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6,098,033 | 25 | 26 | 25. A computer memory containing a word similarity data structure for determining the similarity of a pair of input words, the word similarity data structure comprising entries, each entry identifying: a semantic relation type path pattern comprising an ordered series of semantic relation types; and a weight for the semantic relation type path pattern characterizing the extent to which a semantic relation paths of the semantic relation type path pattern occurring between an arbitrary pair of words indicates that the words of the arbitrary pair have similar meanings, the word similarity data structure being usable to determine the similarity of the input words by combining the weights for semantic relation type path patterns that occur between the input words. | 25. A computer memory containing a word similarity data structure for determining the similarity of a pair of input words, the word similarity data structure comprising entries, each entry identifying: a semantic relation type path pattern comprising an ordered series of semantic relation types; and a weight for the semantic relation type path pattern characterizing the extent to which a semantic relation paths of the semantic relation type path pattern occurring between an arbitrary pair of words indicates that the words of the arbitrary pair have similar meanings, the word similarity data structure being usable to determine the similarity of the input words by combining the weights for semantic relation type path patterns that occur between the input words. 26. The computer memory of claim 25 wherein the weight indicated by each entry of the word similarity data structure comprises the frequency with which the semantic relation type path pattern of the entry occurs between words known to be synonyms. | 0.897595 |
8,989,713 | 1 | 6 | 1. A method of communication comprising: receiving an audio message from a caller to a recipient; transcribing the audio voice message to produce text; providing a text message including the text and a link comprising information that links to a conversion system capable of converting speech to text and an identifier indicating a destination for a reply message, such that when the recipient selects the link, the recipient is connected to the conversion system to speak the reply message that is automatically transcribed into a reply text message and provided to the destination associated with the identifier; and transmitting the text message to a mobile device of the recipient. | 1. A method of communication comprising: receiving an audio message from a caller to a recipient; transcribing the audio voice message to produce text; providing a text message including the text and a link comprising information that links to a conversion system capable of converting speech to text and an identifier indicating a destination for a reply message, such that when the recipient selects the link, the recipient is connected to the conversion system to speak the reply message that is automatically transcribed into a reply text message and provided to the destination associated with the identifier; and transmitting the text message to a mobile device of the recipient. 6. The method of claim 1 , further comprising: receiving a communication to connect in response to the recipient selecting the link; prompting the recipient to provide speech input subsequent to connecting; receiving speech input from the recipient; transcribing the speech input from the recipient to text; and automatically transmitting a text message including the text to the destination identified by the identifier in the link. | 0.646242 |
7,779,397 | 4 | 5 | 4. The computer-implemented system of claim 3 , further comprising a type conversion module that uses the wrapper module to strongly type the at least one untyped datum. | 4. The computer-implemented system of claim 3 , further comprising a type conversion module that uses the wrapper module to strongly type the at least one untyped datum. 5. The computer-implemented system of claim 4 , wherein the wrapper module provides a default implementation to strongly type the at least one untyped datum. | 0.932734 |
10,027,356 | 13 | 14 | 13. A method of compensating for zero crossing (ZC) distortion, the method comprising: determining ZC distortion in a ZC caused by a bandpass filter (BPF) used to convert an output digital signal into an analog signal at a carrier frequency; compensating for the ZC distortion and producing an output value based thereon; and generating code words to create an output digital to be applied to the BPF after compensating for the ZC distortion, interpolating a first interpolated baseband in-phase quadrature-phase (IQ) signal and producing a second interpolated baseband IQ signal; determining ZCs of the second interpolated baseband IQ signal; providing and determining absolute magnitudes of the second interpolated baseband IQ signal; and adjusting clocking of the ZCs and the absolute magnitudes of the second interpolated baseband IQ signal to change a frequency of the ZCs and the absolute magnitudes of the second interpolated baseband IQ signal prior to compensating for the ZC distortion. | 13. A method of compensating for zero crossing (ZC) distortion, the method comprising: determining ZC distortion in a ZC caused by a bandpass filter (BPF) used to convert an output digital signal into an analog signal at a carrier frequency; compensating for the ZC distortion and producing an output value based thereon; and generating code words to create an output digital to be applied to the BPF after compensating for the ZC distortion, interpolating a first interpolated baseband in-phase quadrature-phase (IQ) signal and producing a second interpolated baseband IQ signal; determining ZCs of the second interpolated baseband IQ signal; providing and determining absolute magnitudes of the second interpolated baseband IQ signal; and adjusting clocking of the ZCs and the absolute magnitudes of the second interpolated baseband IQ signal to change a frequency of the ZCs and the absolute magnitudes of the second interpolated baseband IQ signal prior to compensating for the ZC distortion. 14. The method of claim 13 , wherein: compensation for the ZC distortion is dependent on one of: the output signal immediately prior to and immediately after the ZC, or a baseband signal on which the output signal is based immediately prior to and immediately after the ZC. | 0.83574 |
9,875,096 | 12 | 16 | 12. A system for creating a structured report that aggregates information related to a plurality of source code files based on selective scanning of one or more repositories, the system comprising: a memory: and a processor configured to execute instructions stored in the memory, causing the system to: provide a user interface operable to access a source repository; receive, from a user via the user interface, a repository search request, the search request including a repository identifier associated with the source repository in which source code files are stored for a plurality of software projects, a project identifier associated with a specified software project corresponding to one of the plurality of software projects stored within the source repository, and a search term; scan the specified repository for source code files corresponding to the specified software project in response to the search request to identify source code files for the specified software project that include the search term; determine whether an instance of the search term within the source code files corresponds to a reserved term based on a context or usage of the instance of the search term within the source code, the reserved term being associated with a programming language implemented within the source code files; create, in response to the search request, a new file directory in a non-transitory computer-readable medium; generate a report identifying the source code files that include an instance of the search term, the report including a line number in the source code of the source code files at which the instance of the search term is found, a name of a software module within the source code at which the instance of search term is found, and a segment of the source code that contains the instance of the search term, the segment of code indicating whether the instance of the search term corresponds to the reserved term; populate the new file directory with a first results file including the report and a second results file including a further report, the further report identifying the source code files for specified software project in the specified repository that do not include the search term and; update the user interface, responsive to populating the new file directory with a first results file and the second results file, to display content from the first results file and the second file, wherein the updating comprises displaying the segment of the source code that contains the instance of the search term, the segment of code indicating whether the instance of the search term corresponds to the reserved term. | 12. A system for creating a structured report that aggregates information related to a plurality of source code files based on selective scanning of one or more repositories, the system comprising: a memory: and a processor configured to execute instructions stored in the memory, causing the system to: provide a user interface operable to access a source repository; receive, from a user via the user interface, a repository search request, the search request including a repository identifier associated with the source repository in which source code files are stored for a plurality of software projects, a project identifier associated with a specified software project corresponding to one of the plurality of software projects stored within the source repository, and a search term; scan the specified repository for source code files corresponding to the specified software project in response to the search request to identify source code files for the specified software project that include the search term; determine whether an instance of the search term within the source code files corresponds to a reserved term based on a context or usage of the instance of the search term within the source code, the reserved term being associated with a programming language implemented within the source code files; create, in response to the search request, a new file directory in a non-transitory computer-readable medium; generate a report identifying the source code files that include an instance of the search term, the report including a line number in the source code of the source code files at which the instance of the search term is found, a name of a software module within the source code at which the instance of search term is found, and a segment of the source code that contains the instance of the search term, the segment of code indicating whether the instance of the search term corresponds to the reserved term; populate the new file directory with a first results file including the report and a second results file including a further report, the further report identifying the source code files for specified software project in the specified repository that do not include the search term and; update the user interface, responsive to populating the new file directory with a first results file and the second results file, to display content from the first results file and the second file, wherein the updating comprises displaying the segment of the source code that contains the instance of the search term, the segment of code indicating whether the instance of the search term corresponds to the reserved term. 16. The system of claim 12 , wherein in response to a determination that the instance of the search term corresponds to a reserved term, the report is generated to include a visual indicator in the segment of code modifying the instance of the search term as the reserved term. | 0.743519 |
8,321,203 | 10 | 16 | 10. An apparatus to generate information on a content, the apparatus comprising: a text dividing unit to divide a text extracted from a content into one or more predetermined units; a relationship determining unit to determine one or more dominant relationships between characters of the content by comparing the divided units with relationship keyword information in which keywords contained in the categories are defined, wherein the categories represent one or more relationships between the characters, the relationship determining unit comprising: a match-up portion to match the divided units to the categories with reference to the relationship keyword information; a counting portion to count the number of the divided units corresponding to each of the categories; and a determining portion to determine a relationship represented by the category corresponding to the highest number of the divided units as the dominant relationship between the characters; and an information generation unit to generate information on the relationships between the characters in accordance with the determined dominant relationships. | 10. An apparatus to generate information on a content, the apparatus comprising: a text dividing unit to divide a text extracted from a content into one or more predetermined units; a relationship determining unit to determine one or more dominant relationships between characters of the content by comparing the divided units with relationship keyword information in which keywords contained in the categories are defined, wherein the categories represent one or more relationships between the characters, the relationship determining unit comprising: a match-up portion to match the divided units to the categories with reference to the relationship keyword information; a counting portion to count the number of the divided units corresponding to each of the categories; and a determining portion to determine a relationship represented by the category corresponding to the highest number of the divided units as the dominant relationship between the characters; and an information generation unit to generate information on the relationships between the characters in accordance with the determined dominant relationships. 16. The apparatus of claim 10 , wherein the information generating unit generates the information on the dominant relationships between the characters with reference to a predetermined time interval or a predetermined specific character of the content. | 0.707657 |
