and a description thereof, corresponding to said first data structure, the second data structure comprising nodes corresponding to the nodes of the first data structure with all nodes of the second data structure other than its root node being linked directly to the root node of the second data structure, wherein the links between the nodes of the second data structure are representative of paths of inheritance of substitutability, implementation and interface between those nodes, the processor being further operable to generate, based on the pattern of links of said one said first data structure, implementation rules which define the rules of substitutability of the first data structure to be enforced in relation to nodes of the second data structure during a subsequent processing operation which utilizes said second data structure in order to establish compliance with the inheritance of substitutability represented by the first data structure, wherein the second data structure and the implementation rules form one said implementation of the first data structure, the code portion being expressed in the computer-programming language, the code-portion-handling tool comprising: memory for storing said implementation; and at least one processor configured for operating on a candidate code portion in dependence upon the implementation, the candidate code portion comprising instance nodes corresponding to nodes of the second data structure. 13. A code-portion-handling tool as claimed in claim 12 , wherein: said second data structure is in an input form in which its instance nodes and links therebetween are not explicitly expressed; and the at least one processor is configured for converting the received candidate code portion into an abstracted form in which the instance nodes and links therebetween are explicitly expressed. | 0.5 |
9,946,524 | 9 | 12 | 9. An apparatus configured to compile a source code described by a first programming language, the apparatus comprising: a memory; and a processor coupled to the memory and configured to: specify a function from the source code, the function being described by a second programming language different from the first programming language from the source code, the function being allowed to use one or more arguments, and create an instruction to store given data that causes occurrence of an error when a register is accessed due to execution of the function, and store the given data that causes occurrence of the error in an area in the register for an argument that is not set for the function in the source code among the one or more arguments. | 9. An apparatus configured to compile a source code described by a first programming language, the apparatus comprising: a memory; and a processor coupled to the memory and configured to: specify a function from the source code, the function being described by a second programming language different from the first programming language from the source code, the function being allowed to use one or more arguments, and create an instruction to store given data that causes occurrence of an error when a register is accessed due to execution of the function, and store the given data that causes occurrence of the error in an area in the register for an argument that is not set for the function in the source code among the one or more arguments. 12. The apparatus according to claim 9 , wherein the given data is a null value. | 0.947849 |
8,188,978 | 1 | 2 | 1. A method of enabling input into a handheld electronic device comprising an input apparatus, a processor, and a memory, the input apparatus comprising a plurality of input keys, at least some of the input keys each having a number of linguistic elements assigned thereto, the memory having stored therein a plurality of language objects and a plurality of word frames associated with the plurality of language objects, the method comprising: detecting an ambiguous input comprising sequential selections of the input keys; identifying, from the stored word frames, a word frame corresponding with the ambiguous input, the word frame comprising a root portion and a contracted portion, the root portion comprising one or more linguistic elements, the contracted portion comprising a representation of one of the plurality of input keys; identifying a language object associated with the identified word frame, the associated language object comprising the linguistic elements of the root portion, and one or more linguistic elements corresponding with the contracted portion; and outputting the associated language object. | 1. A method of enabling input into a handheld electronic device comprising an input apparatus, a processor, and a memory, the input apparatus comprising a plurality of input keys, at least some of the input keys each having a number of linguistic elements assigned thereto, the memory having stored therein a plurality of language objects and a plurality of word frames associated with the plurality of language objects, the method comprising: detecting an ambiguous input comprising sequential selections of the input keys; identifying, from the stored word frames, a word frame corresponding with the ambiguous input, the word frame comprising a root portion and a contracted portion, the root portion comprising one or more linguistic elements, the contracted portion comprising a representation of one of the plurality of input keys; identifying a language object associated with the identified word frame, the associated language object comprising the linguistic elements of the root portion, and one or more linguistic elements corresponding with the contracted portion; and outputting the associated language object. 2. The method of claim 1 , further comprising: identifying, from the stored language objects, one or more language objects corresponding with the ambiguous input, each of the identified language objects comprising a sequential plurality of linguistic elements corresponding with the sequentially selected input keys of the ambiguous input. | 0.5 |
8,457,947 | 8 | 9 | 8. A hybrid translation method comprising: generalizing an input source language sentence for each node; transforming, using a computer, the source language sentence generalized for each node into a node expression using statistics-based translation knowledge DB to generate a first translation result; store learning data generalized for each node to be acquired to utilize the learning data as transformation knowledge for statistics-based translation; repeatedly performing the generation of a target word for each node on the first translation result using pattern-based knowledge to generate a second translation result as target words for the respective nodes; and combining the first translation result and the second translation result to output a target language sentence, wherein before said generalizing: performing the generic transformation for a node expression using all sentence pairs of a pre-constructed parallel corpus, and storing acquired training data in a database of the statistics-based translation knowledge DB. | 8. A hybrid translation method comprising: generalizing an input source language sentence for each node; transforming, using a computer, the source language sentence generalized for each node into a node expression using statistics-based translation knowledge DB to generate a first translation result; store learning data generalized for each node to be acquired to utilize the learning data as transformation knowledge for statistics-based translation; repeatedly performing the generation of a target word for each node on the first translation result using pattern-based knowledge to generate a second translation result as target words for the respective nodes; and combining the first translation result and the second translation result to output a target language sentence, wherein before said generalizing: performing the generic transformation for a node expression using all sentence pairs of a pre-constructed parallel corpus, and storing acquired training data in a database of the statistics-based translation knowledge DB. 9. The method of claim 8 , wherein said combining performs combination by inserting a target word for each node based on the second translation result as a substitute to each node position of the first translation result. | 0.621575 |
9,471,565 | 1 | 9 | 1. A method comprising: performing a generic web crawl to identify a first webpage in a first language having a link thereon which points to a second webpage in a second language, wherein the first webpage and the second webpage comprise a bilingual website comprising the first webpage and the second webpage in respective languages; based on an analysis of parameters on the first webpage comprising at least two of: the link pointing to the second webpage, a title, a link neighborhood, a link context and data indicating a separate version of the first webpage, classifying the first webpage as a root page and as an entry point for the bilingual website via the link to the second webpage; identifying, using a visitation policy which constrains web-crawling to a graph neighborhood of bilingual websites, a pattern of links within between the first webpage and the second webpage, to yield a bipartite graph; ranking a relevance of candidate links which point to parallel text in the first webpage and the second webpage, to yield classifications of links based on the bipartite graph and the relevance; performing, based on the relevance, a bidirectional web crawl of the candidate links, to identify the first webpage and the second webpage as a bilingual website, the bidirectional web crawl utilizing the classifications of links to avoid links having a low respective relevance; analyzing the first webpage and the second webpage to identify information pairs in the first language and the second language; extracting the information pairs from the first webpage and the second webpage for use in a language translation model, the information pairs comprising at least one of a sentence pair and a paragraph pair; and updating a statistical model with domain representative data using the information pairs. | 1. A method comprising: performing a generic web crawl to identify a first webpage in a first language having a link thereon which points to a second webpage in a second language, wherein the first webpage and the second webpage comprise a bilingual website comprising the first webpage and the second webpage in respective languages; based on an analysis of parameters on the first webpage comprising at least two of: the link pointing to the second webpage, a title, a link neighborhood, a link context and data indicating a separate version of the first webpage, classifying the first webpage as a root page and as an entry point for the bilingual website via the link to the second webpage; identifying, using a visitation policy which constrains web-crawling to a graph neighborhood of bilingual websites, a pattern of links within between the first webpage and the second webpage, to yield a bipartite graph; ranking a relevance of candidate links which point to parallel text in the first webpage and the second webpage, to yield classifications of links based on the bipartite graph and the relevance; performing, based on the relevance, a bidirectional web crawl of the candidate links, to identify the first webpage and the second webpage as a bilingual website, the bidirectional web crawl utilizing the classifications of links to avoid links having a low respective relevance; analyzing the first webpage and the second webpage to identify information pairs in the first language and the second language; extracting the information pairs from the first webpage and the second webpage for use in a language translation model, the information pairs comprising at least one of a sentence pair and a paragraph pair; and updating a statistical model with domain representative data using the information pairs. 9. The method of claim 1 , wherein a frontier scheduler generates a list of links for use in the bidirectional web crawl. | 0.772556 |
7,970,812 | 1 | 2 | 1. A computer storage medium having instructions stored thereon, said instructions causing a computer to perform a process, said process for adjusting spacing of recognition results and comprising the steps of: receiving recognition results from ink-to-text conversions, wherein the recognition results include a plurality of characters of varying size converted from user-entered electronic ink; displaying the recognition results, wherein the plurality of characters are displayed in a plurality of display boxes arranged in a line, wherein the edge of the displayed recognition results that is closest to the middle of the display is a current writing location capable of receiving additional user-entered electronic ink, wherein each character has an initial character width and an initial font size, wherein each of the plurality of display boxes has an initial box width and contains at least one character, the initial box width of each of the plurality of display boxes equal to the sum of the initial character widths of the characters in the correspinding display box, and wherein the display includes at least one modification region that receives user selection or editing of at least one of the plurality of characters included in the recognition results; determining that a first display box has an initial box width that is less than a minimum width predefined by a user-controlled setting; calculating an adjusted box width for the first display box based on the total width of the plurality of display boxes in the line, the total number of characters in the plurality of display boxes in the line, and the number of characters in the first display box; adjusting a font size of each character within the first display box to create adjusted characters such that each of the adjusted characters fits within the adjusted box width of the first display box; and adjusting the displayed recognition results to reflect the adjusted box width of the first display box and the adjusted characters, wherein the current writing location remains fixed during the adjustment such that the adjustment does not affect the overall width of the displayed recognition results. | 1. A computer storage medium having instructions stored thereon, said instructions causing a computer to perform a process, said process for adjusting spacing of recognition results and comprising the steps of: receiving recognition results from ink-to-text conversions, wherein the recognition results include a plurality of characters of varying size converted from user-entered electronic ink; displaying the recognition results, wherein the plurality of characters are displayed in a plurality of display boxes arranged in a line, wherein the edge of the displayed recognition results that is closest to the middle of the display is a current writing location capable of receiving additional user-entered electronic ink, wherein each character has an initial character width and an initial font size, wherein each of the plurality of display boxes has an initial box width and contains at least one character, the initial box width of each of the plurality of display boxes equal to the sum of the initial character widths of the characters in the correspinding display box, and wherein the display includes at least one modification region that receives user selection or editing of at least one of the plurality of characters included in the recognition results; determining that a first display box has an initial box width that is less than a minimum width predefined by a user-controlled setting; calculating an adjusted box width for the first display box based on the total width of the plurality of display boxes in the line, the total number of characters in the plurality of display boxes in the line, and the number of characters in the first display box; adjusting a font size of each character within the first display box to create adjusted characters such that each of the adjusted characters fits within the adjusted box width of the first display box; and adjusting the displayed recognition results to reflect the adjusted box width of the first display box and the adjusted characters, wherein the current writing location remains fixed during the adjustment such that the adjustment does not affect the overall width of the displayed recognition results. 2. The medium of claim 1 , wherein each of the characters is either a character of a first type or a character of a second type. | 0.875728 |
8,209,163 | 1 | 4 | 1. A machine translation system, comprising: a translation component receiving a textual input in a first language and generating a textual translation hypothesis in a second language; a grammatical element prediction component coupled to the translation component to receive the translation hypothesis and information corresponding to the textual input and, thereafter, the grammatical element prediction component identifying phrases in the translation hypothesis and assigning scores to possible grammatical elements for each phrase, the possible grammatical elements including at least one of function words, case markers, and inflections, and the grammatical element prediction component assigning the possible grammatical elements to each phrase in the translation hypothesis based on words in the translation hypothesis and the information corresponding to the textual input, wherein the grammatical element prediction component comprises at least one statistical model trained to assign the scores to the possible grammatical elements and wherein the prediction component determines whether a given phrase has a predicted grammatical element, the grammatical elements prediction component assigning the scores based on both monolingual features of the second language and bilingual features of the first and second languages; a re-ranking component generating a plurality of additional textual translation hypotheses by, after the translation component generates the textual translation hypothesis, varying only the grammatical elements assigned to the textual translation hypothesis using the grammatical element prediction component and generating a rank ordered set of textual translation hypotheses based on accuracy features indicative of an accuracy of the grammatical elements assigned to each of the textual translation hypothesis and the additional textual translation hypotheses, wherein the accuracy features include a count of the words without semantic meaning added to each of the additional textual translation hypotheses; and a computer processor, being a functional part of the machine translation system, and activated by the grammatical element prediction component to facilitate assigning scores to the possible grammatical elements. | 1. A machine translation system, comprising: a translation component receiving a textual input in a first language and generating a textual translation hypothesis in a second language; a grammatical element prediction component coupled to the translation component to receive the translation hypothesis and information corresponding to the textual input and, thereafter, the grammatical element prediction component identifying phrases in the translation hypothesis and assigning scores to possible grammatical elements for each phrase, the possible grammatical elements including at least one of function words, case markers, and inflections, and the grammatical element prediction component assigning the possible grammatical elements to each phrase in the translation hypothesis based on words in the translation hypothesis and the information corresponding to the textual input, wherein the grammatical element prediction component comprises at least one statistical model trained to assign the scores to the possible grammatical elements and wherein the prediction component determines whether a given phrase has a predicted grammatical element, the grammatical elements prediction component assigning the scores based on both monolingual features of the second language and bilingual features of the first and second languages; a re-ranking component generating a plurality of additional textual translation hypotheses by, after the translation component generates the textual translation hypothesis, varying only the grammatical elements assigned to the textual translation hypothesis using the grammatical element prediction component and generating a rank ordered set of textual translation hypotheses based on accuracy features indicative of an accuracy of the grammatical elements assigned to each of the textual translation hypothesis and the additional textual translation hypotheses, wherein the accuracy features include a count of the words without semantic meaning added to each of the additional textual translation hypotheses; and a computer processor, being a functional part of the machine translation system, and activated by the grammatical element prediction component to facilitate assigning scores to the possible grammatical elements. 4. The machine translation system of claim 1 wherein the translation component generates a plurality of rank ordered translation hypotheses and wherein the re-ranking component re-ranks the plurality of rank ordered translation hypotheses based on the grammatical elements assigned to each hypothesis. | 0.5 |