9,697,195 | 1 | 14 | 1. A computing system that generates a lexicon of a social network, the computing system comprising: a processor; and memory storing instructions that, when executed by the processor, provide: a lexicon generator that: for respective messages of the social network: identifies a context of the message, scans the message to identify a set of word sequences, and counts an occurrence of the respective word sequences among the messages within the context; for respective contexts, generates a lexicon for the context that comprises the word sequences identified in the messages with a higher count than a word sequence count threshold; identifies a selected word sequence by which at least one user of the social network who is associated with the selected word sequence is identifiable; and refrains from including the selected word sequence from the lexicon; and a lexicon presenter that, responsive to a selection by a user of a selected context, presents to the user the lexicon of word sequences for the selected context. | 1. A computing system that generates a lexicon of a social network, the computing system comprising: a processor; and memory storing instructions that, when executed by the processor, provide: a lexicon generator that: for respective messages of the social network: identifies a context of the message, scans the message to identify a set of word sequences, and counts an occurrence of the respective word sequences among the messages within the context; for respective contexts, generates a lexicon for the context that comprises the word sequences identified in the messages with a higher count than a word sequence count threshold; identifies a selected word sequence by which at least one user of the social network who is associated with the selected word sequence is identifiable; and refrains from including the selected word sequence from the lexicon; and a lexicon presenter that, responsive to a selection by a user of a selected context, presents to the user the lexicon of word sequences for the selected context. 14. The method of claim 1 , wherein identifying the selected word sequence in the lexicon by which at least one user of the social network who is associated with the selected word sequence is identifiable further comprises: determining that the selected word sequence uniquely identifies at least one user. | 0.68125 |
9,916,299 | 1 | 14 | 1. A computer-implemented method, comprising: receiving multiple data snippets, each of the data snippets comprising one or more n-grams; processing each of the data snippets by: identifying n-grams in the corresponding data snippet that have not occurred in any of the data snippets processed prior to the corresponding data snippet, as unseen n-grams, and computing a snippet score for the corresponding data snippet based on a frequency of the unseen n-grams and a length of the corresponding data snippet; sorting the data snippets based on the snippet scores to generate sorted data snippets; selecting, based on a first specified criterion, one or more of the sorted data snippets as training data for a language processing engine; and storing the training data in a memory, wherein the training data is to be used by the language processing engine to perform automated language processing functions. | 1. A computer-implemented method, comprising: receiving multiple data snippets, each of the data snippets comprising one or more n-grams; processing each of the data snippets by: identifying n-grams in the corresponding data snippet that have not occurred in any of the data snippets processed prior to the corresponding data snippet, as unseen n-grams, and computing a snippet score for the corresponding data snippet based on a frequency of the unseen n-grams and a length of the corresponding data snippet; sorting the data snippets based on the snippet scores to generate sorted data snippets; selecting, based on a first specified criterion, one or more of the sorted data snippets as training data for a language processing engine; and storing the training data in a memory, wherein the training data is to be used by the language processing engine to perform automated language processing functions. 14. The computer-implemented method of claim 1 further comprising: using the training data to train a classifier component of the language processing engine to perform the automated language processing functions. | 0.777778 |
8,230,112 | 18 | 19 | 18. A computer program product comprising: a non-transitory computer-readable medium having instructions encoded thereon, wherein the instructions comprise: a first set of receiving instructions configured to receive a first message from a first sender and a second message from a second sender, wherein the first message is in a first character set and a first native format, the second message is in a second character set and a second native format, the first native format and the second native format are each formats for formatting electronic messages, and the first native format differs from the second native format; a first set of converting instructions configured to convert the first message from the first native format to an independent format and to convert the second message from the second native format to the independent format; a first set of storing instructions configured to store the first message and the second message in the independent format; a first set of selecting instructions configured to select a first display format for the first message and a second display format for the second message, wherein the first display format differs from the first native format, and the second display format differs from the second native format; a second set of converting instructions configured to convert the first message from the independent format to the first display format and to convert the second message from the independent format to the second display format; a first set of displaying instructions configured to display the first message in the first display format, wherein said displaying the first message comprises displaying the first message to the user via an application program client; a second set of displaying instructions configured to display the second message in the second display format, wherein said displaying the second message comprises displaying the second message to the user via the application program client; a second set of receiving instructions configured to receive, via the application program client, a first response to the first message and to receive a second response to the second message, wherein the first response is in a first response character set and a first response format, the second response is in a second response character set and a second response format, the first response format and the second response format each specify a format for formatting electronic messages, the first response format differs from the first native format, and the second response format differs from the second native format; a third set of converting instructions configured to convert the first response from the first response format to the independent format and to convert the second response from the second response format to the independent format; a second set of selecting instructions configured to dynamically select a first final response format for the first response and to dynamically select a second final response format for the second response, wherein said dynamically selecting the first final response format comprises retrieving an indicator of the first native format, if the first final response format is to be the same as the first native format, said dynamically selecting the second final response format comprises retrieving an indicator of the second native format, if the second final response format is to be the same as the second native format, and the first final response format and the second final response format each specify a format for formatting electronic messages; a fourth set of converting instructions configured to convert the first response from the independent format to the first final response format and to convert the second response from the independent format to the second final response format; including instructions configured to include a pre-formulated content in the first response, wherein the pre-formulated content is responsive to a portion of the first message, and the pre-formulated content is provided in the first native format as part of the first response; and providing instructions configured to provide the first response in the first final response format to the first sender and to provide the second response in the second final response format to the second sender. | 18. A computer program product comprising: a non-transitory computer-readable medium having instructions encoded thereon, wherein the instructions comprise: a first set of receiving instructions configured to receive a first message from a first sender and a second message from a second sender, wherein the first message is in a first character set and a first native format, the second message is in a second character set and a second native format, the first native format and the second native format are each formats for formatting electronic messages, and the first native format differs from the second native format; a first set of converting instructions configured to convert the first message from the first native format to an independent format and to convert the second message from the second native format to the independent format; a first set of storing instructions configured to store the first message and the second message in the independent format; a first set of selecting instructions configured to select a first display format for the first message and a second display format for the second message, wherein the first display format differs from the first native format, and the second display format differs from the second native format; a second set of converting instructions configured to convert the first message from the independent format to the first display format and to convert the second message from the independent format to the second display format; a first set of displaying instructions configured to display the first message in the first display format, wherein said displaying the first message comprises displaying the first message to the user via an application program client; a second set of displaying instructions configured to display the second message in the second display format, wherein said displaying the second message comprises displaying the second message to the user via the application program client; a second set of receiving instructions configured to receive, via the application program client, a first response to the first message and to receive a second response to the second message, wherein the first response is in a first response character set and a first response format, the second response is in a second response character set and a second response format, the first response format and the second response format each specify a format for formatting electronic messages, the first response format differs from the first native format, and the second response format differs from the second native format; a third set of converting instructions configured to convert the first response from the first response format to the independent format and to convert the second response from the second response format to the independent format; a second set of selecting instructions configured to dynamically select a first final response format for the first response and to dynamically select a second final response format for the second response, wherein said dynamically selecting the first final response format comprises retrieving an indicator of the first native format, if the first final response format is to be the same as the first native format, said dynamically selecting the second final response format comprises retrieving an indicator of the second native format, if the second final response format is to be the same as the second native format, and the first final response format and the second final response format each specify a format for formatting electronic messages; a fourth set of converting instructions configured to convert the first response from the independent format to the first final response format and to convert the second response from the independent format to the second final response format; including instructions configured to include a pre-formulated content in the first response, wherein the pre-formulated content is responsive to a portion of the first message, and the pre-formulated content is provided in the first native format as part of the first response; and providing instructions configured to provide the first response in the first final response format to the first sender and to provide the second response in the second final response format to the second sender. 19. The computer program product of claim 18 , wherein the instructions further comprise: generating instructions to automatically generate the first response. | 0.503125 |