9,158,768 | 2 | 5 | 2. The system of claim 1 , wherein the dictionary generator module is further configured to: generate dictionary information that corresponds to a second user, the dictionary information that corresponds to the second user includes a second list of compatible query operators and a description of compatible data types that correspond to each operator in the second list of compatible query operators, wherein the dictionary information that corresponds to the second user is different from the dictionary information that corresponds to the first user. | 2. The system of claim 1 , wherein the dictionary generator module is further configured to: generate dictionary information that corresponds to a second user, the dictionary information that corresponds to the second user includes a second list of compatible query operators and a description of compatible data types that correspond to each operator in the second list of compatible query operators, wherein the dictionary information that corresponds to the second user is different from the dictionary information that corresponds to the first user. 5. The system of claim 2 , wherein the query receiver module is further configured to: receive, from the second user, a search query to search for document information in the database, wherein the search query from the second user is identical to the search query from the first user. | 0.503497 |
9,361,504 | 6 | 7 | 6. An optical information reading method, comprising: adding of 2 elements through n elements, the 2 elements containing a bar and a space that are adjacent within one character, wherein n is greater than 2, the n greater than 2 elements containing two or more bars and one or more spaces and one or more bars and two or more spaces that are adjacent within one character to obtain respective 2- through n-element added widths; converting each of the obtained 2- through n-element added widths into a module number with respect to one character to obtain an actual measurement value of the module number of the 2- through n-element added widths; extracting a candidate character by narrowing down target characters wherein each of errors between the actual measurement value and an ideal value of the module number for all the 1-element and the obtained 2- through n-element added widths is equal to or less than 1, to extract candidate characters; and searching the one candidate character having the minimum of the summed errors as the readout decode result. | 6. An optical information reading method, comprising: adding of 2 elements through n elements, the 2 elements containing a bar and a space that are adjacent within one character, wherein n is greater than 2, the n greater than 2 elements containing two or more bars and one or more spaces and one or more bars and two or more spaces that are adjacent within one character to obtain respective 2- through n-element added widths; converting each of the obtained 2- through n-element added widths into a module number with respect to one character to obtain an actual measurement value of the module number of the 2- through n-element added widths; extracting a candidate character by narrowing down target characters wherein each of errors between the actual measurement value and an ideal value of the module number for all the 1-element and the obtained 2- through n-element added widths is equal to or less than 1, to extract candidate characters; and searching the one candidate character having the minimum of the summed errors as the readout decode result. 7. The optical information reading method according to claim 6 wherein the adding includes adding the widths of the elements containing the character bars and the spaces of the characters before and after the target character when obtaining each of the 2- through n-element added widths of the target character. | 0.693294 |
8,626,511 | 12 | 13 | 12. A system comprising: one or more computers; and a computer-readable medium coupled to the one or more computers having instructions stored thereon which, when executed by the one or more computers, cause the one or more computers to perform operations comprising: obtaining two or more candidate transcriptions of a single voice command; identifying one or more possible intended actions for each of the two or more candidate transcriptions of the single voice command, including identifying two or more possible intended actions for a particular transcription of the two or more candidate transcriptions of the single voice command; providing information for display, the information identifying (i) the two or more candidate transcriptions of the single voice command, and (ii) the one or more possible intended actions for each of the two or more transcriptions of the single voice command, including the two or more possible intended actions for the particular transcription; receiving data indicating a selection of a particular possible intended action from among the displayed one or more possible intended actions for each of the two or more transcriptions of the single voice command, and the displayed two or more possible intended actions for the particular transcription; and invoking the selected particular possible intended action. | 12. A system comprising: one or more computers; and a computer-readable medium coupled to the one or more computers having instructions stored thereon which, when executed by the one or more computers, cause the one or more computers to perform operations comprising: obtaining two or more candidate transcriptions of a single voice command; identifying one or more possible intended actions for each of the two or more candidate transcriptions of the single voice command, including identifying two or more possible intended actions for a particular transcription of the two or more candidate transcriptions of the single voice command; providing information for display, the information identifying (i) the two or more candidate transcriptions of the single voice command, and (ii) the one or more possible intended actions for each of the two or more transcriptions of the single voice command, including the two or more possible intended actions for the particular transcription; receiving data indicating a selection of a particular possible intended action from among the displayed one or more possible intended actions for each of the two or more transcriptions of the single voice command, and the displayed two or more possible intended actions for the particular transcription; and invoking the selected particular possible intended action. 13. The system of claim 12 , wherein, when the particular command transcription comprises a name of a person, the two or more possible intended actions for the particular transcription includes two or more of (i) an action that initiates a call, (ii) an action that initiates an email, and (iii) an action that initiates an instant messaging session with the person. | 0.5 |
10,007,717 | 13 | 14 | 13. The computer implemented method of claim 1 , further comprising: identifying the additional communication; and determining the additional communication is associated with the indication of the classification based on comparing the feature set to content of the additional communication. | 13. The computer implemented method of claim 1 , further comprising: identifying the additional communication; and determining the additional communication is associated with the indication of the classification based on comparing the feature set to content of the additional communication. 14. The computer implemented method of claim 13 , wherein the indication of the classification is an indication of the data extraction parser for that classification, and further comprising: selecting the data extraction parser for parsing the additional communication. | 0.5 |
9,996,611 | 1 | 14 | 1. A method using a computer having a hardware processor to classify a plurality of users in social media, the method comprising the steps of: generating, using the hardware processor, a content feature vector for each of a portion of users of the plurality of users on the basis of content associated with the portion of users; generating, using the hardware processor, a plurality of clusters on the basis of the content feature vectors; mapping, using the hardware processor the portion of users to the plurality of clusters on the basis of the content feature vectors; generating, using the hardware processor, a first profile feature vector for each of the plurality of clusters on the basis of profiles associated with corresponding users within the portion of users mapped to each cluster; and classifying, using the hardware processor, each of the other users excluding the portion of users into the plurality of clusters on the basis of profiles associated with the other users and the first profile feature vectors, and outputting, via display device associated with said computer, clusters corresponding to the classified other users. | 1. A method using a computer having a hardware processor to classify a plurality of users in social media, the method comprising the steps of: generating, using the hardware processor, a content feature vector for each of a portion of users of the plurality of users on the basis of content associated with the portion of users; generating, using the hardware processor, a plurality of clusters on the basis of the content feature vectors; mapping, using the hardware processor the portion of users to the plurality of clusters on the basis of the content feature vectors; generating, using the hardware processor, a first profile feature vector for each of the plurality of clusters on the basis of profiles associated with corresponding users within the portion of users mapped to each cluster; and classifying, using the hardware processor, each of the other users excluding the portion of users into the plurality of clusters on the basis of profiles associated with the other users and the first profile feature vectors, and outputting, via display device associated with said computer, clusters corresponding to the classified other users. 14. The method according to claim 1 , wherein the number of the portion of users is smaller than the number of other users, and the amount of information in a profile is less than the amount of information in the content. | 0.716667 |
7,657,433 | 21 | 22 | 21. The method of claim 19 , wherein the feature is defined over a continuous range and the partitions are defined automatically as ranges within the continuous range. | 21. The method of claim 19 , wherein the feature is defined over a continuous range and the partitions are defined automatically as ranges within the continuous range. 22. The method of claim 21 , wherein the automatically defined ranges within the continuous range are based on a greedy iterative partitioning technique. | 0.5 |
8,700,568 | 2 | 4 | 2. The method of claim 1 , wherein normalizing the value of the name fact comprises: normalizing the value of the name fact by applying a group of normalization rules to the value of the name fact. | 2. The method of claim 1 , wherein normalizing the value of the name fact comprises: normalizing the value of the name fact by applying a group of normalization rules to the value of the name fact. 4. The method of claim 2 , wherein the group of normalization rules comprises at least one rule selected from a group consisting of: removing single letter words; removing punctuation marks; removing stop words; and converting uppercase characters into lowercase. | 0.5 |
7,743,317 | 9 | 10 | 9. A computer-readable storage medium that stores a set of instructions which when executed perform a method for pasting spreadsheet elements, the method executed by the set of instructions comprising: determining a selection of a spreadsheet element having a first set of formatting properties in a source document; determining a paste point in a target document; comparing the first set of formatting properties associated with the spreadsheet element to a second set of formatting properties associated with the target document in order to determine an intended set of formatting properties for the spreadsheet element when included in the target document, wherein comparing the first set of formatting properties associated with the spreadsheet element to the second set of formatting properties associated with the target document comprises: determining whether source style properties of the source document have been at least one of: used and modified in the target document, in response to a determination that the source style properties have been at least one of: used and modified in the target document, setting target style properties as intended style properties associated with the intended formatting properties, and in response to a determination that the source style properties have not been at least one of: used and modified in the target document, setting the source style properties as the intended style properties associated with the intended formatting properties; determining if the selected spreadsheet element is a single cell; and if the selected spreadsheet element is not a single cell, pasting the spreadsheet element as a table at the paste point in the target document with the intended set of formatting properties applied thereto. | 9. A computer-readable storage medium that stores a set of instructions which when executed perform a method for pasting spreadsheet elements, the method executed by the set of instructions comprising: determining a selection of a spreadsheet element having a first set of formatting properties in a source document; determining a paste point in a target document; comparing the first set of formatting properties associated with the spreadsheet element to a second set of formatting properties associated with the target document in order to determine an intended set of formatting properties for the spreadsheet element when included in the target document, wherein comparing the first set of formatting properties associated with the spreadsheet element to the second set of formatting properties associated with the target document comprises: determining whether source style properties of the source document have been at least one of: used and modified in the target document, in response to a determination that the source style properties have been at least one of: used and modified in the target document, setting target style properties as intended style properties associated with the intended formatting properties, and in response to a determination that the source style properties have not been at least one of: used and modified in the target document, setting the source style properties as the intended style properties associated with the intended formatting properties; determining if the selected spreadsheet element is a single cell; and if the selected spreadsheet element is not a single cell, pasting the spreadsheet element as a table at the paste point in the target document with the intended set of formatting properties applied thereto. 10. The computer-readable storage medium of claim 9 , further comprising, if the selected spreadsheet element is a single cell, pasting contents of the spreadsheet cell as text at the paste point in the target document. | 0.5 |
8,601,062 | 12 | 13 | 12. The method of claim 11 , further including producing formatting information associated with the conversation topic of a respective conversation, such that, when displayed, the conversation topic is highlighted when the conversation includes at least one message that has not been viewed or marked as read by the user. | 12. The method of claim 11 , further including producing formatting information associated with the conversation topic of a respective conversation, such that, when displayed, the conversation topic is highlighted when the conversation includes at least one message that has not been viewed or marked as read by the user. 13. The method of claim 12 , wherein the portion of the conversation includes first text information relevant to at least one search term of the search query. | 0.5 |