8,965,926 | 1 | 2 | 1. A system for creating and managing persistent document collections comprising: memory; a data store for storing one or more persistent document collections; and a browser-based content management application that receives user input from a client-side web browser, the content management application being processed to manage documents for a plurality of users, to create a persistent document collection grouping together a plurality of the documents upon user request from the client-side web browser and to assign an attribute to the persistent document collection that specifies any external applications with which the persistent document collection can share its documents, the persistent document collection enabling at least one of the plurality of users to access the plurality of documents as a group, and to store the one or more persistent document collections in the data store, in storage space allocated to the one or more persistent document collections, wherein the content management application is further processed to assign one or more attributes to the persistent document collection, wherein the one or more attributes include an amount of disk space measured in a multiple of bits or a multiple of bytes to allocate to the persistent document collection. | 1. A system for creating and managing persistent document collections comprising: memory; a data store for storing one or more persistent document collections; and a browser-based content management application that receives user input from a client-side web browser, the content management application being processed to manage documents for a plurality of users, to create a persistent document collection grouping together a plurality of the documents upon user request from the client-side web browser and to assign an attribute to the persistent document collection that specifies any external applications with which the persistent document collection can share its documents, the persistent document collection enabling at least one of the plurality of users to access the plurality of documents as a group, and to store the one or more persistent document collections in the data store, in storage space allocated to the one or more persistent document collections, wherein the content management application is further processed to assign one or more attributes to the persistent document collection, wherein the one or more attributes include an amount of disk space measured in a multiple of bits or a multiple of bytes to allocate to the persistent document collection. 2. The system of claim 1 , wherein the content management application is further processed to receive a request from an external application for collection information regarding at least a portion of the persistent document collection, and in response to the request from the external application, the content management application being further processed to retrieve the collection information from the data store and return the collection information to the external application. | 0.501035 |
5,410,612 | 37 | 40 | 37. An apparatus for recognizing characters in image information, comprising: means for inputting coordinate data of a plurality of character patterns; means for displaying the character patterns on the basis of the coordinate data; means for recognizing character information on the basis of the coordinate data; means for determining whether the recognized character information consists of a punctuation mark; and means for outputting the result of character recognition in a case where it has been determined by said determining means that the recognized character information consists of a punctuation mark and, in a case where it has not been determined by said determining means that the recognized character information consists of a punctuation mark, not outputting the result of character recognition, thereby resulting in the reduction of screen display flicker. | 37. An apparatus for recognizing characters in image information, comprising: means for inputting coordinate data of a plurality of character patterns; means for displaying the character patterns on the basis of the coordinate data; means for recognizing character information on the basis of the coordinate data; means for determining whether the recognized character information consists of a punctuation mark; and means for outputting the result of character recognition in a case where it has been determined by said determining means that the recognized character information consists of a punctuation mark and, in a case where it has not been determined by said determining means that the recognized character information consists of a punctuation mark, not outputting the result of character recognition, thereby resulting in the reduction of screen display flicker. 40. An apparatus for recognizing characters in image information according to claim 37, further comprising means for inputting the image information, wherein said recognizing means performs its recognition of character information in response to the determination that inputting of one character of image information is complete. | 0.571615 |
8,583,284 | 23 | 24 | 23. An active perception module comprising a decision making mechanism according to claim 1 , wherein said active perception module is configured to autonomously investigate scenes. | 23. An active perception module comprising a decision making mechanism according to claim 1 , wherein said active perception module is configured to autonomously investigate scenes. 24. The active perception module according to claim 23 , wherein said active perception module is implemented in a robot. | 0.965683 |
7,844,957 | 1 | 14 | 1. A computer-implemented method for designing and compiling custom runtime components that provide improved processing of certain types of messages that comprise data objects serialized for transmission from one system to another, the method comprising: in a graphical user interface, receiving design-time input from a user for characterizing logical structure and physical structure of a particular message type in serialized form; based on said design-time input, creating metadata in markup language format that characterizes the logical structure and physical structure of the particular message type in serialized form; based on the metadata, automatically generating source code for de novo creation of custom runtime components comprising newly created custom source code that is highly optimized for runtime processing of said particular message type; and compiling the source code to create said custom runtime components for deployment in a runtime environment, so that at runtime, messages of the particular message type are processed with said custom runtime components in a manner that is highly optimized for the particular message type. | 1. A computer-implemented method for designing and compiling custom runtime components that provide improved processing of certain types of messages that comprise data objects serialized for transmission from one system to another, the method comprising: in a graphical user interface, receiving design-time input from a user for characterizing logical structure and physical structure of a particular message type in serialized form; based on said design-time input, creating metadata in markup language format that characterizes the logical structure and physical structure of the particular message type in serialized form; based on the metadata, automatically generating source code for de novo creation of custom runtime components comprising newly created custom source code that is highly optimized for runtime processing of said particular message type; and compiling the source code to create said custom runtime components for deployment in a runtime environment, so that at runtime, messages of the particular message type are processed with said custom runtime components in a manner that is highly optimized for the particular message type. 14. The method of claim 1 , wherein processing of messages at runtime includes parsing message data, and wherein only a single copy of parsed message data is maintained in memory. | 0.631687 |
10,142,708 | 6 | 10 | 6. The method of claim 2 , further comprising: identifying, by the at least one processor-based component, at least one candidate for the user to share with based at least in part on the narrative path graph specific to the user and the respective narrative. | 6. The method of claim 2 , further comprising: identifying, by the at least one processor-based component, at least one candidate for the user to share with based at least in part on the narrative path graph specific to the user and the respective narrative. 10. The method of claim 6 wherein identifying at least one candidate for the user to share with based at least in part on the narrative path graph for the user and the respective narrative includes determining a set of metrics that are at least partially representative of the narrative path graph specific to the user and the respective narrative, and comparing the determined set of metrics to at least one other set of metrics that are at least partially representative of a respective narrative path graph specific to another user and the respective narrative. | 0.839225 |
9,378,301 | 1 | 2 | 1. A structured document searching apparatus that stores a plurality of structured document data each including a plurality of elements that are hierarchized, the apparatus comprising: a first store unit that stores a data stream in which the elements included in each of the structured document data are arranged in an order of a result of a syntactic analysis; a second store unit that stores at least one index stream in which the elements that are included in the structured document data and serve as an index when searching the structured document data are arranged in the order of the result of the syntactic analysis; a creating unit that creates a scanning plan that instructs a scanning of the data stream and the index stream based on a search criterion for searching through the structured document data; and an executing unit that executes a scanning on at least one of the data stream and the index stream in accordance with the scanning plan, wherein the creating unit creates the scanning plan, for each structured document data, that instructs a scanning of a first stream among streams including the data stream and the index stream, and instructs a scanning of a second stream in accordance with a result of the scanning of the first stream, based on the search criterion; the data stream includes a plurality of arranged data blocks each having a fixed length, the first store unit embeds, for each the structured document data, a synchronization signal in a leading data block of each of the arranged data blocks, and stores the data stream; and the executing unit executes the scanning of the first stream in accordance with the scanning plan in each structured document data and skipping the scanning of the data blocks between an appearance of a first synchronization signal and an appearance of a second synchronization signal in the second stream as the data stream according to the result of the scanning of the leading data block of the arranged data blocks, thereby executing the scanning of the data stream with the plurality of arranged data blocks between appearance of the synchronization signal and appearance of the second synchronization signal embedded in each structured document data. | 1. A structured document searching apparatus that stores a plurality of structured document data each including a plurality of elements that are hierarchized, the apparatus comprising: a first store unit that stores a data stream in which the elements included in each of the structured document data are arranged in an order of a result of a syntactic analysis; a second store unit that stores at least one index stream in which the elements that are included in the structured document data and serve as an index when searching the structured document data are arranged in the order of the result of the syntactic analysis; a creating unit that creates a scanning plan that instructs a scanning of the data stream and the index stream based on a search criterion for searching through the structured document data; and an executing unit that executes a scanning on at least one of the data stream and the index stream in accordance with the scanning plan, wherein the creating unit creates the scanning plan, for each structured document data, that instructs a scanning of a first stream among streams including the data stream and the index stream, and instructs a scanning of a second stream in accordance with a result of the scanning of the first stream, based on the search criterion; the data stream includes a plurality of arranged data blocks each having a fixed length, the first store unit embeds, for each the structured document data, a synchronization signal in a leading data block of each of the arranged data blocks, and stores the data stream; and the executing unit executes the scanning of the first stream in accordance with the scanning plan in each structured document data and skipping the scanning of the data blocks between an appearance of a first synchronization signal and an appearance of a second synchronization signal in the second stream as the data stream according to the result of the scanning of the leading data block of the arranged data blocks, thereby executing the scanning of the data stream with the plurality of arranged data blocks between appearance of the synchronization signal and appearance of the second synchronization signal embedded in each structured document data. 2. The apparatus according to claim 1 , further comprising an acquiring unit that acquires query data as a search request designating the search criterion, from a client device, wherein the creating unit creates the scanning plan by use of the query data. | 0.734375 |