8,099,407 | 1 | 15 | 1. A computer-implemented method for processing media files using a computer, comprising: monitoring at least one application for the occurrences of events wherein at least one event is associated with a media file; capturing the at least one event upon the occurrence of the event by queuing event data associated with the event at a position in a queue; indexing and storing at least some of the events and the media file associated with the event at a time after the occurrence of the event, wherein the time is based on performance data indicating a readiness to process the event and a position in the queue; receiving a search query; locating at least one relevant media file from the indexed and stored events relevant to the search query; and outputting a result set comprising the at least one relevant media file. | 1. A computer-implemented method for processing media files using a computer, comprising: monitoring at least one application for the occurrences of events wherein at least one event is associated with a media file; capturing the at least one event upon the occurrence of the event by queuing event data associated with the event at a position in a queue; indexing and storing at least some of the events and the media file associated with the event at a time after the occurrence of the event, wherein the time is based on performance data indicating a readiness to process the event and a position in the queue; receiving a search query; locating at least one relevant media file from the indexed and stored events relevant to the search query; and outputting a result set comprising the at least one relevant media file. 15. The method of claim 1 , wherein capturing the event associated with the media file comprises identifying the event based at least in part on one or more of network activity, system activity, and media application activity. | 0.573585 |
7,523,108 | 1 | 11 | 1. A method of providing records to a user, comprising: identifying a set of different languages of terms submitted in a query by the user; using the set of languages to automatically discern a set of suspected geographical origins from which the user may have corrected to a server; finding a best combination of a language and a suspected geographical origin from the sets of languages and origins; and using the best combination to guide retrieval of search results responsive to the query and to rank the search results. | 1. A method of providing records to a user, comprising: identifying a set of different languages of terms submitted in a query by the user; using the set of languages to automatically discern a set of suspected geographical origins from which the user may have corrected to a server; finding a best combination of a language and a suspected geographical origin from the sets of languages and origins; and using the best combination to guide retrieval of search results responsive to the query and to rank the search results. 11. The method of claim 1 , further comprising providing a display to the user that includes first and second areas, each of which contains a portion of the search results. | 0.524862 |
9,432,396 | 22 | 24 | 22. One or more non-transitory storage media storing instructions which, when executed by one or more computing devices, cause: extracting a set of accessed domain names from a set of events stored in a field-searchable data store; identifying a respective registration time for each accessed domain name in the set of accessed domain names, wherein the respective registration time is indicative of when the accessed domain name was registered with a registrar; identifying a subset of accessed domain names in the set of accessed domain names for which the identified respective registration time of each accessed domain name in the subset is recent relative to times for other accessed domain names in the set of accessed domain names; determining, for each accessed domain name in the subset, an access count corresponding to how many times the set of events indicates that the accessed domain name in the subset was accessed; causing display of information relating to the access count corresponding to each accessed domain name in the subset. | 22. One or more non-transitory storage media storing instructions which, when executed by one or more computing devices, cause: extracting a set of accessed domain names from a set of events stored in a field-searchable data store; identifying a respective registration time for each accessed domain name in the set of accessed domain names, wherein the respective registration time is indicative of when the accessed domain name was registered with a registrar; identifying a subset of accessed domain names in the set of accessed domain names for which the identified respective registration time of each accessed domain name in the subset is recent relative to times for other accessed domain names in the set of accessed domain names; determining, for each accessed domain name in the subset, an access count corresponding to how many times the set of events indicates that the accessed domain name in the subset was accessed; causing display of information relating to the access count corresponding to each accessed domain name in the subset. 24. The one or more non-transitory storage media of claim 22 , wherein the respective registration time comprises an age from a first time when the accessed domain name was registered with the registrar to a current time. | 0.866223 |
8,515,737 | 1 | 7 | 1. A non-transitory processor-readable medium storing code representing instructions that when executed cause a processor to: select a narrative template based at least in part on a predetermined content type associated with a real-world event; select a narrative tone type based at least in part on a tone associated with the real-world event; and for each phrase included in an ordered set of phrases associated with the narrative template: select, based at least in part on the narrative tone type, a phrase variation from a set of phrase variations associated with that phrase; define, based on the selected phrase variation and at least one datum from a set of data, a narrative content portion associated with the real-world event; and send a signal such that the narrative content portion is output at a display. | 1. A non-transitory processor-readable medium storing code representing instructions that when executed cause a processor to: select a narrative template based at least in part on a predetermined content type associated with a real-world event; select a narrative tone type based at least in part on a tone associated with the real-world event; and for each phrase included in an ordered set of phrases associated with the narrative template: select, based at least in part on the narrative tone type, a phrase variation from a set of phrase variations associated with that phrase; define, based on the selected phrase variation and at least one datum from a set of data, a narrative content portion associated with the real-world event; and send a signal such that the narrative content portion is output at a display. 7. The non-transitory processor-readable medium of claim 1 , wherein the code further represents instructions that when executed cause the processor to: store, at a memory, the narrative content portion. | 0.706647 |
9,311,408 | 15 | 17 | 15. A computer readable medium configured to store instructions, the instructions when executed by a processor cause the processor to: monitor at least one application for occurrences of events wherein at least one event is associated with a media file; capture the at least one event upon the occurrence of the event by queuing event data associated with the at least one event at a position in the queue; and index and store at least some of the event data and the media file associated with the at least one event at a time after the occurrence of the at least one event, wherein the time is based on performance data indicating a readiness to process the at least one event and the position in the queue. | 15. A computer readable medium configured to store instructions, the instructions when executed by a processor cause the processor to: monitor at least one application for occurrences of events wherein at least one event is associated with a media file; capture the at least one event upon the occurrence of the event by queuing event data associated with the at least one event at a position in the queue; and index and store at least some of the event data and the media file associated with the at least one event at a time after the occurrence of the at least one event, wherein the time is based on performance data indicating a readiness to process the at least one event and the position in the queue. 17. The computer readable medium of claim 15 , wherein capturing the at least one event associated with the media file comprises determining event data external to the media file. | 0.717666 |
8,775,442 | 1 | 6 | 1. A method, comprising: providing a data store including documents; providing a semantic model including a plurality of concepts, wherein the semantic model is derived at least in part from a reference source that includes content not included in the data store; determining at least one similarity metric for each document of the plurality of documents, wherein each respective similarity metric represents a similarity between a respective document of the plurality of documents and a respective concept of the plurality of concepts in the semantic model; receiving a search query; computing at least one relevance metric of the search query, wherein each relevance metric represents a relevance of the search query to a respective concept of the plurality of concepts represented in the semantic model; and determining a ranking of at least a subset of the plurality of documents with respect to the search query using at least the at least one similarity metric and the at least one relevance metric. | 1. A method, comprising: providing a data store including documents; providing a semantic model including a plurality of concepts, wherein the semantic model is derived at least in part from a reference source that includes content not included in the data store; determining at least one similarity metric for each document of the plurality of documents, wherein each respective similarity metric represents a similarity between a respective document of the plurality of documents and a respective concept of the plurality of concepts in the semantic model; receiving a search query; computing at least one relevance metric of the search query, wherein each relevance metric represents a relevance of the search query to a respective concept of the plurality of concepts represented in the semantic model; and determining a ranking of at least a subset of the plurality of documents with respect to the search query using at least the at least one similarity metric and the at least one relevance metric. 6. The method of claim 1 , wherein the semantic model is stored on a user device. | 0.811628 |
9,436,927 | 1 | 6 | 1. A computer-implemented method for web-based multiuser collaboration, the method comprising executing instructions in one or more computer systems to perform the operations of: modeling a document on a server into semantic pieces as a plurality of cells for a construct of the document; identifying at least one cell of interest of the plurality of cells to at least one user of a plurality of users, the at least one cell of interest comprising a portion of the document cached on the server; in response to identifying the at least one cell of interest, sending the cached portion of the at least one cell of interest to a browser associated with the at least one user; associating the at least one cell of interest with the at least one user; storing an association between the at least one cell of interest and the at least one user; obtaining a first revision of interest to the at least one cell of interest; determining, based upon the association between the at least one cell of interest and the at least one user, that the first revision of interest is to be sent to the browser associated with the at least one user; and sending a cached portion of the first revision of interest to the browser associated with the at least one user, whereby the cached portion of the first revision is configured to be displayed by the browser along with a first identifier indicating the association between the at least one cell of interest and the at least one user. | 1. A computer-implemented method for web-based multiuser collaboration, the method comprising executing instructions in one or more computer systems to perform the operations of: modeling a document on a server into semantic pieces as a plurality of cells for a construct of the document; identifying at least one cell of interest of the plurality of cells to at least one user of a plurality of users, the at least one cell of interest comprising a portion of the document cached on the server; in response to identifying the at least one cell of interest, sending the cached portion of the at least one cell of interest to a browser associated with the at least one user; associating the at least one cell of interest with the at least one user; storing an association between the at least one cell of interest and the at least one user; obtaining a first revision of interest to the at least one cell of interest; determining, based upon the association between the at least one cell of interest and the at least one user, that the first revision of interest is to be sent to the browser associated with the at least one user; and sending a cached portion of the first revision of interest to the browser associated with the at least one user, whereby the cached portion of the first revision is configured to be displayed by the browser along with a first identifier indicating the association between the at least one cell of interest and the at least one user. 6. The computer-implemented method of claim 1 , wherein identifying the at least one cell of interest to the at least one user of the plurality of users include: sending names of pages of the document to the browser associated with the at least one user of the plurality of users, along with a content of a first page of the document; and receiving a selection of a name of a page of the document from the browser associated with the at least one user of the plurality of users. | 0.5 |
9,026,509 | 11 | 14 | 11. The method of claim 7 , wherein said displaying comprises displaying said stored related terms in groups according to a stored Boolean relation and the received term as it appear in said same query. | 11. The method of claim 7 , wherein said displaying comprises displaying said stored related terms in groups according to a stored Boolean relation and the received term as it appear in said same query. 14. The method of claim 11 , comprising ranking said stored related terms separately for different Boolean relations, and wherein said displaying is in an order according to the separate ranking. | 0.572368 |
9,684,721 | 21 | 40 | 21. A system, comprising: one or more memory devices; and one or more processors coupled to the one or more memory devices; wherein the one or more memory devices store machine readable instructions that, when executed by the one or more processors, cause the one or more processors to: receive a user input in an imprecise syntax, wherein: the user input in the imprecise syntax includes at least (i) word, or a phrase, that corresponds to a formula having a plurality of mathematical or scientific parameters, and (ii) one or more words, and/or one or more phrases, that correspond to one or more parameter values corresponding to the formula, the user input in the imprecise syntax is expressed using natural language and/or informal terminology spoken by a user, and the user input in the imprecise syntax corresponds to a digital representation of an audio signal corresponding to speech spoken by the user, the digital representation of the audio signal corresponding to the speech spoken by the user having been generated by a voice recognition system; wherein the one or more memory devices store further machine readable instructions that, when executed by the one or more processors, cause the one or more processors to: analyze the user input in the imprecise syntax to determine the formula with the one or more parameter values integrated into the formula; calculate a numerical result using the determined formula with the one or more parameter values integrated into the determined formula; and generate an output, using the numerical result, to perform a physical machine action. | 21. A system, comprising: one or more memory devices; and one or more processors coupled to the one or more memory devices; wherein the one or more memory devices store machine readable instructions that, when executed by the one or more processors, cause the one or more processors to: receive a user input in an imprecise syntax, wherein: the user input in the imprecise syntax includes at least (i) word, or a phrase, that corresponds to a formula having a plurality of mathematical or scientific parameters, and (ii) one or more words, and/or one or more phrases, that correspond to one or more parameter values corresponding to the formula, the user input in the imprecise syntax is expressed using natural language and/or informal terminology spoken by a user, and the user input in the imprecise syntax corresponds to a digital representation of an audio signal corresponding to speech spoken by the user, the digital representation of the audio signal corresponding to the speech spoken by the user having been generated by a voice recognition system; wherein the one or more memory devices store further machine readable instructions that, when executed by the one or more processors, cause the one or more processors to: analyze the user input in the imprecise syntax to determine the formula with the one or more parameter values integrated into the formula; calculate a numerical result using the determined formula with the one or more parameter values integrated into the determined formula; and generate an output, using the numerical result, to perform a physical machine action. 40. The system of claim 21 , further comprising: the voice recognition system, coupled to the one or more processors. | 0.948819 |
8,452,778 | 7 | 8 | 7. A non-transitory computer-readable storage medium storing executable computer program instructions comprising: instructions for storing a taxonomy of hierarchically-arranged categories; instructions for storing a set of labeled videos, each of the labeled videos having associated textual metadata and being initially labeled as representing one or more of the categories; instructions for storing labels initially associated with a set of text documents distinct from the labeled videos, each stored label corresponding to one of the categories and indicating that the associated text document represents the category; instructions for identifying, for each of the categories, a positive training subset of the text documents that represent the category based on their stored labels, and a negative training subset of the text documents that do not represent the category based on their stored labels; instructions for training a set of text-based classifiers based on the positive training subsets and the negative training subsets, each text-based classifier associated with one of the categories and producing, when applied to text, a score providing a measure of how strongly the text represents the associated category; instructions for identifying, for each of the categories, a positive training subset of the labeled videos that represent the category based on their labels, and a negative training subset of the labeled videos that do not represent the category based on their labels; instructions for, for each video of the positive training subsets of the labeled videos and of the negative training subsets of the labeled videos: applying the text-based classifiers to the associated textual metadata of the video, thereby producing a vector of scores for the video, the scores providing measures of how strongly the textual metadata of the video represents the categories associated with the text-based classifiers; extracting a content feature vector from video content of frames of the video; forming a hybrid feature vector comprising the vector of scores and the content feature vector for the video; and instructions for training a set of adapted classifiers based on the hybrid feature vectors of the videos in the positive training subsets of the labeled videos and on the hybrid feature vectors of the videos in the negative training subsets of the labeled videos, each adapted classifier associated with one of the categories and producing, when applied to an unlabeled video, a score providing a measure of how strongly the unlabeled video represents the associated category. | 7. A non-transitory computer-readable storage medium storing executable computer program instructions comprising: instructions for storing a taxonomy of hierarchically-arranged categories; instructions for storing a set of labeled videos, each of the labeled videos having associated textual metadata and being initially labeled as representing one or more of the categories; instructions for storing labels initially associated with a set of text documents distinct from the labeled videos, each stored label corresponding to one of the categories and indicating that the associated text document represents the category; instructions for identifying, for each of the categories, a positive training subset of the text documents that represent the category based on their stored labels, and a negative training subset of the text documents that do not represent the category based on their stored labels; instructions for training a set of text-based classifiers based on the positive training subsets and the negative training subsets, each text-based classifier associated with one of the categories and producing, when applied to text, a score providing a measure of how strongly the text represents the associated category; instructions for identifying, for each of the categories, a positive training subset of the labeled videos that represent the category based on their labels, and a negative training subset of the labeled videos that do not represent the category based on their labels; instructions for, for each video of the positive training subsets of the labeled videos and of the negative training subsets of the labeled videos: applying the text-based classifiers to the associated textual metadata of the video, thereby producing a vector of scores for the video, the scores providing measures of how strongly the textual metadata of the video represents the categories associated with the text-based classifiers; extracting a content feature vector from video content of frames of the video; forming a hybrid feature vector comprising the vector of scores and the content feature vector for the video; and instructions for training a set of adapted classifiers based on the hybrid feature vectors of the videos in the positive training subsets of the labeled videos and on the hybrid feature vectors of the videos in the negative training subsets of the labeled videos, each adapted classifier associated with one of the categories and producing, when applied to an unlabeled video, a score providing a measure of how strongly the unlabeled video represents the associated category. 8. The non-transitory computer-readable storage medium of claim 7 , wherein the text-based classifiers are trained with a first learning algorithm, and the adapted classifiers are trained with a second learning algorithm different from the first learning algorithm. | 0.726804 |
9,268,793 | 1 | 9 | 1. A method performed by data processing apparatus, the method comprising: receiving, by the data processing apparatus, data identifying images determined to be responsive to a query, each image having an associated search score that is a measure of responsiveness to the query, and the images ranked according to a first order based on the search scores, and in response to receiving the data identifying the images: for each image in a first set of the images, detecting, by the data processing apparatus, a face depicted in the image and generating a face template from the depicted face by processing the image using a facial template generation process that identifies machine-identifiable properties of structures of a face, and where each face template is a temporary face template that is not persisted to memory; clustering, by the data processing apparatus, the images in the first set of images into a plurality of clusters based on similarity of the facial templates to each other, each cluster including at least one of the images, and each image belonging to only one cluster; determining, by the data processing apparatus, a quantity of images belonging to each cluster; selecting, by the data processing apparatus, the cluster with the highest quantity relative to the quantity of other clusters as an inlier cluster; for each image in the first set of images, determining, by the data processing apparatus, an inlier score for the image that is a measure of similarity of the facial template of the image to the facial templates of the images that belong to the inlier cluster; and re-ranking, by the data processing apparatus, the images determined to be responsive to a query based on the inlier scores of the set of images; wherein the determining the inlier score comprises, for images that belong to the inlier cluster that are determined to be duplicate images of each other, using only one of the images from the images determined to be duplicate images of each other to determine the inlier score. | 1. A method performed by data processing apparatus, the method comprising: receiving, by the data processing apparatus, data identifying images determined to be responsive to a query, each image having an associated search score that is a measure of responsiveness to the query, and the images ranked according to a first order based on the search scores, and in response to receiving the data identifying the images: for each image in a first set of the images, detecting, by the data processing apparatus, a face depicted in the image and generating a face template from the depicted face by processing the image using a facial template generation process that identifies machine-identifiable properties of structures of a face, and where each face template is a temporary face template that is not persisted to memory; clustering, by the data processing apparatus, the images in the first set of images into a plurality of clusters based on similarity of the facial templates to each other, each cluster including at least one of the images, and each image belonging to only one cluster; determining, by the data processing apparatus, a quantity of images belonging to each cluster; selecting, by the data processing apparatus, the cluster with the highest quantity relative to the quantity of other clusters as an inlier cluster; for each image in the first set of images, determining, by the data processing apparatus, an inlier score for the image that is a measure of similarity of the facial template of the image to the facial templates of the images that belong to the inlier cluster; and re-ranking, by the data processing apparatus, the images determined to be responsive to a query based on the inlier scores of the set of images; wherein the determining the inlier score comprises, for images that belong to the inlier cluster that are determined to be duplicate images of each other, using only one of the images from the images determined to be duplicate images of each other to determine the inlier score. 9. The method of claim 1 , wherein determining an inlier score for the image comprises determining a central tendency similarity measure of the facial template of the image to the facial templates of the images that belong to the inlier cluster. | 0.734273 |
9,934,778 | 2 | 3 | 2. The method of claim 1 , wherein converting the non-back-off language model to the back-off language model utilizes a background language model. | 2. The method of claim 1 , wherein converting the non-back-off language model to the back-off language model utilizes a background language model. 3. The method of claim 2 , wherein the background language model comprises at least one lower order converted language model, the at least one lower order converted language model comprising at least one lower order non-back-off language model previously converted into a back-off language model. | 0.5 |
9,930,085 | 1 | 2 | 1. A method for configuring an audio channel with a processor, the method comprising: generating a confidence metric indicative of at least one control cue in a telecommunication audio feed, wherein generating the confidence metric comprises: analyzing the at least one control cue to determine a cue type; assigning a confidence metric value for the at least one control cue based on the cue type, wherein the cue type comprises both explicit speech to perform an action and muffled voice having a lower amplitude than the average amplitude of other portions of the audio feed; comparing the confidence metric value to a predetermined threshold value associated with the cue type; updating a context history with the cue type and the confidence metric value; and configuring an input of the audio channel based on the confidence metric and the context history. | 1. A method for configuring an audio channel with a processor, the method comprising: generating a confidence metric indicative of at least one control cue in a telecommunication audio feed, wherein generating the confidence metric comprises: analyzing the at least one control cue to determine a cue type; assigning a confidence metric value for the at least one control cue based on the cue type, wherein the cue type comprises both explicit speech to perform an action and muffled voice having a lower amplitude than the average amplitude of other portions of the audio feed; comparing the confidence metric value to a predetermined threshold value associated with the cue type; updating a context history with the cue type and the confidence metric value; and configuring an input of the audio channel based on the confidence metric and the context history. 2. The method of claim 1 , wherein the processor configures the input of the audio channel by one of muting the input and unmuting the input. | 0.748214 |
9,026,529 | 18 | 19 | 18. The data processing system of claim 2 , wherein the a sub-systems configured to accomplish determining a member-level demographic characteristic and determining a first demographic characteristic further comprise: a sub-system configured to accomplish assigning each member, of the first result-base, a confidence distribution, wherein a plurality of values are assigned, each with a confidence level; and a sub-system configured to accomplish combining the confidence distributions. | 18. The data processing system of claim 2 , wherein the a sub-systems configured to accomplish determining a member-level demographic characteristic and determining a first demographic characteristic further comprise: a sub-system configured to accomplish assigning each member, of the first result-base, a confidence distribution, wherein a plurality of values are assigned, each with a confidence level; and a sub-system configured to accomplish combining the confidence distributions. 19. The data processing system of claim 18 , wherein each confidence distribution represents a demographic characteristic with two values. | 0.5 |
4,797,930 | 7 | 8 | 7. A speech producing apparatus as claimed in claim 6, wherein: said control means further includes a falling mode primary accent pitch pattern assignment means for assigning to the primary accent syllable a pitch pattern steeply declining in frequency if the primary accent falls on a syllable which is the only syllable, for assigning to the primary accent syllable a pitch pattern moderately declining in frequency if the primary accent falls on the last of a plurality of syllables and for assigning to the primary accent syllable a pitch pattern only slightly declining in frequency if the primary accent falls on an intermediate syllable of a plurality of syllables, whenever said rise/fall indicia indicates a falling mode. | 7. A speech producing apparatus as claimed in claim 6, wherein: said control means further includes a falling mode primary accent pitch pattern assignment means for assigning to the primary accent syllable a pitch pattern steeply declining in frequency if the primary accent falls on a syllable which is the only syllable, for assigning to the primary accent syllable a pitch pattern moderately declining in frequency if the primary accent falls on the last of a plurality of syllables and for assigning to the primary accent syllable a pitch pattern only slightly declining in frequency if the primary accent falls on an intermediate syllable of a plurality of syllables, whenever said rise/fall indicia indicates a falling mode. 8. A speech producing apparatus as claimed in claim 7, wherein: said control means further includes a rising mode primary accent pitch pattern assignment means for assigning to the primary accent syllable a pitch pattern sharply increasing in frequency if the primary accent falls on a syllable which is the only syllable, for assigning to the primary accent syllable a pitch pattern moderately rising in frequency if the primary accent falls on the last of a plurality of syllables and for assigning to the primary accent syllable a pitch pattern only slightly rising in frequency if the primary accent falls on an intermediate syllable of a plurality of syllables, whenever said rise/fall indicia indicates a rising mode. | 0.5 |
10,122,535 | 1 | 4 | 1. A computer implemented method comprising: receiving, at a computing device, a document designated for notarization and identification information including a photograph; requesting, from a server, a code-word; activating a camera on the computing device; prompting a user of the computing device to center an image displayed on the computing device from the camera on the user and to sign and write the code-word on a touch input of the computing device; capturing a photograph of the user and a signature of the user that includes the code-word; superimposing the signature of the user including the code-word on the photograph of the user to permit the server to verify a contemporaneousness of the photograph of the user with the signature of the user based on determining that the signature of the user includes the code-word; sending, to the server for notarization, the document designated for notarization, the identification information, and the photograph of the user superimposed with the signature of the user including the code-word. | 1. A computer implemented method comprising: receiving, at a computing device, a document designated for notarization and identification information including a photograph; requesting, from a server, a code-word; activating a camera on the computing device; prompting a user of the computing device to center an image displayed on the computing device from the camera on the user and to sign and write the code-word on a touch input of the computing device; capturing a photograph of the user and a signature of the user that includes the code-word; superimposing the signature of the user including the code-word on the photograph of the user to permit the server to verify a contemporaneousness of the photograph of the user with the signature of the user based on determining that the signature of the user includes the code-word; sending, to the server for notarization, the document designated for notarization, the identification information, and the photograph of the user superimposed with the signature of the user including the code-word. 4. The method of claim 1 further comprising: presenting at least one document notarization option; receiving a selection of an option; and sending, to the server, data indicating the selected option. | 0.768605 |
7,949,106 | 8 | 9 | 8. The method of claim 1 wherein said signal is received from a content server that delivers said content stream to said telecommunications terminal. | 8. The method of claim 1 wherein said signal is received from a content server that delivers said content stream to said telecommunications terminal. 9. The method of claim 8 wherein said signal indicates one of: (i) the start of playback of said content stream, and (ii) the completion of playback of said content stream. | 0.5 |
5,544,049 | 14 | 17 | 14. A computer implemented method for performing a search of a corpus of documents for similarity to a query having multiple terms using an inverted index of the corpus of documents, the method comprising the sequential steps of: (a) retrieving a first portion of the inverted index corresponding to a first plurality of documents from a first memory into a second memory; (b) searching the first portion of the inverted index for each of the query terms and determining a number of occurrences of the query terms in each of the documents in the first portion of the inverted index; (c) calculating similarity scores for each of the documents in the first portion of the inverted index based on the number of occurrences of the query terms in the documents; (d) retrieving a subsequent portion of the inverted index corresponding to a subsequent plurality of documents from the first memory into the second memory; (e) searching the subsequent portion of the inverted index for each of the query terms and determining a number of occurrences of the query terms in each of the documents in the subsequent portion of the inverted index; (f) calculating similarity scores for each of the documents in the subsequent portion of the inverted index based on the number of occurrences of the query terms in the documents; and (g) repeating steps (d) through (f) until a similarity score has been calculated for each of the documents in the corpus of documents. | 14. A computer implemented method for performing a search of a corpus of documents for similarity to a query having multiple terms using an inverted index of the corpus of documents, the method comprising the sequential steps of: (a) retrieving a first portion of the inverted index corresponding to a first plurality of documents from a first memory into a second memory; (b) searching the first portion of the inverted index for each of the query terms and determining a number of occurrences of the query terms in each of the documents in the first portion of the inverted index; (c) calculating similarity scores for each of the documents in the first portion of the inverted index based on the number of occurrences of the query terms in the documents; (d) retrieving a subsequent portion of the inverted index corresponding to a subsequent plurality of documents from the first memory into the second memory; (e) searching the subsequent portion of the inverted index for each of the query terms and determining a number of occurrences of the query terms in each of the documents in the subsequent portion of the inverted index; (f) calculating similarity scores for each of the documents in the subsequent portion of the inverted index based on the number of occurrences of the query terms in the documents; and (g) repeating steps (d) through (f) until a similarity score has been calculated for each of the documents in the corpus of documents. 17. The method of claim 14, wherein the query terms comprise images, and wherein the inverted index comprises an inverted index of images that occur in the plurality of documents. | 0.633197 |