8,676,787 | 1 | 4 | 1. A system, comprising: a processor; and a memory containing a program that, when executed by the processor, performs an operation for incorporating query results into an abstract database, comprising: receiving a first set of query results produced by executing a first abstract query using a first data abstraction model against a first database; determining one or more mappings between the first set of query results and one or more logical fields in a second data abstraction model, wherein the second data abstraction model models underlying physical data in a manner making a schema of the physical data transparent to a user of the second data abstraction model, further comprising: determining similarities between at least a portion of the first set of query results and at least one field in the second database; and determining at least one logical field that maps to the at least one field in the second database; and modifying one or more logical field definitions within the second data abstraction model to further map to the at least a portion of the first set of query results, based on the determined one or more mappings, wherein the one or more logical field definitions correspond to the one or more logical fields, such that abstract queries can be executed against both a second database and the first set of query results using the modified second data abstraction model, wherein the first database is distinct from the second database, and wherein the first data abstraction model is distinct from the second data abstraction model. | 1. A system, comprising: a processor; and a memory containing a program that, when executed by the processor, performs an operation for incorporating query results into an abstract database, comprising: receiving a first set of query results produced by executing a first abstract query using a first data abstraction model against a first database; determining one or more mappings between the first set of query results and one or more logical fields in a second data abstraction model, wherein the second data abstraction model models underlying physical data in a manner making a schema of the physical data transparent to a user of the second data abstraction model, further comprising: determining similarities between at least a portion of the first set of query results and at least one field in the second database; and determining at least one logical field that maps to the at least one field in the second database; and modifying one or more logical field definitions within the second data abstraction model to further map to the at least a portion of the first set of query results, based on the determined one or more mappings, wherein the one or more logical field definitions correspond to the one or more logical fields, such that abstract queries can be executed against both a second database and the first set of query results using the modified second data abstraction model, wherein the first database is distinct from the second database, and wherein the first data abstraction model is distinct from the second data abstraction model. 4. The system of claim 1 , the operation further comprising: identifying one or more portions of the first database to which the first set of query results correspond; determining one or more portions of the second database that correspond to the identified one or more portions of the first database; determining one or more logical fields in the second data abstraction model that map to the determined one or more portions of the second database; and modifying the one or more logical field definitions for the determined one or more logical fields in the second data abstraction model to further map to the first set of query results. | 0.681637 |
9,582,555 | 1 | 3 | 1. A computer system comprising: one or more processors; a software program, executable on said computer system, the software program configured to: cause an enrichment engine to receive an input data set comprising a first supplier and a second supplier; cause the enrichment engine to perform standardization and cleansing of the input data set, the standardization comprising validating supplier addresses to a locality/city level; cause the enrichment engine to identify duplicate entries in the input data set; cause a matching component of the enrichment engine to perform matching of non-duplicate entries of the input data set to a business compendium having data compiled from a third party source, wherein the matching is performed according to criteria comprising, a first priority comprising name or address having a score greater than a minimum and changed manually in the past, a second priority comprising name or address having the score greater than the minimum and generated by a matching engine, a third priority comprising the third party source as a preferred data provider, and a fourth priority comprising a most recent updating; cause the enrichment engine to create an enriched data set including additional information based upon the matching, wherein the additional information comprises common corporate ownership and a unique supplier location indicating the first supplier as a subsidiary of the second supplier; and providing the enriched data set to a user for manual review. | 1. A computer system comprising: one or more processors; a software program, executable on said computer system, the software program configured to: cause an enrichment engine to receive an input data set comprising a first supplier and a second supplier; cause the enrichment engine to perform standardization and cleansing of the input data set, the standardization comprising validating supplier addresses to a locality/city level; cause the enrichment engine to identify duplicate entries in the input data set; cause a matching component of the enrichment engine to perform matching of non-duplicate entries of the input data set to a business compendium having data compiled from a third party source, wherein the matching is performed according to criteria comprising, a first priority comprising name or address having a score greater than a minimum and changed manually in the past, a second priority comprising name or address having the score greater than the minimum and generated by a matching engine, a third priority comprising the third party source as a preferred data provider, and a fourth priority comprising a most recent updating; cause the enrichment engine to create an enriched data set including additional information based upon the matching, wherein the additional information comprises common corporate ownership and a unique supplier location indicating the first supplier as a subsidiary of the second supplier; and providing the enriched data set to a user for manual review. 3. A computer system as in claim 1 wherein the additional information further comprises diversity information, government sanction information, bankruptcy information, or risk information. | 0.502646 |
8,972,372 | 21 | 23 | 21. A computer implemented method of providing search results, comprising: identifying a plurality of documents that are indexed according to a first scheme and associated with a first set of information; generating a second scheme that associates, based on predefined mapping information, the first set of information with a second set of information; indexing the plurality of documents according to the second scheme and storing the documents in a repository according to the second scheme; in response to receiving a specification that comprises an input-output pair including a first data entity and a second data entity, identifying one or more modules of program code, within the plurality of documents, that have code constraints specified by the second scheme that are satisfiable by a constraint solver with one or more input-output constraints based on the input-output pair; and generating search results referencing the one or more modules of program code that are satisfiable by the constraint solver with the input-output constraints and providing the search results to a user. | 21. A computer implemented method of providing search results, comprising: identifying a plurality of documents that are indexed according to a first scheme and associated with a first set of information; generating a second scheme that associates, based on predefined mapping information, the first set of information with a second set of information; indexing the plurality of documents according to the second scheme and storing the documents in a repository according to the second scheme; in response to receiving a specification that comprises an input-output pair including a first data entity and a second data entity, identifying one or more modules of program code, within the plurality of documents, that have code constraints specified by the second scheme that are satisfiable by a constraint solver with one or more input-output constraints based on the input-output pair; and generating search results referencing the one or more modules of program code that are satisfiable by the constraint solver with the input-output constraints and providing the search results to a user. 23. The method of claim 21 , wherein identifying the one or more modules of program code comprises determining, for each module of program code, whether using the first data entity as an input argument to the module of program code results in an output argument represented within the second data entity. | 0.659955 |
7,675,435 | 5 | 7 | 5. The method of claim 4 , further comprising: modifying contents of at least one of the equivalence table, the digit mapping table, and the dynamic custom tables in response to a modification request. | 5. The method of claim 4 , further comprising: modifying contents of at least one of the equivalence table, the digit mapping table, and the dynamic custom tables in response to a modification request. 7. The method of claim 5 , wherein the modification request is received as part of a configuration update process. | 0.951282 |
9,348,479 | 6 | 7 | 6. The computer storage medium of claim 5 , wherein the determining includes: normalizing the context data into context features that are expressed as name value pairs. | 6. The computer storage medium of claim 5 , wherein the determining includes: normalizing the context data into context features that are expressed as name value pairs. 7. The computer storage medium of claim 6 , wherein the skin package for a user interface is a first skin package for a user interface, and wherein the selecting includes: selecting a second skin package that corresponds to a neutral emotional state when the classification confidence value is below the confidence value threshold. | 0.892463 |
9,100,319 | 7 | 8 | 7. The method of claim 1 , wherein said pre-matching comprises performing string-level matching to determine existence of one or more strings associated with the plurality of predefined conditions within packets of the packet stream. | 7. The method of claim 1 , wherein said pre-matching comprises performing string-level matching to determine existence of one or more strings associated with the plurality of predefined conditions within packets of the packet stream. 8. The method of claim 7 , wherein said pre-matching further comprises performing passive matching of overflow patterns that occur between packet characters or strings within a defined range. | 0.94094 |
8,799,297 | 18 | 19 | 18. A computer-implemented method for evaluating the supply of electronic content on an electronic network, the method comprising: receiving search results history for a plurality of queries by users for at least one keyword; determining, based on the search results history, an abandonment rate for the plurality of queries for the at least one keyword, wherein determining the abandonment rate includes calculating a number of times the users abandoned queries by not clicking on any result in the search results history for the plurality of queries for the at least one keyword; determining a search results variability of the search results history for the plurality of queries for at the least one keyword, wherein determining the search results variability includes calculating a number of unique results contained in the search results history for the plurality of queries for the at least one keyword; determining, with at least one processor, a supply value indicative of a supply of electronic content on the electronic network relating to the at least one keyword, based on at least one of the determined search results variability or the determined abandonment rate; and making available, over the electronic network, an additionally supply of electronic content relating to the at least one keyword based on the determined supply value. | 18. A computer-implemented method for evaluating the supply of electronic content on an electronic network, the method comprising: receiving search results history for a plurality of queries by users for at least one keyword; determining, based on the search results history, an abandonment rate for the plurality of queries for the at least one keyword, wherein determining the abandonment rate includes calculating a number of times the users abandoned queries by not clicking on any result in the search results history for the plurality of queries for the at least one keyword; determining a search results variability of the search results history for the plurality of queries for at the least one keyword, wherein determining the search results variability includes calculating a number of unique results contained in the search results history for the plurality of queries for the at least one keyword; determining, with at least one processor, a supply value indicative of a supply of electronic content on the electronic network relating to the at least one keyword, based on at least one of the determined search results variability or the determined abandonment rate; and making available, over the electronic network, an additionally supply of electronic content relating to the at least one keyword based on the determined supply value. 19. The method of claim 18 , further including: determining an advertising value associated with the at least one keyword; adjusting the supply value based the determined advertising value; and making available the additional supply of electronic content relating to the at least one keyword based on the adjusted supply value. | 0.727045 |