9,753,962 | 1 | 15 | 1. A method in a host organization having at least a processor and a memory therein to execute instructions, the method comprising: receiving a request at the host organization from a user device to display a tabular dataset; retrieving the tabular dataset from a database system executing at the host organization; displaying the tabular dataset as output to the user device, the displayed output including a plurality of data values depicted as known values and a plurality of null values depicted as unknown values; receiving input from the user device to populate the tabular dataset to a specified fill percentage; querying the database system for predicted values to populate a portion of the null values of the tabular dataset, wherein querying the database system comprises issuing a PREDICT command term and passing as a parameter one or more specified columns of the tabular dataset to be predicted; receiving a distribution for every one of the plurality of null values within the tabular dataset responsive to querying the indices for the predicted values; calculating a credible interval for each distribution received; populating the portion of the null values of the tabular dataset with the predicted values until the specified fill percentage is reached; and displaying the tabular dataset having the predicted values populated therein as updated output to the user device by displaying selected ones of the predicted values that correspond to a calculated credible interval in excess of a minimum threshold. | 1. A method in a host organization having at least a processor and a memory therein to execute instructions, the method comprising: receiving a request at the host organization from a user device to display a tabular dataset; retrieving the tabular dataset from a database system executing at the host organization; displaying the tabular dataset as output to the user device, the displayed output including a plurality of data values depicted as known values and a plurality of null values depicted as unknown values; receiving input from the user device to populate the tabular dataset to a specified fill percentage; querying the database system for predicted values to populate a portion of the null values of the tabular dataset, wherein querying the database system comprises issuing a PREDICT command term and passing as a parameter one or more specified columns of the tabular dataset to be predicted; receiving a distribution for every one of the plurality of null values within the tabular dataset responsive to querying the indices for the predicted values; calculating a credible interval for each distribution received; populating the portion of the null values of the tabular dataset with the predicted values until the specified fill percentage is reached; and displaying the tabular dataset having the predicted values populated therein as updated output to the user device by displaying selected ones of the predicted values that correspond to a calculated credible interval in excess of a minimum threshold. 15. The method of claim 1 , further comprising: displaying a prediction difficulty score for every column of the tabular dataset displayed as output to the user on a per-column basis, wherein the prediction difficulty score is calculated for each column of the tabular dataset by: (i) identifying all unknown values within the column; (ii) querying the indices for a predicted value corresponding to each of the unknown values identified within the column; (iii) receiving the confidence indicator for each of the unknown values identified within the column; and (iv) calculating the prediction difficulty score for the column based on the confidence indicators received for the unknown values identified within the column. | 0.633739 |
9,946,708 | 8 | 9 | 8. A computer program product for identifying word-senses, the computer program product comprising: one or more computer-readable storage media and program instructions stored on the one or more computer-readable storage media, the program instructions comprising: program instructions to identify a frequency of occurrence value of a received word from each of a plurality of domain tables, wherein each of the plurality of domain tables comprises a frequency of occurrence value corresponding to the received word, a word-sense corresponding to the received word, and temporal properties corresponding to the received word, wherein the frequency of occurrence value is determined using an n-gram viewer; program instructions to associate the received word with a domain table from the plurality of domain tables based on the frequency of occurrence value corresponding to the received word meeting a threshold value; and program instructions to identify a word-sense of the received word based on the corresponding word-sense from the associated domain table. | 8. A computer program product for identifying word-senses, the computer program product comprising: one or more computer-readable storage media and program instructions stored on the one or more computer-readable storage media, the program instructions comprising: program instructions to identify a frequency of occurrence value of a received word from each of a plurality of domain tables, wherein each of the plurality of domain tables comprises a frequency of occurrence value corresponding to the received word, a word-sense corresponding to the received word, and temporal properties corresponding to the received word, wherein the frequency of occurrence value is determined using an n-gram viewer; program instructions to associate the received word with a domain table from the plurality of domain tables based on the frequency of occurrence value corresponding to the received word meeting a threshold value; and program instructions to identify a word-sense of the received word based on the corresponding word-sense from the associated domain table. 9. The computer program product of claim 8 , wherein each of the plurality of domain tables comprises aggregated statistical information corresponding to the plurality of words, the aggregated statistical information comprises frequency of usage, frequency of co-occurrence with other words, year of usage, or usage within a specific profession or domain, or context of usage. | 0.663082 |
5,559,969 | 41 | 43 | 41. A data processing system according to claim 40 wherein each of the plurality of second sub-packets comprises a word. | 41. A data processing system according to claim 40 wherein each of the plurality of second sub-packets comprises a word. 43. A data processing system according to claim 41 wherein each of the plurality of first data packets comprises a MSB sub-packet and an LSB sub-packet. | 0.5 |
8,510,339 | 4 | 5 | 4. The computer-implemented method of claim 1 , wherein the documents of the group of documents are webpages. | 4. The computer-implemented method of claim 1 , wherein the documents of the group of documents are webpages. 5. The computer-implemented method of claim 4 , wherein each attribute value for a webpage is associated with a URL for the webpage. | 0.5 |
9,946,945 | 7 | 8 | 7. A device comprising: a computer configured to: generate a description of a character symbol from a binarized image; compare a template for the character symbol with the description of the character symbol based on a reference description, wherein the template comprises a grid of cells, a set of local features which may be present in the grid of cells, the reference description specifying which member of the set of local features should be present or absent in the grid of cells, and a threshold of an accepted deviation with the description of the character symbol; assign a penalty value to the description of the character symbol via a cost function when a discrepancy exists based on the comparing; select the template as a match candidate for the character symbol when the penalty value is below the threshold; recognize the character symbol based on the selecting. | 7. A device comprising: a computer configured to: generate a description of a character symbol from a binarized image; compare a template for the character symbol with the description of the character symbol based on a reference description, wherein the template comprises a grid of cells, a set of local features which may be present in the grid of cells, the reference description specifying which member of the set of local features should be present or absent in the grid of cells, and a threshold of an accepted deviation with the description of the character symbol; assign a penalty value to the description of the character symbol via a cost function when a discrepancy exists based on the comparing; select the template as a match candidate for the character symbol when the penalty value is below the threshold; recognize the character symbol based on the selecting. 8. The device of claim 7 , wherein a ratio of a number of cells by height and width in the grid of cells substantially corresponds to a ratio of a recognizable font of the character symbol. | 0.892736 |
8,732,097 | 1 | 7 | 1. A computer-implemented method, comprising: computing, by a computing device, a first set of activity probabilities based on contextual information for a first set of historical activities and an activity-prediction model for a target activity, wherein an activity probability indicates a likelihood that the target activity has occurred; comparing a first set of probability thresholds with the first set of activity probabilities to determine a prediction success rate for each probability threshold; computing a utility score for each probability threshold based on the prediction success rates and a utility function, wherein the utility function has the form:
U ( P th )= U 1 (TP)+ U 2 (FP)+ U 3 (FN)+ U 4 (TN); wherein P th is the threshold value used to determine whether a historical activity matches the target activity, wherein U 1 (TP) and U 4 (TN) compute a benefit of making a recommendation based on predicting a true positive and a true negative, respectively, and wherein U 2 (FP) and U 3 (FN) compute a cost of making a recommendation based on predicting a false positive and a false negative, respectively; selecting a first probability threshold whose utility score is greater than or equal to a baseline utility score and other utility scores of the first set of thresholds; and assigning the first probability threshold to the activity-prediction model. | 1. A computer-implemented method, comprising: computing, by a computing device, a first set of activity probabilities based on contextual information for a first set of historical activities and an activity-prediction model for a target activity, wherein an activity probability indicates a likelihood that the target activity has occurred; comparing a first set of probability thresholds with the first set of activity probabilities to determine a prediction success rate for each probability threshold; computing a utility score for each probability threshold based on the prediction success rates and a utility function, wherein the utility function has the form:
U ( P th )= U 1 (TP)+ U 2 (FP)+ U 3 (FN)+ U 4 (TN); wherein P th is the threshold value used to determine whether a historical activity matches the target activity, wherein U 1 (TP) and U 4 (TN) compute a benefit of making a recommendation based on predicting a true positive and a true negative, respectively, and wherein U 2 (FP) and U 3 (FN) compute a cost of making a recommendation based on predicting a false positive and a false negative, respectively; selecting a first probability threshold whose utility score is greater than or equal to a baseline utility score and other utility scores of the first set of thresholds; and assigning the first probability threshold to the activity-prediction model. 7. The method of claim 1 , wherein the contextual information includes one or more of: a geographic location; a motion trajectory; a date range; a logical name associated with a geographic location; a logical name associated with an activity description; a list of participants of the historical activity; and a set of keywords associated with the historical activity. | 0.759477 |
8,874,594 | 1 | 4 | 1. A method comprising: obtaining a set of location information data entries stored at a computer-readable memory location and affiliated with a user account, wherein each location information data entry includes data indicative of a location acquired by a client device associated with the user account as a result of the client device being present at the location; determining a set of geographic locations, wherein each geographic location in the set of geographic locations corresponds to at least one location information data entry in the set of location information data entries; providing a user interface that includes a prompt that accepts search terms from the client device associated with the user account as input to a search query and an interface component for including an indication of at least one geographic location as input to the search query; transmitting the search query to a search engine; and providing, for display, results provided by the search engine in response to the search query; wherein the interface component for including an indication of at least one geographic location as input to the search query comprises an option to apply a filter to the set of geographic locations and include the geographic locations that pass the filter as input to the search query, and wherein the option to apply the filter to the set of geographic locations includes one or more of: an option to select a subset of the set of geographic locations, wherein the subset is determined through data associated with the set of location information data entries, and wherein the subset of the set of geographic locations includes the geographic locations at which the client device was present during a particular period of time, and an option to select the entire set of geographic locations. | 1. A method comprising: obtaining a set of location information data entries stored at a computer-readable memory location and affiliated with a user account, wherein each location information data entry includes data indicative of a location acquired by a client device associated with the user account as a result of the client device being present at the location; determining a set of geographic locations, wherein each geographic location in the set of geographic locations corresponds to at least one location information data entry in the set of location information data entries; providing a user interface that includes a prompt that accepts search terms from the client device associated with the user account as input to a search query and an interface component for including an indication of at least one geographic location as input to the search query; transmitting the search query to a search engine; and providing, for display, results provided by the search engine in response to the search query; wherein the interface component for including an indication of at least one geographic location as input to the search query comprises an option to apply a filter to the set of geographic locations and include the geographic locations that pass the filter as input to the search query, and wherein the option to apply the filter to the set of geographic locations includes one or more of: an option to select a subset of the set of geographic locations, wherein the subset is determined through data associated with the set of location information data entries, and wherein the subset of the set of geographic locations includes the geographic locations at which the client device was present during a particular period of time, and an option to select the entire set of geographic locations. 4. The method of claim 1 , wherein the determining a set of geographic locations wherein each geographic location in the set of geographic locations corresponds to at least one location information data entry in the set of location information data entries comprises: identifying a set of location information data entries; identifying, for each entry in the set of location information data entries, one or more geographic locations corresponding to the location information data entry; and generating the set of geographic locations wherein each geographic location in the set corresponds to at least one entry in the set of location information data entries. | 0.5 |