9,726,267 | 2 | 6 | 2. The system of claim 1 , wherein the drive mechanism comprises: a drive shaft having a threaded portion; a first bearing to facilitate rotation of the drive shaft, the bearing being configured to support the drive shaft and interface with the fixed support member; and a drive member engaged with the threaded portion of the drive shaft and configured to be fixed to the translatable member to facilitate translation relative to the threaded portion upon rotation of the drive shaft, wherein an angle of misalignment of the bearing compensates for drive shaft rotational misalignment, and wherein a position of the drive member is adjustable upon assembly to compensate for drive axis translational misalignment. | 2. The system of claim 1 , wherein the drive mechanism comprises: a drive shaft having a threaded portion; a first bearing to facilitate rotation of the drive shaft, the bearing being configured to support the drive shaft and interface with the fixed support member; and a drive member engaged with the threaded portion of the drive shaft and configured to be fixed to the translatable member to facilitate translation relative to the threaded portion upon rotation of the drive shaft, wherein an angle of misalignment of the bearing compensates for drive shaft rotational misalignment, and wherein a position of the drive member is adjustable upon assembly to compensate for drive axis translational misalignment. 6. The system of claim 2 , further comprising: a second bearing configured to interface with the fixed support member the first bearing, the second bearing having clearance for the drive shaft extending therethrough; and a spring configured to act on an inner race of the second bearing to facilitate preload of the first and second bearings. | 0.833496 |
7,921,018 | 2 | 5 | 2. The translation system according to claim 1 , wherein the translation server further comprises a translation dictionary updating part, which updates the dictionaries or replaces a dictionary to which the translation engine refers with another dictionary. | 2. The translation system according to claim 1 , wherein the translation server further comprises a translation dictionary updating part, which updates the dictionaries or replaces a dictionary to which the translation engine refers with another dictionary. 5. The translation system according to claim 2 , wherein the user dictionary editing part adds contents of a specified user dictionary stored in the user dictionary shared database of the user community server into the user dictionary by the translation dictionary updating part. | 0.878059 |
6,161,130 | 41 | 65 | 41. The apparatus in claim 37 wherein the processor, in response to the stored instructions: detects whether each of a first group of predefined handcrafted features exists in the incoming message so as to yield first output data; analyzes text in the incoming message so as to break the text into a plurality of constituent tokens; ascertains, using a word-oriented indexer and in response to said tokens, whether each of a second group of predefined word-oriented features exists in the incoming message so as to yield second output data, said first and second groups collectively defining an n-element feature space (where n is an integer greater than N); forms, in response to the first and second output data, an N-element feature vector which specifies whether each of said N features exists in the incoming message; and applies the feature vector as input to the probabilistic classifier so as to yield the output confidence level for the incoming message. | 41. The apparatus in claim 37 wherein the processor, in response to the stored instructions: detects whether each of a first group of predefined handcrafted features exists in the incoming message so as to yield first output data; analyzes text in the incoming message so as to break the text into a plurality of constituent tokens; ascertains, using a word-oriented indexer and in response to said tokens, whether each of a second group of predefined word-oriented features exists in the incoming message so as to yield second output data, said first and second groups collectively defining an n-element feature space (where n is an integer greater than N); forms, in response to the first and second output data, an N-element feature vector which specifies whether each of said N features exists in the incoming message; and applies the feature vector as input to the probabilistic classifier so as to yield the output confidence level for the incoming message. 65. The apparatus in claim 41 wherein the processor, in response to the stored instructions, updates, from a remote server, the probabilistic classifier and definitions of features associated with the first class. | 0.864848 |
7,683,916 | 7 | 9 | 7. A method performed by a computing system having a processor, comprising: under control of the processor, selecting a first template comprising a foreground image with at least one cutout region; producing a user-defined graphics edit by adjusting at least one parameter associated with a selected editable object; importing at least a part of the user-defined graphics edit into the cutout region of the first template; selecting a second template comprising a foreground image with at least one cutout region; and importing at least a part of the user-defined graphics edit into the cutout region of the second template. | 7. A method performed by a computing system having a processor, comprising: under control of the processor, selecting a first template comprising a foreground image with at least one cutout region; producing a user-defined graphics edit by adjusting at least one parameter associated with a selected editable object; importing at least a part of the user-defined graphics edit into the cutout region of the first template; selecting a second template comprising a foreground image with at least one cutout region; and importing at least a part of the user-defined graphics edit into the cutout region of the second template. 9. The method of claim 7 , further comprising storing the user-defined graphics edit, and wherein importing at least a part of the user-defined graphics edit into the cutout region of the second template further comprises using the stored user-defined graphics edit. | 0.729124 |
9,628,506 | 12 | 14 | 12. The system of claim 11 , wherein the facets comprise a text facet. | 12. The system of claim 11 , wherein the facets comprise a text facet. 14. The system of claim 12 , wherein the facets comprise at least one of: an origin facet; a destination facet; and an attachment facet. | 0.960164 |
8,372,122 | 11 | 16 | 11. A spine stabilization device comprising: a first element; a second element; and a self-centering ball-joint connecting the first element and the second element; wherein the self-centering ball-joint includes, a housing having a socket; a ball-rod received in the socket; and a centering rod having an inner core and an outer sheath, the centering rod received partially within the ball-rod and partially within the housing; and whereby deflection of the ball-rod bends the centering rod and the centering rod exerts a restoring force on the ball-rod. | 11. A spine stabilization device comprising: a first element; a second element; and a self-centering ball-joint connecting the first element and the second element; wherein the self-centering ball-joint includes, a housing having a socket; a ball-rod received in the socket; and a centering rod having an inner core and an outer sheath, the centering rod received partially within the ball-rod and partially within the housing; and whereby deflection of the ball-rod bends the centering rod and the centering rod exerts a restoring force on the ball-rod. 16. The spine stabilization device of claim 11 , wherein the first element is a bone anchor. | 0.941698 |
7,643,822 | 1 | 3 | 1. A method, comprising: receiving an incoming message containing a text query and a query type indicator, the text query and the query type indicator being distinct pieces of information specified by a user on a mobile device, the text query including a user-specified keyword for a search, the key word being at least one of an event, an entity, a product, a term, a stock quotation, a particular contact, a particular location, and a mathematical expression, and the query type indicator identifying at least one category of content to be searched based on the text query including the user-specified keyword; extracting the text query from the incoming message, the incoming message being any one of a Short Messaging Service (SMS) message, a Multimedia Message Service (MMS) message, and an email message; determining at least one of a plurality of query types for the query based on the query type indicator; obtaining a result of a search using the at least one of the plurality of query types, the result of the search being based on a search of content for the user-specified keyword; and creating an outgoing message with the result of the search for delivery to the mobile device. | 1. A method, comprising: receiving an incoming message containing a text query and a query type indicator, the text query and the query type indicator being distinct pieces of information specified by a user on a mobile device, the text query including a user-specified keyword for a search, the key word being at least one of an event, an entity, a product, a term, a stock quotation, a particular contact, a particular location, and a mathematical expression, and the query type indicator identifying at least one category of content to be searched based on the text query including the user-specified keyword; extracting the text query from the incoming message, the incoming message being any one of a Short Messaging Service (SMS) message, a Multimedia Message Service (MMS) message, and an email message; determining at least one of a plurality of query types for the query based on the query type indicator; obtaining a result of a search using the at least one of the plurality of query types, the result of the search being based on a search of content for the user-specified keyword; and creating an outgoing message with the result of the search for delivery to the mobile device. 3. The method of claim 1 wherein obtaining the result of the search comprises: forwarding the query to a search engine associated with the one of the plurality of query types; and receiving the result of the search from the search engine associated with the one of the plurality of query types. | 0.716763 |
8,954,909 | 9 | 11 | 9. The method of claim 7 further comprises creating one or more lemmas from the set of constraints that represent the violation and conjoining the lemmas with the abstraction model for subsequent iterations. | 9. The method of claim 7 further comprises creating one or more lemmas from the set of constraints that represent the violation and conjoining the lemmas with the abstraction model for subsequent iterations. 11. The method of claim 9 further comprises extracting one or more subset of constraints from the set of constraints that represent the violation and creating the lemmas by negating the subsets of constraints. | 0.930426 |
4,582,441 | 1 | 5 | 1. An electronic keyboard entry and display device having a character display point comprising: means for encoding words descriptive of text segments to be associated with preselected positions of said display point on said display, means for generating audible tones understandable as said words responsive to said encoded words, said means for generating said audible tones responsive to the positioning of said display point in a predetermined manner in said preselected position by said electronic keyboard entry device, thereby audibly prompting an operator as to the proper text to be typed at said preselected position. | 1. An electronic keyboard entry and display device having a character display point comprising: means for encoding words descriptive of text segments to be associated with preselected positions of said display point on said display, means for generating audible tones understandable as said words responsive to said encoded words, said means for generating said audible tones responsive to the positioning of said display point in a predetermined manner in said preselected position by said electronic keyboard entry device, thereby audibly prompting an operator as to the proper text to be typed at said preselected position. 5. The electronic keyboard entry and display device of claim 1 wherein said device comprises a typewriter and said display point being the typewriter print point. | 0.8875 |
9,672,289 | 10 | 11 | 10. A matching service system, comprising: a number of communications ports which provide communications with a plurality of end user devices, the end user devices logically associable with a plurality of end user client accounts of the matching services, the end user client accounts logically associable with end user clients of the matching service; at least one nontransitory processor-readable medium that stores at least one of processor executable instructions or data; and at least one processor communicatively coupled to the communications ports and at least one nontransitory processor-readable medium, and that: for each of a number of respective end user clients, identifies a plurality of potential candidates, each of the potential candidates associated with a value indicative of a quality of a match between the respective potential candidate and the respective end user client; for each of at least two of the potential candidates, determines a size for a respective graphical object based on the respective quality of potential match between the respective potential candidate and the respective end user client, wherein the graphical objects are windows and the at least one processor determines the size of the window based at least in part on an assessment of the quality of a match between the respective potential candidate and the respective end user client, and the at least one processor selects a diagonal dimension of the respective window from at least three sizes, the diagonal dimension for higher quality matches larger than the diagonal dimension for lower quality matches; and causes a presentation to the respective end user client of at least two of the graphical objects at the determined size. | 10. A matching service system, comprising: a number of communications ports which provide communications with a plurality of end user devices, the end user devices logically associable with a plurality of end user client accounts of the matching services, the end user client accounts logically associable with end user clients of the matching service; at least one nontransitory processor-readable medium that stores at least one of processor executable instructions or data; and at least one processor communicatively coupled to the communications ports and at least one nontransitory processor-readable medium, and that: for each of a number of respective end user clients, identifies a plurality of potential candidates, each of the potential candidates associated with a value indicative of a quality of a match between the respective potential candidate and the respective end user client; for each of at least two of the potential candidates, determines a size for a respective graphical object based on the respective quality of potential match between the respective potential candidate and the respective end user client, wherein the graphical objects are windows and the at least one processor determines the size of the window based at least in part on an assessment of the quality of a match between the respective potential candidate and the respective end user client, and the at least one processor selects a diagonal dimension of the respective window from at least three sizes, the diagonal dimension for higher quality matches larger than the diagonal dimension for lower quality matches; and causes a presentation to the respective end user client of at least two of the graphical objects at the determined size. 11. The matching service system of claim 10 wherein the at least one processor determines the size to provide a visual indication of an assessed relative rank or relative degree of match between the respective potential candidate and the respective end user client. | 0.78243 |
8,805,684 | 1 | 2 | 1. A method comprising: performing, by a user device, automatic speech recognition (ASR) on received utterances, wherein performing the ASR includes: generating feature vectors based on the utterances, updating the feature vectors based on feature-space speaker adaptation parameters, transcribing the utterances to text strings, wherein the transcriptions are based at least in part on an acoustic model and the updated feature vectors, and updating the feature-space speaker adaptation parameters based on the feature vectors; transmitting, by the user device, a representation of at least some of the utterances to a computing device for development of an updated acoustic model; after transmitting the representation, receiving, by the user device, the updated acoustic model from the computing device, wherein the updated acoustic model is based on the representation; and replacing, by the user device, the acoustic model with the updated acoustic model, wherein the feature-space speaker adaptation parameters are updated more frequently than the acoustic model is updated, and wherein the acoustic model is updated when the computing device has received a threshold extent of the representations from the user device. | 1. A method comprising: performing, by a user device, automatic speech recognition (ASR) on received utterances, wherein performing the ASR includes: generating feature vectors based on the utterances, updating the feature vectors based on feature-space speaker adaptation parameters, transcribing the utterances to text strings, wherein the transcriptions are based at least in part on an acoustic model and the updated feature vectors, and updating the feature-space speaker adaptation parameters based on the feature vectors; transmitting, by the user device, a representation of at least some of the utterances to a computing device for development of an updated acoustic model; after transmitting the representation, receiving, by the user device, the updated acoustic model from the computing device, wherein the updated acoustic model is based on the representation; and replacing, by the user device, the acoustic model with the updated acoustic model, wherein the feature-space speaker adaptation parameters are updated more frequently than the acoustic model is updated, and wherein the acoustic model is updated when the computing device has received a threshold extent of the representations from the user device. 2. The method of claim 1 , further comprising: receiving, by the user device, new feature-space speaker adaptation parameters; and replacing, by the user device, the updated feature-space speaker adaptation parameters with the new feature-space speaker adaptation parameters. | 0.667874 |
7,991,720 | 35 | 36 | 35. A data processing system as in claim 32 , further comprising: means for displaying a user interface for confirmation of updating said collective mathematical representation. | 35. A data processing system as in claim 32 , further comprising: means for displaying a user interface for confirmation of updating said collective mathematical representation. 36. A data processing system as in claim 35 , wherein said user interface allows manual modification to said collective mathematical representation. | 0.91676 |
8,175,333 | 1 | 3 | 1. A method for analyzing an object being tracked in a sequence of video frames, comprising: receiving a representation of the tracked object, as depicted by a current video frame, of the sequence of video frames; evaluating, by operation of one or more computer processors, the representation of the tracked object using at least a first classifier and a second classifier, wherein the first classifier is configured to determine a first classification score indicating whether the tracked object depicts an instance of a first classification type, and wherein the second classifier is configured to determine a second classification score indicating whether the tracked object depicts an instance of a second classification type; adding the first classification score to a first rolling average, wherein the first rolling average provides an average of the first classification score determined for the tracked object for each of a specified number of previous video frames, of the plurality of; adding the second classification score to a second rolling average, wherein the second rolling average provides an average of the second classification score determined for the tracked object for each of a specified number of previous video frames, of the plurality, wherein the final classification value is determined from the first rolling average and the second rolling average; determining a final classification value for the tracked object in the current video frame, based on the first and second rolling averages; and passing the final classification value for the tracked objects to a machine learning engine configured to identify patterns of behavior engaged in by the tracked object, based at least in part on the final classification value. | 1. A method for analyzing an object being tracked in a sequence of video frames, comprising: receiving a representation of the tracked object, as depicted by a current video frame, of the sequence of video frames; evaluating, by operation of one or more computer processors, the representation of the tracked object using at least a first classifier and a second classifier, wherein the first classifier is configured to determine a first classification score indicating whether the tracked object depicts an instance of a first classification type, and wherein the second classifier is configured to determine a second classification score indicating whether the tracked object depicts an instance of a second classification type; adding the first classification score to a first rolling average, wherein the first rolling average provides an average of the first classification score determined for the tracked object for each of a specified number of previous video frames, of the plurality of; adding the second classification score to a second rolling average, wherein the second rolling average provides an average of the second classification score determined for the tracked object for each of a specified number of previous video frames, of the plurality, wherein the final classification value is determined from the first rolling average and the second rolling average; determining a final classification value for the tracked object in the current video frame, based on the first and second rolling averages; and passing the final classification value for the tracked objects to a machine learning engine configured to identify patterns of behavior engaged in by the tracked object, based at least in part on the final classification value. 3. The method of claim 1 , further comprising, analyzing the tracked object to determine one or more instance-specific attributes of the object depicted in the sequence of video frames based on a final classification value indicating that the tracked object depicts one of vehicle or a person. | 0.863594 |
9,430,570 | 1 | 16 | 1. A system for creating a personal ranking of specific to a user information, the system comprising: an interface for interfacing with online data, stored user data, and user device data; a processor executing an application to configured for analyzing information derived or inferred at least in part from the online data, the stored user data, and the user device data, wherein the analysis incorporates criteria derived or inferred from both the stored user data and the user device data; a database, the database configured for storing data relating to inputs and/or outputs of the relevance engine or the application, wherein the relevance engine is further configured for generating a series of personalised attention rankings outputs accessible by the user device by applying both a user-specific attention profile and a user-specific psychometric profile in producing machine readable, user-specific attention ranking of the online data; and further wherein the personalised attention rankings outputs are generated at least partially in response to changes in the personalised attention profile outputs or changes in the personalised the psychometric profile outputs specific to the user. | 1. A system for creating a personal ranking of specific to a user information, the system comprising: an interface for interfacing with online data, stored user data, and user device data; a processor executing an application to configured for analyzing information derived or inferred at least in part from the online data, the stored user data, and the user device data, wherein the analysis incorporates criteria derived or inferred from both the stored user data and the user device data; a database, the database configured for storing data relating to inputs and/or outputs of the relevance engine or the application, wherein the relevance engine is further configured for generating a series of personalised attention rankings outputs accessible by the user device by applying both a user-specific attention profile and a user-specific psychometric profile in producing machine readable, user-specific attention ranking of the online data; and further wherein the personalised attention rankings outputs are generated at least partially in response to changes in the personalised attention profile outputs or changes in the personalised the psychometric profile outputs specific to the user. 16. The system according to claim 1 , wherein the interface is configured to be adapted or extended to support additional volume of the online data and/or stored user data sources. | 0.811715 |