4,860,213 | 26 | 27 | 26. The reasoning with uncertainty system of claim 21 wherein said value certainty, premise certainty, conclusion detachment and conclusion aggregation intervals are defined on an interval [0,1.0]; said premise certainty interval being computed in accordance with the equation: EQU [b,B]=[T.sub.i (a.sub.1,a.sub.2, . . . ,a.sub.m),T.sub.i (A.sub.1,A.sub.2, . . . ,A.sub.m)] where: [b,B] is said premise certainty interval; [a.sub.1,A.sub.1 ], [a.sub.2,A.sub.2 ], . . . , [a.sub.m,A.sub.m ] are m said value certainty intervals associated with the values assigned to the premise variables; and T.sub.i is a triangular norm (T-norm) function selected from a first function set comprising the predetermined T-norm functions T.sub.1, T.sub.2 and T.sub.3 ; said conclusion detachment interval being computed in accordance with the equation: EQU [c,C]=[T.sub.i (s,b),N(T.sub.i (n,N(B)))] where: [c,C] is said conclusion detachment interval; T.sub.i is the T-norm function selected from said first function set; s is said rule sufficiency factor; n is said rule necessity factor; [b,B] is said premise certainty interval; and N is a negation operator such that N(.alpha.)=1-.alpha.; and said conclusion aggregation interval being computed in accordance with the equation: EQU [d,D]=[S.sub.i (c.sub.1,c.sub.2, . . . ,c.sub.m),S.sub.i (C.sub.1,C.sub.2, . . . ,C.sub.m)] where: [d,D] is said conclusion aggregation interval; [c.sub.1,C.sub.1 ], [c.sub.2,C.sub.2 ], . . . , [c.sub.m,C.sub.m ] are m said conclusion detachment intervals respectively associated with the same conclusion being aggregated; and S.sub.i is a triangular conorm (T-conorm) function selected from a second function set comprising the predetermined T-conorm functions S.sub.2, S.sub.2.5 and S.sub.3. | 26. The reasoning with uncertainty system of claim 21 wherein said value certainty, premise certainty, conclusion detachment and conclusion aggregation intervals are defined on an interval [0,1.0]; said premise certainty interval being computed in accordance with the equation: EQU [b,B]=[T.sub.i (a.sub.1,a.sub.2, . . . ,a.sub.m),T.sub.i (A.sub.1,A.sub.2, . . . ,A.sub.m)] where: [b,B] is said premise certainty interval; [a.sub.1,A.sub.1 ], [a.sub.2,A.sub.2 ], . . . , [a.sub.m,A.sub.m ] are m said value certainty intervals associated with the values assigned to the premise variables; and T.sub.i is a triangular norm (T-norm) function selected from a first function set comprising the predetermined T-norm functions T.sub.1, T.sub.2 and T.sub.3 ; said conclusion detachment interval being computed in accordance with the equation: EQU [c,C]=[T.sub.i (s,b),N(T.sub.i (n,N(B)))] where: [c,C] is said conclusion detachment interval; T.sub.i is the T-norm function selected from said first function set; s is said rule sufficiency factor; n is said rule necessity factor; [b,B] is said premise certainty interval; and N is a negation operator such that N(.alpha.)=1-.alpha.; and said conclusion aggregation interval being computed in accordance with the equation: EQU [d,D]=[S.sub.i (c.sub.1,c.sub.2, . . . ,c.sub.m),S.sub.i (C.sub.1,C.sub.2, . . . ,C.sub.m)] where: [d,D] is said conclusion aggregation interval; [c.sub.1,C.sub.1 ], [c.sub.2,C.sub.2 ], . . . , [c.sub.m,C.sub.m ] are m said conclusion detachment intervals respectively associated with the same conclusion being aggregated; and S.sub.i is a triangular conorm (T-conorm) function selected from a second function set comprising the predetermined T-conorm functions S.sub.2, S.sub.2.5 and S.sub.3. 27. The reasoning with uncertainty system of claim 26 wherein each said T-norm function T.sub.i is selected from said first function set in accordance with an attitude toward uncertainty evaluation, such that: the T-norm function T.sub.1 corresponds to a conservative attitude toward uncertainty evaluation; the T-norm function T.sub.2 corresponds to an intermediate attitude toward uncertainty evaluation; and the T-norm function T.sub.3 corresponds to a nonconservative attitude toward uncertainty evaluation; each said T-conorm function S.sub.i being selected in accordance with whether the rules providing the same conclusion being aggregated are characterized as being correlated, such that: said T-conorm function S.sub.2 is selected for aggregating the same conclusions provided by uncorrelated rules; said T-conorm function S.sub.3 is selected for aggregating the same conclusions provided by correlated rules; and said T-conorm function S.sub.2.5 is selected for aggregating the same conclusions being provided by rules characterized as being intermediate between correlated and uncorrelated. | 0.5 |
9,031,894 | 1 | 3 | 1. A method for generating a tuple of structured data files comprising: detecting an expression that describes a structure of a structured image; using an inference-rule based search strategy to identify a hierarchical arrangement of bounding boxes in the structured image that match the expression; and generating a first tuple of structured data files based on the identified hierarchical arrangement of bounding boxes in the structured image. | 1. A method for generating a tuple of structured data files comprising: detecting an expression that describes a structure of a structured image; using an inference-rule based search strategy to identify a hierarchical arrangement of bounding boxes in the structured image that match the expression; and generating a first tuple of structured data files based on the identified hierarchical arrangement of bounding boxes in the structured image. 3. The method of claim 1 , wherein the inference-rule based search strategy comprises identifying the bounding boxes by detecting a contour within the structured image using image processing techniques. | 0.5 |
8,078,463 | 29 | 37 | 29. A computerized apparatus for spotting an at least one call interaction out of a multiplicity of call interactions in which a target speaker participates, the apparatus comprising: a training computerized component configured for generating a multiplicity of speaker models based on a multiplicity of speaker speech samples from the at least one call interaction; and a speaker spotting computerized component configured for matching the target speaker speech sample with speaker models of the multiplicity of speaker models to determine a target speaker model, determining a score for each call interaction of the multiplicity of call interactions according to a comparison between the target speaker model and the multiplicity of speaker models, and based on scores that are higher than a predetermined threshold, determining call interactions, of the multiplicity of call interactions, and in which the at least one target speaker participates. | 29. A computerized apparatus for spotting an at least one call interaction out of a multiplicity of call interactions in which a target speaker participates, the apparatus comprising: a training computerized component configured for generating a multiplicity of speaker models based on a multiplicity of speaker speech samples from the at least one call interaction; and a speaker spotting computerized component configured for matching the target speaker speech sample with speaker models of the multiplicity of speaker models to determine a target speaker model, determining a score for each call interaction of the multiplicity of call interactions according to a comparison between the target speaker model and the multiplicity of speaker models, and based on scores that are higher than a predetermined threshold, determining call interactions, of the multiplicity of call interactions, and in which the at least one target speaker participates. 37. The apparatus of claim 29 further comprising a call logging system for capturing the target speaker speech sample or at least one of the multiplicity of call interactions. | 0.741888 |
4,847,784 | 39 | 40 | 39. The knowledge system as claimed in claim 36, wherein said knowledge base includes rules having factors in premises and concluding values for expressions, and said means for probing includes: means for probing the subject system for a value of an expression pertaining to said condition upon which the operation of the knowledge base interpreter is interrupted. means for probing the subject system for factors supporting the value of said expression pertaining to said condition, and means for probing the subject system for the values of factors supporting the values of said expression pertaining to said condition. | 39. The knowledge system as claimed in claim 36, wherein said knowledge base includes rules having factors in premises and concluding values for expressions, and said means for probing includes: means for probing the subject system for a value of an expression pertaining to said condition upon which the operation of the knowledge base interpreter is interrupted. means for probing the subject system for factors supporting the value of said expression pertaining to said condition, and means for probing the subject system for the values of factors supporting the values of said expression pertaining to said condition. 40. The knowledge system as claimed in claim 39, wherein said subset of conditions is indicated by a selected subset of said expressions, and said conditions occur when values are found for the expressions included in said subset of expressions. | 0.5 |
8,477,109 | 19 | 22 | 19. An electronic device, comprising: one or more processors; memory coupled to the one or more processors; a touch-sensitive display communicatively coupled to the one or more processors to render a content item and to detect user inputs of varying force; a content item stored in the memory; and a reference entry selection module, stored in the memory and executable on the one or more processors to: receive an indication that the touch-sensitive display has detected a user input having a particular force; select one of multiple reference work entries to output on the touch-sensitive display based at least in part on the particular force of the user input; receive an indication that the touch-sensitive display has detected another user input having a greater amount of force than the particular force; and select a different one of the multiple reference work entries to output on the touch-sensitive display based at least in part on the greater amount of force of the another user input. | 19. An electronic device, comprising: one or more processors; memory coupled to the one or more processors; a touch-sensitive display communicatively coupled to the one or more processors to render a content item and to detect user inputs of varying force; a content item stored in the memory; and a reference entry selection module, stored in the memory and executable on the one or more processors to: receive an indication that the touch-sensitive display has detected a user input having a particular force; select one of multiple reference work entries to output on the touch-sensitive display based at least in part on the particular force of the user input; receive an indication that the touch-sensitive display has detected another user input having a greater amount of force than the particular force; and select a different one of the multiple reference work entries to output on the touch-sensitive display based at least in part on the greater amount of force of the another user input. 22. An electronic device as recited in claim 19 , wherein the selected reference work entry is from a dictionary, a thesaurus, an almanac, an atlas, an encyclopedia, or a gazetteer. | 0.705212 |
8,812,435 | 1 | 13 | 1. A method for learning objects and facts from documents, comprising: on a computer system having one or more processors and memory storing one or more programs for execution by the one or more processors, without user intervention: selecting a source object from a plurality of objects stored in a fact repository, the source object having an object name and an attribute-value pair; selecting a source document from a plurality of documents based on a determination that a title of the source document includes the object name of the source object; and content of the source document includes an attribute and a value related to the attribute-value pair of the source object; identifying a title pattern for the title of the source document based on a first syntax of (i) the title of the source document, and (ii) the object name of the source object within the title of the source document; identifying a contextual pattern for the content of the source document based on a second syntax of (i) the attribute and (ii) the value related to the attribute-value pair of the source object, the contextual pattern being a structural pattern in which one or more attribute-value pairs, including the attribute-value pair of the source object, are presented; selecting a second document from the plurality of documents based on a determination that (i) the contextual pattern is found in the second document and (ii) a title of the second document matches the title pattern; identifying a new object name and a new attribute-value pair from the second document by applying the title pattern and the contextual pattern identified from the source document to the second document; and storing into the fact repository the new attribute-value pair and a new object having the new object name. | 1. A method for learning objects and facts from documents, comprising: on a computer system having one or more processors and memory storing one or more programs for execution by the one or more processors, without user intervention: selecting a source object from a plurality of objects stored in a fact repository, the source object having an object name and an attribute-value pair; selecting a source document from a plurality of documents based on a determination that a title of the source document includes the object name of the source object; and content of the source document includes an attribute and a value related to the attribute-value pair of the source object; identifying a title pattern for the title of the source document based on a first syntax of (i) the title of the source document, and (ii) the object name of the source object within the title of the source document; identifying a contextual pattern for the content of the source document based on a second syntax of (i) the attribute and (ii) the value related to the attribute-value pair of the source object, the contextual pattern being a structural pattern in which one or more attribute-value pairs, including the attribute-value pair of the source object, are presented; selecting a second document from the plurality of documents based on a determination that (i) the contextual pattern is found in the second document and (ii) a title of the second document matches the title pattern; identifying a new object name and a new attribute-value pair from the second document by applying the title pattern and the contextual pattern identified from the source document to the second document; and storing into the fact repository the new attribute-value pair and a new object having the new object name. 13. The method of claim 1 , wherein title of the source document includes (i) a prefix section, (ii) the object name of the source object, and (iii) a suffix section; and identifying the title pattern for the title of the source document based on structural arrangement the object name of the source object includes: identifying the title pattern for the title of the source document based on structural arrangement of (i) the prefix section, (ii) the object name of the source object, and (iii) the suffix section within the title of the source document. | 0.5 |
8,679,015 | 14 | 15 | 14. The interactive television system according to claim 1 , wherein the processing unit is directed by embedded software instructions, software instructions included in a broadcast program or a combination of embedded and broadcast instructions. | 14. The interactive television system according to claim 1 , wherein the processing unit is directed by embedded software instructions, software instructions included in a broadcast program or a combination of embedded and broadcast instructions. 15. The interactive television system according to claim 14 , wherein the embedded software instructions comprise signal processing software. | 0.5 |
8,818,984 | 1 | 2 | 1. A method for using user provided tags for searching, comprising: collecting, by a processor, a plurality of user provided tags associated with a plurality of entities, wherein the plurality of user provided tags comprises semantic descriptions; creating, by the processor, a tag topological network layer that is managed by a service provider, wherein the tag topological network layer predefines a next entity for each one of the plurality of entities based upon the plurality of user provided tags; receiving, by the processor, a user query that contains a search term; and generating, by the processor, a search result containing an entity of the plurality of entities in the tag topological network layer, wherein the entity is found based on a distance measure of a tag vector, tp, for the entity, p, wherein a function tp(i) represents a measure of a weight of a tag, i, that is used to tag the entity, p, based on a normalized count of times tag, i, is used to tag the entity, p, wherein the entity contains a link to another entity in accordance with the tag topological network layer, wherein the link is created in accordance with the tag vector of the entity. | 1. A method for using user provided tags for searching, comprising: collecting, by a processor, a plurality of user provided tags associated with a plurality of entities, wherein the plurality of user provided tags comprises semantic descriptions; creating, by the processor, a tag topological network layer that is managed by a service provider, wherein the tag topological network layer predefines a next entity for each one of the plurality of entities based upon the plurality of user provided tags; receiving, by the processor, a user query that contains a search term; and generating, by the processor, a search result containing an entity of the plurality of entities in the tag topological network layer, wherein the entity is found based on a distance measure of a tag vector, tp, for the entity, p, wherein a function tp(i) represents a measure of a weight of a tag, i, that is used to tag the entity, p, based on a normalized count of times tag, i, is used to tag the entity, p, wherein the entity contains a link to another entity in accordance with the tag topological network layer, wherein the link is created in accordance with the tag vector of the entity. 2. The method of claim 1 , wherein the generating the search result comprises: converting the search term into a tag vector, wherein the entity is found based on a distance measure of the tag vector of the entity to the tag vector of the search term. | 0.5 |
7,840,512 | 20 | 21 | 20. The method of claim 17 wherein training comprises assigning a variable in the graphical model to each associated term. | 20. The method of claim 17 wherein training comprises assigning a variable in the graphical model to each associated term. 21. The method of claim 20 wherein training comprises constructing the training data as a plurality of vectors, each vector having a location for each of the variables, labeling a disease of interest corresponding to the associated terms, and training the model from the training data where at least one variable of at least one vector has a missing value. | 0.5 |
8,086,959 | 4 | 5 | 4. The method of claim 3 , further comprising determining a data type for the data associated with each discovered node. | 4. The method of claim 3 , further comprising determining a data type for the data associated with each discovered node. 5. The method of claim 4 , wherein determining a data type for the data associated with each discovered node comprises: comparing the data associated with the discovered node to a list of known data types; and determining the data type based on the list of known data types. | 0.5 |