8,478,052 | 1 | 4 | 1. A computer-implemented method, comprising: obtaining a plurality of n-grams, each of the n-grams including a unique set of one or more terms; for each of the n-grams: identifying, in a processing device, a plurality of training images for training an image classification model, the plurality of training images comprising: positive training images having relevance measures, for the n-gram, that satisfy a relevance threshold; and negative training images having relevance measures, for the n-gram, that do not satisfy the relevance threshold; selecting, in the processing device, a training image from the plurality of training images, wherein the selecting comprises semi-randomly selecting the training image subject to a selection requirement specifying that a second image be selected with a specified likelihood; classifying, in the processing device, the training image with the image classification model based on a feature vector of the training image, the feature vector comprising image feature values for the training image; and training, in the processing device, the image classification model based on the feature vector of the training image and the classification of the training image. | 1. A computer-implemented method, comprising: obtaining a plurality of n-grams, each of the n-grams including a unique set of one or more terms; for each of the n-grams: identifying, in a processing device, a plurality of training images for training an image classification model, the plurality of training images comprising: positive training images having relevance measures, for the n-gram, that satisfy a relevance threshold; and negative training images having relevance measures, for the n-gram, that do not satisfy the relevance threshold; selecting, in the processing device, a training image from the plurality of training images, wherein the selecting comprises semi-randomly selecting the training image subject to a selection requirement specifying that a second image be selected with a specified likelihood; classifying, in the processing device, the training image with the image classification model based on a feature vector of the training image, the feature vector comprising image feature values for the training image; and training, in the processing device, the image classification model based on the feature vector of the training image and the classification of the training image. 4. The method of claim 1 , wherein identifying the plurality of training images comprises identifying, as a positive training image, an image having at least a threshold selection rate when referenced in search results for the n-gram. | 0.832138 |
9,477,662 | 1 | 3 | 1. A method of tracking quality measures in one or more documents, the method comprising: receiving, from a provider device, a document; extracting at least one item from the received document; determining at least one code based on the at least one extracted item; determining at least one quality measure included in at least one of the at least one code and the at least one extracted item, the at least one quality measure including at least one quality measure criterion; determining whether a quality measure criterion of the at least one quality measure criterion remains unsatisfied; and generating a score indicating a performance of quality measures. | 1. A method of tracking quality measures in one or more documents, the method comprising: receiving, from a provider device, a document; extracting at least one item from the received document; determining at least one code based on the at least one extracted item; determining at least one quality measure included in at least one of the at least one code and the at least one extracted item, the at least one quality measure including at least one quality measure criterion; determining whether a quality measure criterion of the at least one quality measure criterion remains unsatisfied; and generating a score indicating a performance of quality measures. 3. The method of claim 1 further comprising evaluating at least one word neighboring the at least one extracted item to determine a context of the at least one extracted item. | 0.780151 |
7,689,412 | 11 | 13 | 11. The computer readable storage medium of claim 6 wherein selecting comprises constructing a translation model of probabilities of candidate synonymous collocations as a function of the collocation translations. | 11. The computer readable storage medium of claim 6 wherein selecting comprises constructing a translation model of probabilities of candidate synonymous collocations as a function of the collocation translations. 13. The computer readable storage medium of claim 11 wherein constructing a translation model includes using a bilingual corpus to approximate word translation probabilities of component words of the candidate synonymous collocations. | 0.81804 |
8,719,733 | 10 | 14 | 10. A system for implementing website navigation, comprising: a processor configured to: derive a confidence level for at least one leaf node of a website navigation category diagram using historical user operation data, wherein the confidence level for the at least one lead node is determined based at least in part on historical search behavior data; generate a plurality of navigation hierarchical structure diagrams based on the website navigation category diagram, wherein for at least one of the plurality of navigation hierarchical structure diagrams, leaf nodes with a corresponding confidence level greater than a preset threshold value are included in a predetermined level of the navigation hierarchical structure diagram; determine a searching cost associated with each of the plurality of navigation hierarchical structure diagrams; determine the navigation hierarchical structure diagram associated with the lowest to searching cost; and implement at least in part a website navigation process using the navigation hierarchical structure diagram associated with the lowest searching cost; and a memory coupled to the processor and configured to provide the processor with instructions. | 10. A system for implementing website navigation, comprising: a processor configured to: derive a confidence level for at least one leaf node of a website navigation category diagram using historical user operation data, wherein the confidence level for the at least one lead node is determined based at least in part on historical search behavior data; generate a plurality of navigation hierarchical structure diagrams based on the website navigation category diagram, wherein for at least one of the plurality of navigation hierarchical structure diagrams, leaf nodes with a corresponding confidence level greater than a preset threshold value are included in a predetermined level of the navigation hierarchical structure diagram; determine a searching cost associated with each of the plurality of navigation hierarchical structure diagrams; determine the navigation hierarchical structure diagram associated with the lowest to searching cost; and implement at least in part a website navigation process using the navigation hierarchical structure diagram associated with the lowest searching cost; and a memory coupled to the processor and configured to provide the processor with instructions. 14. The method of claim 10 , wherein the plurality of navigation hierarchical structure diagrams is generated using one or more of the following binding conditions: a total number of leaf nodes in a level of the navigation hierarchical structure diagram is no greater than a number of leaf nodes for which associated information is permitted to be displayed at a web page; for a particular leaf node that appears more frequently in the navigation hierarchical structure diagram more than a substantially similar leaf node appears at the website navigation category diagram, at least one copy of that particular leaf node is eliminated. | 0.606079 |
9,922,643 | 11 | 12 | 11. The method according to claim 1 , further comprising obtaining or generating multiple search pronunciations for the search term, wherein the number of search pronunciations for the search term is equal to or exceeds a threshold number of pronunciations. | 11. The method according to claim 1 , further comprising obtaining or generating multiple search pronunciations for the search term, wherein the number of search pronunciations for the search term is equal to or exceeds a threshold number of pronunciations. 12. The method according to claim 11 , wherein the audio sections are retrieved from the speech of the at least one speaker in locations that correspond to the pronunciation elements of the provided at least one search term. | 0.950877 |
8,838,613 | 21 | 26 | 21. A system comprising: one or more computer processors; and one or more non-transitory computer readable devices that include instructions that, when executed by the one or more computer processors, causes the processors to perform operations, the operations comprising: identifying, with one or more processors, a set T of query terms; selecting, with the one or more processors, candidate documents D that each satisfy one or more of the terms of the set T; selecting, from the candidate documents D, a set S of quantity k of the candidate documents D based on a weighted-coverage function ƒ for the set T of terms; pairing each document in the set S with another document in the set S based on a distance between the paired documents; generating, with the one or more processors, a set C of clusters from the paired documents, each cluster in the set C being associated with one or more topics of the query terms; selecting, with the one or more processors, from the set S, a set V of quantity p documents, for one or more clusters of the set C, based on a diversity function, wherein the diversity function is expressed as (Σ 1≦i≦k, 1≦j≦p Σ 1≦i′≦k′, 1≦j′≦p GJD(D i j , D i′ j′ )); and providing for presentation the set V of documents as representative documents that cover one or more topics associated with the one or more clusters. | 21. A system comprising: one or more computer processors; and one or more non-transitory computer readable devices that include instructions that, when executed by the one or more computer processors, causes the processors to perform operations, the operations comprising: identifying, with one or more processors, a set T of query terms; selecting, with the one or more processors, candidate documents D that each satisfy one or more of the terms of the set T; selecting, from the candidate documents D, a set S of quantity k of the candidate documents D based on a weighted-coverage function ƒ for the set T of terms; pairing each document in the set S with another document in the set S based on a distance between the paired documents; generating, with the one or more processors, a set C of clusters from the paired documents, each cluster in the set C being associated with one or more topics of the query terms; selecting, with the one or more processors, from the set S, a set V of quantity p documents, for one or more clusters of the set C, based on a diversity function, wherein the diversity function is expressed as (Σ 1≦i≦k, 1≦j≦p Σ 1≦i′≦k′, 1≦j′≦p GJD(D i j , D i′ j′ )); and providing for presentation the set V of documents as representative documents that cover one or more topics associated with the one or more clusters. 26. The system of claim 21 , wherein identifying the set T of recent, popular query terms comprises: determining a query score for each of multiple query terms that have been submitted to a search engine within a predetermined period of time; and selecting, as part of the set T of query terms, the query terms that have been submitted to the search engine within the predetermined period of time and that have a query score which satisfies a threshold. | 0.714376 |