9,715,557 | 1 | 12 | 1. An electronic device comprising: storage configured to maintain a primary web-browser application and a secondary web-browser application; at least one processor connected to said storage and configured to execute said primary web-browser application; an interface connected to said processor, said processor configured to receive a web-page stored at a web-server via said interface, said web-page including context sensitive content related to a plurality of context sensitive items on said web-page, said context sensitive content being able to change without further input from the web-server, said context sensitive content comprising a data portion and a non-data portion, said non-data portion comprising scripts executable by said primary web-browser application and said data portion comprising tags, labels, or text; a display connected to said processor; said processor further configured to render said web-page on said display; an input device connected to said processor, said processor configured to receive focus on one of said plurality of context sensitive items via said input device placing a pointer over the one context sensitive item; said processor further configured to respond to receiving the focus by rendering only the tags, labels, or text constituting the data portion of the context sensitive content related to said one of the plurality of context sensitive items on said display via the secondary web-browser application; and said processor further configured to not execute said scripts associated with said context sensitive content using said secondary web-browser application. | 1. An electronic device comprising: storage configured to maintain a primary web-browser application and a secondary web-browser application; at least one processor connected to said storage and configured to execute said primary web-browser application; an interface connected to said processor, said processor configured to receive a web-page stored at a web-server via said interface, said web-page including context sensitive content related to a plurality of context sensitive items on said web-page, said context sensitive content being able to change without further input from the web-server, said context sensitive content comprising a data portion and a non-data portion, said non-data portion comprising scripts executable by said primary web-browser application and said data portion comprising tags, labels, or text; a display connected to said processor; said processor further configured to render said web-page on said display; an input device connected to said processor, said processor configured to receive focus on one of said plurality of context sensitive items via said input device placing a pointer over the one context sensitive item; said processor further configured to respond to receiving the focus by rendering only the tags, labels, or text constituting the data portion of the context sensitive content related to said one of the plurality of context sensitive items on said display via the secondary web-browser application; and said processor further configured to not execute said scripts associated with said context sensitive content using said secondary web-browser application. 12. The electronic device of claim 1 wherein said secondary web-browser application is optimized to different input devices of said portable computing device. | 0.674897 |
7,519,589 | 39 | 43 | 39. A method to enable improved analysis and use of sociological data, the method comprising: identifying causal relationships between a plurality of documents; identifying a plurality of characteristics of a communication, including a modality used, actors involved, and proximate events of relevance; enabling a user to query based on all of the characteristics available; and overlaying an organization chart over a communications graph displaying communication between actors, to enable a user to see unusual communications patterns. | 39. A method to enable improved analysis and use of sociological data, the method comprising: identifying causal relationships between a plurality of documents; identifying a plurality of characteristics of a communication, including a modality used, actors involved, and proximate events of relevance; enabling a user to query based on all of the characteristics available; and overlaying an organization chart over a communications graph displaying communication between actors, to enable a user to see unusual communications patterns. 43. The method of claim 39 , further comprising: enabling the overlaying for searches and graphical queries, to enable the identification of anomalies by topic, relevant event, time period. | 0.585526 |
8,976,944 | 8 | 13 | 8. At least one non-transitory computer readable storage device for storing instructions that, when executed on at least one computer, cause the at least one computer to perform a method for converting an audio voice message from a caller to a recipient to text, the method comprising: processing the audio voice message to identify at least one portion of the audio voice message which contains content of a type for which one of a plurality of ASR components is specially configured to automatically recognize; sending the identified at least one portion of the audio voice message to the one of the plurality of ASR components specially configured for the type of content identified in the at least one portion of the audio voice message to be automatically recognized; receiving, from the ASR component to which the at least one portion of the audio voice message was sent, a text portion corresponding to the automatic recognition of the at least one portion of the audio; assembling the text portion into the text;and outputting the text to the recipient. | 8. At least one non-transitory computer readable storage device for storing instructions that, when executed on at least one computer, cause the at least one computer to perform a method for converting an audio voice message from a caller to a recipient to text, the method comprising: processing the audio voice message to identify at least one portion of the audio voice message which contains content of a type for which one of a plurality of ASR components is specially configured to automatically recognize; sending the identified at least one portion of the audio voice message to the one of the plurality of ASR components specially configured for the type of content identified in the at least one portion of the audio voice message to be automatically recognized; receiving, from the ASR component to which the at least one portion of the audio voice message was sent, a text portion corresponding to the automatic recognition of the at least one portion of the audio; assembling the text portion into the text;and outputting the text to the recipient. 13. The at least one non-transitory computer readable storage device of claim 8 , comprising identifying a portion of the audio message that contains a telephone number, and sending the portion to an ASR component specially configured to automatically recognize telephone numbers. | 0.5 |
8,387,029 | 19 | 20 | 19. The method as recited in claim 14 , wherein directly executing the nonlinear program element comprises: inserting the executed body of the loop construct at the head of the input token stream; and directly executing the body of the loop construct until the loop execution ends. | 19. The method as recited in claim 14 , wherein directly executing the nonlinear program element comprises: inserting the executed body of the loop construct at the head of the input token stream; and directly executing the body of the loop construct until the loop execution ends. 20. The method as recited in claim 19 , further comprising forming an iterator comprising tokens representing the body of the loop construct and adding the iterator to a token stack. | 0.5 |
9,570,066 | 1 | 10 | 1. A method of speech synthesis, comprising the steps of: (a) receiving speech input from a sender; (b) obtaining at least one distinguishing characteristic of the sender from the speech input, wherein the at least one distinguishing characteristic includes conversational information or textual information of the speech input; (c) obtaining baseline characteristics, wherein the baseline characteristics include articulation rate, courteousness, formants, or pitch frequency that a recipient user of the system is accustomed to hearing; (d) selecting a default text-to-speech model based on the at least one distinguishing characteristic of the sender; (e) modifying the selected default text-to-speech model using the received speech input; (f) receiving, at a text-to-speech system, a text input sent by the sender; (g) processing, via a processor of the system and the text-to-speech model, the text input responsive to the at least one distinguishing characteristic of the sender to produce synthesized speech that is representative of a voice of the sender; (h) identifying baseline characteristics of the synthesized speech; (i) applying an acoustic feature filter to the synthesized speech, wherein the acoustic feature filter is adjusted using the baseline characteristics obtained from the received speech; and (j) communicating the synthesized speech to the recipient user of the system. | 1. A method of speech synthesis, comprising the steps of: (a) receiving speech input from a sender; (b) obtaining at least one distinguishing characteristic of the sender from the speech input, wherein the at least one distinguishing characteristic includes conversational information or textual information of the speech input; (c) obtaining baseline characteristics, wherein the baseline characteristics include articulation rate, courteousness, formants, or pitch frequency that a recipient user of the system is accustomed to hearing; (d) selecting a default text-to-speech model based on the at least one distinguishing characteristic of the sender; (e) modifying the selected default text-to-speech model using the received speech input; (f) receiving, at a text-to-speech system, a text input sent by the sender; (g) processing, via a processor of the system and the text-to-speech model, the text input responsive to the at least one distinguishing characteristic of the sender to produce synthesized speech that is representative of a voice of the sender; (h) identifying baseline characteristics of the synthesized speech; (i) applying an acoustic feature filter to the synthesized speech, wherein the acoustic feature filter is adjusted using the baseline characteristics obtained from the received speech; and (j) communicating the synthesized speech to the recipient user of the system. 10. The method of claim 1 wherein the at least one distinguishing characteristic includes at least one individual attribute that is personal to the sender that created the text input. | 0.674377 |
8,103,676 | 19 | 21 | 19. A computer program product tangibly stored on a storage device, the product operable to cause a computing device to perform operations comprising: receiving a result set, the result set including a plurality of individual search results of a query, each of the individual search results including a first search result part and a second search result part that is different from the first search result part, wherein: the first search result part is exactly one of a uniform resource locator (URL), a hostname from the URL, a title, a snippet, a label, or a label histogram, and the second search result part is exactly one of a URL, a hostname from the URL, a title, a snippet, a label, or a label histogram; determining an interpretation of the query from the individual search results, the interpretation being represented as a result classification of the result set as a whole, the result classification including a result category and a result score for the result set, wherein determining the result classification comprises: for each of the plurality of individual search results: determining, based on the first search result part: a first search result part category for determining the interpretation of the query; and a corresponding first search result part score; determining, based on the second search result part: a second search result part category for determining the interpretation of the query; and a corresponding second search result part score; and determining an individual result category and a corresponding individual result score based on the first search result part category, the second search result part category, and a weighted combination of the first search result part score and second search result part score; and determining the result category and the result score for the result set based on the individual result categories and corresponding individual result scores; identifying, based on the interpretation of the query as represented by the result classification, a page element that corresponds to the interpretation of the query; and providing for display a page that includes the page element in addition to at least a portion of the result set. | 19. A computer program product tangibly stored on a storage device, the product operable to cause a computing device to perform operations comprising: receiving a result set, the result set including a plurality of individual search results of a query, each of the individual search results including a first search result part and a second search result part that is different from the first search result part, wherein: the first search result part is exactly one of a uniform resource locator (URL), a hostname from the URL, a title, a snippet, a label, or a label histogram, and the second search result part is exactly one of a URL, a hostname from the URL, a title, a snippet, a label, or a label histogram; determining an interpretation of the query from the individual search results, the interpretation being represented as a result classification of the result set as a whole, the result classification including a result category and a result score for the result set, wherein determining the result classification comprises: for each of the plurality of individual search results: determining, based on the first search result part: a first search result part category for determining the interpretation of the query; and a corresponding first search result part score; determining, based on the second search result part: a second search result part category for determining the interpretation of the query; and a corresponding second search result part score; and determining an individual result category and a corresponding individual result score based on the first search result part category, the second search result part category, and a weighted combination of the first search result part score and second search result part score; and determining the result category and the result score for the result set based on the individual result categories and corresponding individual result scores; identifying, based on the interpretation of the query as represented by the result classification, a page element that corresponds to the interpretation of the query; and providing for display a page that includes the page element in addition to at least a portion of the result set. 21. The product of claim 19 , wherein: the first search result part is a URL; the second search result part is a hostname from the URL; determining the first search result part category and the corresponding first search result part score includes determining a full URL category and a corresponding full URL weight based on the URL; determining the second search result part category and the corresponding second search result part score includes determining a hostname category and a corresponding hostname weight based on the hostname from the URL; and determining the individual result category and the corresponding individual result score includes determining the individual result category and the corresponding individual result score based on the full URL category, the hostname category, and a combination of the corresponding URL weight and the corresponding hostname weight. | 0.5 |
9,349,099 | 1 | 3 | 1. A system comprising: a recommendation module, comprising one or more hardware processors, configured to: access social graph information identifying one or more users connected to a particular user on a social graph; identify preferences of the one or more users; and determine preferences of the particular user, based on the identified preferences of the one or more users. | 1. A system comprising: a recommendation module, comprising one or more hardware processors, configured to: access social graph information identifying one or more users connected to a particular user on a social graph; identify preferences of the one or more users; and determine preferences of the particular user, based on the identified preferences of the one or more users. 3. The system of claim 1 , wherein at least one of the one or more users is at least a predetermined number of degrees away from the particular user in the social graph. | 0.705575 |
9,298,817 | 4 | 6 | 4. A computer program product, comprising: a computer-readable, tangible storage device; and a computer-readable program code stored in the computer-readable, tangible storage device, the computer-readable program code containing instructions that are executed by a central processing unit (CPU) of a computer system to implement a method of building an ontology, the method comprising the steps of: the computer system extracting a plurality of complex triples from free-form text provided by a software application, each complex triple including a compound subject, a compound predicate and a compound object; the computer system performing a syntactic transformation of the plurality of complex triples by, based on a grammar, identifying core terms and non-core terms in the plurality of complex triples, identifying syntactic elements in the plurality of complex triples including nouns, verbs, adjectives and adverbs, and standardizing the plurality of complex triples, wherein a result of the step of performing the syntactic transformation is a plurality of syntactically transformed complex triples whose terms are aligned to the grammar; the computer system performing a semantic transformation of the plurality of syntactically transformed complex triples into respective one or more simplified triples included in a plurality of simplified triples by assigning each core term included in the plurality of simplified triples to exactly one term definition and to exactly one identification key of a reference ontology, wherein each simplified triple includes a subject term, a predicate term and an object term, and wherein each of the one or more simplified triples retains the semantics of the respective syntactically transformed complex triple; based in part on a meta-schema of the reference ontology, the computer system performing an enrichment transformation of the plurality of simplified triples into a plurality of simplified and enriched triples by adding relations derived from a correspondence each term in the plurality of simplified triples has with the reference ontology and by adding representations of semantics of definitions of terms in the plurality of simplified triples, wherein the definitions are included in the reference ontology; based on the plurality of simplified and enriched triples, the computer system generating a new ontology that is aligned with the reference ontology and that represents knowledge included within the software application that provides the free-form text, the new ontology addressing a first domain of expertise; the computer system aligning another ontology with the reference ontology, the other ontology addressing a second domain of expertise that is different from the first domain of expertise; and based on the new ontology and the other ontology being aligned with the reference ontology, the computer system merging the new ontology and the other ontology even though the first and second domains of expertise are different, wherein the step of performing the syntactic transformation of the plurality of complex triples includes: determining a term of a complex triple included in the plurality of complex triples is a core term and is an adjective; and determining the adjective is linked to a core noun included in the complex triple, wherein the step of performing the semantic transformation of the plurality of syntactically transformed complex triples into the respective one or more simplified triples included in the plurality of simplified triples is based in part on the adjective linked to the core noun included in the complex triple and includes generating a conceptualized adjective from the adjective, wherein the step of generating the conceptualized adjective includes: sending to a user a list of attributes included in a lexical database that are related to the adjective and determining whether the user selects one of the attributes in the list of attributes; if the user selects one of the attributes, designating the selected attribute as the conceptualized adjective; if the user does not select one of the attributes, sending to the user a list of nouns in the lexical database that are derivationally related forms of the adjective and determining whether the user selects one of the nouns in the list of nouns; if the user selects one of the nouns, designating the selected noun as the conceptualized adjective; if the user does not select one of the nouns, sending to the user a list of hypernyms and hyponyms that the lexical database associates with the nouns in the list of nouns, and determining whether the user selects one of the hypernyms or one of the hyponyms; if the user selects one of the hypernyms or one of the hyponyms, designating the selected hypernym or hyponym as the conceptualized adjective; and if the user does not select one of the hypernyms or hyponyms, creating the conceptualized adjective by adding a suffix_ness to an end of the adjective, and wherein the step of performing the enrichment transformation of the plurality of simplified triples is based in part on the conceptualized adjective and includes the step of generating a new set of complex triples that represents the semantics of the definitions of core terms in the plurality of simplified triples, and wherein the method further comprises the steps of: the computer system receiving a desired analysis depth and initializing an analysis depth parameter; based on the grammar, the computer system syntactically transforming the new set of complex triples into a new syntactically transformed set of complex triples; the computer system semantically transforming the new syntactically transformed set of complex triples into a new set of simplified triples; the computer system updating the analysis depth parameter subsequent to the steps of syntactically transforming and semantically transforming; and while the updated analysis depth parameter does not indicate the desired analysis depth, the computer system: performing an enrichment transformation on the new set of simplified triples to generate another new set of complex triples; and repeating, for the other new set of simplified triples, the steps of syntactically transforming, semantically transforming, and updating the analysis depth parameter. | 4. A computer program product, comprising: a computer-readable, tangible storage device; and a computer-readable program code stored in the computer-readable, tangible storage device, the computer-readable program code containing instructions that are executed by a central processing unit (CPU) of a computer system to implement a method of building an ontology, the method comprising the steps of: the computer system extracting a plurality of complex triples from free-form text provided by a software application, each complex triple including a compound subject, a compound predicate and a compound object; the computer system performing a syntactic transformation of the plurality of complex triples by, based on a grammar, identifying core terms and non-core terms in the plurality of complex triples, identifying syntactic elements in the plurality of complex triples including nouns, verbs, adjectives and adverbs, and standardizing the plurality of complex triples, wherein a result of the step of performing the syntactic transformation is a plurality of syntactically transformed complex triples whose terms are aligned to the grammar; the computer system performing a semantic transformation of the plurality of syntactically transformed complex triples into respective one or more simplified triples included in a plurality of simplified triples by assigning each core term included in the plurality of simplified triples to exactly one term definition and to exactly one identification key of a reference ontology, wherein each simplified triple includes a subject term, a predicate term and an object term, and wherein each of the one or more simplified triples retains the semantics of the respective syntactically transformed complex triple; based in part on a meta-schema of the reference ontology, the computer system performing an enrichment transformation of the plurality of simplified triples into a plurality of simplified and enriched triples by adding relations derived from a correspondence each term in the plurality of simplified triples has with the reference ontology and by adding representations of semantics of definitions of terms in the plurality of simplified triples, wherein the definitions are included in the reference ontology; based on the plurality of simplified and enriched triples, the computer system generating a new ontology that is aligned with the reference ontology and that represents knowledge included within the software application that provides the free-form text, the new ontology addressing a first domain of expertise; the computer system aligning another ontology with the reference ontology, the other ontology addressing a second domain of expertise that is different from the first domain of expertise; and based on the new ontology and the other ontology being aligned with the reference ontology, the computer system merging the new ontology and the other ontology even though the first and second domains of expertise are different, wherein the step of performing the syntactic transformation of the plurality of complex triples includes: determining a term of a complex triple included in the plurality of complex triples is a core term and is an adjective; and determining the adjective is linked to a core noun included in the complex triple, wherein the step of performing the semantic transformation of the plurality of syntactically transformed complex triples into the respective one or more simplified triples included in the plurality of simplified triples is based in part on the adjective linked to the core noun included in the complex triple and includes generating a conceptualized adjective from the adjective, wherein the step of generating the conceptualized adjective includes: sending to a user a list of attributes included in a lexical database that are related to the adjective and determining whether the user selects one of the attributes in the list of attributes; if the user selects one of the attributes, designating the selected attribute as the conceptualized adjective; if the user does not select one of the attributes, sending to the user a list of nouns in the lexical database that are derivationally related forms of the adjective and determining whether the user selects one of the nouns in the list of nouns; if the user selects one of the nouns, designating the selected noun as the conceptualized adjective; if the user does not select one of the nouns, sending to the user a list of hypernyms and hyponyms that the lexical database associates with the nouns in the list of nouns, and determining whether the user selects one of the hypernyms or one of the hyponyms; if the user selects one of the hypernyms or one of the hyponyms, designating the selected hypernym or hyponym as the conceptualized adjective; and if the user does not select one of the hypernyms or hyponyms, creating the conceptualized adjective by adding a suffix_ness to an end of the adjective, and wherein the step of performing the enrichment transformation of the plurality of simplified triples is based in part on the conceptualized adjective and includes the step of generating a new set of complex triples that represents the semantics of the definitions of core terms in the plurality of simplified triples, and wherein the method further comprises the steps of: the computer system receiving a desired analysis depth and initializing an analysis depth parameter; based on the grammar, the computer system syntactically transforming the new set of complex triples into a new syntactically transformed set of complex triples; the computer system semantically transforming the new syntactically transformed set of complex triples into a new set of simplified triples; the computer system updating the analysis depth parameter subsequent to the steps of syntactically transforming and semantically transforming; and while the updated analysis depth parameter does not indicate the desired analysis depth, the computer system: performing an enrichment transformation on the new set of simplified triples to generate another new set of complex triples; and repeating, for the other new set of simplified triples, the steps of syntactically transforming, semantically transforming, and updating the analysis depth parameter. 6. The computer program product of claim 4 , wherein the step of the computer system performing the semantic transformation of the plurality of syntactically transformed complex triples into the respective one or more simplified triples included in the plurality of simplified triples includes: determining a noun is needed in a simplified triple of the respective one or more simplified triples; determining the reference ontology and meta-schema of the reference ontology do not include a definition of the noun needed in the simplified triple; generating the noun that is determined to be needed in the simplified triple; generating a definition of the noun as relationships between the noun and terms in the new ontology being built; storing the noun and the definition of the noun in the meta-schema of the reference ontology; and based in part on the stored noun and the stored definition of the noun, building a second new ontology. | 0.780299 |
9,424,254 | 2 | 3 | 2. The method of claim 1 further comprising: a. repeating the removing step until the current set of input is null to create one or more additional natural language sentences; and b. providing the one or more additional natural language sentences. | 2. The method of claim 1 further comprising: a. repeating the removing step until the current set of input is null to create one or more additional natural language sentences; and b. providing the one or more additional natural language sentences. 3. The method of claim 2 wherein each of the first automated natural language sentence, the second automated natural language sentence and the one or more additional natural language sentences were created by: a. identifying, based on the current set of input, a set of statistically generated templates, the set of statistically generated templates associated with the message type; b. applying a ranking model on the set of statistically generated templates; c. selecting, in response to applying the ranking model, a statistically generated template from the set of statistically generated templates; and d. replacing a set of tags within the statistically generated template with one or more input pieces of the current set of input. | 0.5 |
9,288,058 | 1 | 9 | 1. A method, comprising: identifying, by a hardware processor, a first compliance script; determining a value of a cryptographic hash function of at least part of the first compliance script; determining, using the value of the cryptographic hash function, an installation path of a second compliance script; identifying a security context associated with the second compliance script at installation time; and executing, by the hardware processor, the second compliance script within the security context to determine whether a parameter of a computer system is within an allowed range. | 1. A method, comprising: identifying, by a hardware processor, a first compliance script; determining a value of a cryptographic hash function of at least part of the first compliance script; determining, using the value of the cryptographic hash function, an installation path of a second compliance script; identifying a security context associated with the second compliance script at installation time; and executing, by the hardware processor, the second compliance script within the security context to determine whether a parameter of a computer system is within an allowed range. 9. The method of claim 1 , wherein executing the second compliance script comprises, responsive to determining that a compliance rule is violated, executing a defined process. | 0.748563 |
7,548,913 | 21 | 25 | 21. A method, performed on one or more processing devices, for generating a report from multiple sources, the method comprising: (I) obtaining a topic based on input queries relating to the topic; (II) obtaining information about the topic from the multiple sources, the information comprising excerpts from the multiple sources that meet one or more criteria; and (III) generating the report using the excerpts, wherein generating the report comprises: (i) obtaining subtopics for the excerpts; (ii) organizing the excerpts based on the subtopics; and (iii) editing text in the excerpts; (IV) wherein obtaining information about the topic comprises: (i) assigning the topic to one or more categories; (ii) determining whether the topic is ambiguous based on the one or more categories to which the topic is assigned; (iii) wherein, if the topic is determined to be ambiguous, the method comprises: disambiguating the topic by combining names of the one or more categories with the topic, thereby producing a disambiguated topic; and formulating a search query based on the disambiguated topic; (iv) wherein, if the topic is determined not to be ambiguous, the method comprises: formulating the search query based on the topic; (v) searching the multiple sources using the search query; (vi) receiving search results as a result of searching the multiple sources, the search results comprising documents corresponding to the search query; (vii) selecting a subset of the documents from which to extract the excerpts, the subset of the documents being selected based on intrinsic metrics and extrinsic metrics, the intrinsic metrics comprising data from the documents and the extrinsic metrics comprising data relating to the documents but not from the documents; and (viii) extracting the excerpts from the subset the documents, wherein the excerpts are extracted from the subset of documents based on the one or more criteria, the excerpts comprising text and non-text. | 21. A method, performed on one or more processing devices, for generating a report from multiple sources, the method comprising: (I) obtaining a topic based on input queries relating to the topic; (II) obtaining information about the topic from the multiple sources, the information comprising excerpts from the multiple sources that meet one or more criteria; and (III) generating the report using the excerpts, wherein generating the report comprises: (i) obtaining subtopics for the excerpts; (ii) organizing the excerpts based on the subtopics; and (iii) editing text in the excerpts; (IV) wherein obtaining information about the topic comprises: (i) assigning the topic to one or more categories; (ii) determining whether the topic is ambiguous based on the one or more categories to which the topic is assigned; (iii) wherein, if the topic is determined to be ambiguous, the method comprises: disambiguating the topic by combining names of the one or more categories with the topic, thereby producing a disambiguated topic; and formulating a search query based on the disambiguated topic; (iv) wherein, if the topic is determined not to be ambiguous, the method comprises: formulating the search query based on the topic; (v) searching the multiple sources using the search query; (vi) receiving search results as a result of searching the multiple sources, the search results comprising documents corresponding to the search query; (vii) selecting a subset of the documents from which to extract the excerpts, the subset of the documents being selected based on intrinsic metrics and extrinsic metrics, the intrinsic metrics comprising data from the documents and the extrinsic metrics comprising data relating to the documents but not from the documents; and (viii) extracting the excerpts from the subset the documents, wherein the excerpts are extracted from the subset of documents based on the one or more criteria, the excerpts comprising text and non-text. 25. The method of claim 21 , wherein editing text comprises at least one of: deleting words from a sentence based on at least one of a location of the words in the sentence and a location of the sentence in the excerpt; and adding words to a sentence based on a location of the sentence in the excerpt. | 0.802614 |
10,148,961 | 18 | 19 | 18. The apparatus of claim 13 , wherein the one or more processors are further configured to: code a first syntax element that indicates whether a default window size is used for the plurality of contexts. | 18. The apparatus of claim 13 , wherein the one or more processors are further configured to: code a first syntax element that indicates whether a default window size is used for the plurality of contexts. 19. The apparatus of claim 18 , wherein, based on the first syntax element indicating that the default window size is not used for the plurality of contexts, the one or more processors are further configured to: code a second syntax element that indicates the window size for the first context. | 0.589385 |
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