9,031,384 | 3 | 9 | 3. The interesting section identifying device of claim 2 , further comprising a reference index calculating unit configured to calculate a reference vector based on a plurality of second unit section frequency vectors in a reference section composed of a plurality of continuous second unit sections that include the designated time, and to assign, to the reference value, a largest one of the variances of the second unit sections included in the reference section, wherein the interesting section candidate extracting unit initially designates the reference section as a temporary interesting section candidate and repeats a process of (i) determining whether the second unit section frequency vector of a second unit section adjacent to the temporary interesting section candidate has at least a predetermined correlation to the reference vector and (ii) including the second unit section adjacent to the temporary interesting section candidate in the temporary interesting section candidate when determining that the second unit section frequency vector and the reference vector have at least the predetermined correlation, the interesting section candidate extracting unit terminating repetition of the process and designating the temporary interesting section candidate as the interesting section candidate upon determining that the second unit section frequency vector and the reference vector do not have at least the predetermined correlation. | 3. The interesting section identifying device of claim 2 , further comprising a reference index calculating unit configured to calculate a reference vector based on a plurality of second unit section frequency vectors in a reference section composed of a plurality of continuous second unit sections that include the designated time, and to assign, to the reference value, a largest one of the variances of the second unit sections included in the reference section, wherein the interesting section candidate extracting unit initially designates the reference section as a temporary interesting section candidate and repeats a process of (i) determining whether the second unit section frequency vector of a second unit section adjacent to the temporary interesting section candidate has at least a predetermined correlation to the reference vector and (ii) including the second unit section adjacent to the temporary interesting section candidate in the temporary interesting section candidate when determining that the second unit section frequency vector and the reference vector have at least the predetermined correlation, the interesting section candidate extracting unit terminating repetition of the process and designating the temporary interesting section candidate as the interesting section candidate upon determining that the second unit section frequency vector and the reference vector do not have at least the predetermined correlation. 9. The interesting section identifying device of claim 3 , wherein the reference index calculating unit is further configured to calculate a threshold KL (Kullback-Leibler) divergence as a KL divergence from the reference vector to a second unit section frequency vector having a greatest KL divergence from the reference vector among a plurality of second unit section frequency vectors included in the reference section, and the interesting section candidate extracting unit initially designates the reference section as a temporary interesting section candidate and repeats a process of (i) determining whether the KL divergence from the reference vector to the second unit section frequency vector of the second unit section adjacent to the temporary interesting section exceeds the threshold KL divergence and (ii) including the second unit section adjacent to the temporary interesting section candidate in the temporary interesting section candidate when determining that the KL divergence does not exceed the threshold KL divergence, the interesting section candidate extracting unit terminating repetition of the process and designating the temporary interesting section candidate as the interesting section candidate upon determining that the KL divergence exceeds the threshold KL divergence. | 0.678113 |
7,649,877 | 12 | 14 | 12. A method comprising: providing a user interface to a display device of a mobile device, the user interface including a first area to receive a text message and a second area to receive an identifier associated with an addressee device; receiving the text message and the identifier; submitting the text message for conversion into an audio message and for transmission of the audio message and an acknowledge message to the addressee device associated with the identifier, wherein the acknowledge message permits the addressee device to accept delivery of the audio message or to decline delivery of the audio message; receiving, at the mobile device, a reply voice message in response to the addressee device accepting delivery of the audio message; and providing a repeat input option at the user interface, the repeat input option to specify an option to automatically attempt one or more additional transmissions in response to the addressee device not accepting or declining delivery of the audio message. | 12. A method comprising: providing a user interface to a display device of a mobile device, the user interface including a first area to receive a text message and a second area to receive an identifier associated with an addressee device; receiving the text message and the identifier; submitting the text message for conversion into an audio message and for transmission of the audio message and an acknowledge message to the addressee device associated with the identifier, wherein the acknowledge message permits the addressee device to accept delivery of the audio message or to decline delivery of the audio message; receiving, at the mobile device, a reply voice message in response to the addressee device accepting delivery of the audio message; and providing a repeat input option at the user interface, the repeat input option to specify an option to automatically attempt one or more additional transmissions in response to the addressee device not accepting or declining delivery of the audio message. 14. The method of claim 12 , wherein the user interface further comprises a third area to receive a language indicator specifying a desired language, and wherein the text message is translated into the desired language before the text message is converted into the audio message. | 0.734791 |
9,292,360 | 15 | 18 | 15. A computer, comprising: a processor; and a computer-readable storage medium in communication with the processor, the computer-readable storage medium having computer readable instructions stored thereupon that, when executed by the processor, cause the processor to execute a plurality of application programs configured to expose a set of programming language application programming interfaces for interacting with the plurality of application programs and for interacting with a file associated with at least one of the plurality of application programs, initialize, via the set application programming language interfaces, an extension object for supplementing functionality of the plurality of application programs by implementing an initialize delegate defined in a programming language file, the initialize delegate comprising a reason parameter for defining a context in which the extension object is initialized, access, via the set of application programming language interfaces, a section of the file the one of the plurality of application programs interacts with, identify the section of the file as a generic data type that is applicable to individual ones of the plurality of application programs, and perform an operation on the section by presenting a user interface for selecting a portion of the file, wherein the user interface is configured to allow a user to select the portion of the file and to return the selected portion of the file to the extension object for use as a binding. | 15. A computer, comprising: a processor; and a computer-readable storage medium in communication with the processor, the computer-readable storage medium having computer readable instructions stored thereupon that, when executed by the processor, cause the processor to execute a plurality of application programs configured to expose a set of programming language application programming interfaces for interacting with the plurality of application programs and for interacting with a file associated with at least one of the plurality of application programs, initialize, via the set application programming language interfaces, an extension object for supplementing functionality of the plurality of application programs by implementing an initialize delegate defined in a programming language file, the initialize delegate comprising a reason parameter for defining a context in which the extension object is initialized, access, via the set of application programming language interfaces, a section of the file the one of the plurality of application programs interacts with, identify the section of the file as a generic data type that is applicable to individual ones of the plurality of application programs, and perform an operation on the section by presenting a user interface for selecting a portion of the file, wherein the user interface is configured to allow a user to select the portion of the file and to return the selected portion of the file to the extension object for use as a binding. 18. The computer of claim 15 , wherein initializing the extension object further comprises initializing a settings object for obtaining settings associated with the extension object and implementing the settings during initialization of the extension object. | 0.752874 |
8,433,572 | 10 | 13 | 10. A spoken dialog system comprising a processor and a memory encoded with computer-executable instructions that, when executed by the processor, perform a method for multiple value confirmation and correction, comprising: responsive to collecting a set of values from a user, inviting the user to interrupt a spoken confirmation of the set of values, previously collected from the user, upon hearing an incorrect value, to correct the incorrect value; presenting the spoken confirmation of the set of previously collected values to the user wherein the spoken confirmation includes a sequence of prompts spoken by the spoken dialog system, the sequence of prompts comprising spoken confirmation values of the set of previously collected values as understood by the spoken dialog system and a pause after each of the spoken confirmation values, each pause except the last immediately followed by a next spoken confirmation value in the sequence of prompts; detecting a user interruption of a spoken confirmation value or a pause in the sequence of prompts, wherein the user interruption comprises an indication that the spoken confirmation value is incorrect; halting the sequence of prompts being spoken by the spoken dialog system in response to detecting the user interruption; collecting a corrected value from the user in response to detecting the user interruption; and providing a new spoken confirmation to the user, wherein the new spoken confirmation includes the corrected value. | 10. A spoken dialog system comprising a processor and a memory encoded with computer-executable instructions that, when executed by the processor, perform a method for multiple value confirmation and correction, comprising: responsive to collecting a set of values from a user, inviting the user to interrupt a spoken confirmation of the set of values, previously collected from the user, upon hearing an incorrect value, to correct the incorrect value; presenting the spoken confirmation of the set of previously collected values to the user wherein the spoken confirmation includes a sequence of prompts spoken by the spoken dialog system, the sequence of prompts comprising spoken confirmation values of the set of previously collected values as understood by the spoken dialog system and a pause after each of the spoken confirmation values, each pause except the last immediately followed by a next spoken confirmation value in the sequence of prompts; detecting a user interruption of a spoken confirmation value or a pause in the sequence of prompts, wherein the user interruption comprises an indication that the spoken confirmation value is incorrect; halting the sequence of prompts being spoken by the spoken dialog system in response to detecting the user interruption; collecting a corrected value from the user in response to detecting the user interruption; and providing a new spoken confirmation to the user, wherein the new spoken confirmation includes the corrected value. 13. The spoken dialog system of claim 10 , wherein the spoken confirmation and the new spoken confirmation comprise at least one carrier phrase. | 0.660377 |
7,561,780 | 48 | 56 | 48. A method for decoding a text subtitle stream downloaded from an external source, the method comprising: loading the text subtitle stream into a subtitle loading buffer, the text subtitle stream including a style segment defining region styles and one or more presentation segments, each presentation segment including presentation information and text data for at least one region; parsing the text subtitle stream into composition information, rendering information, and the text data for each region; storing the parsed composition and rendering information in a first buffer, and further storing the text data in a second buffer, the text data including one or more text strings for each region; rendering the text strings stored in the second buffer into a bitmap object for each region according to the rendering information, and storing the rendered bitmap object into a third buffer; and composing the stored bitmap object in a graphics plane for each region according to the composition information. | 48. A method for decoding a text subtitle stream downloaded from an external source, the method comprising: loading the text subtitle stream into a subtitle loading buffer, the text subtitle stream including a style segment defining region styles and one or more presentation segments, each presentation segment including presentation information and text data for at least one region; parsing the text subtitle stream into composition information, rendering information, and the text data for each region; storing the parsed composition and rendering information in a first buffer, and further storing the text data in a second buffer, the text data including one or more text strings for each region; rendering the text strings stored in the second buffer into a bitmap object for each region according to the rendering information, and storing the rendered bitmap object into a third buffer; and composing the stored bitmap object in a graphics plane for each region according to the composition information. 56. The method of claim 48 , wherein the composition information includes at least a portion of region style information specifying one of the region styles defined by the style segment. | 0.899351 |
8,495,002 | 12 | 13 | 12. The software tool of claim 1 , wherein the knowledge base building processes and the knowledge base testing processes use a modeling engine to analyze and classify corpus items. | 12. The software tool of claim 1 , wherein the knowledge base building processes and the knowledge base testing processes use a modeling engine to analyze and classify corpus items. 13. The software tool of claim 12 , wherein the modeling engine includes a natural language processing engine and a semantic modeling engine. | 0.965893 |
8,145,660 | 10 | 11 | 10. The method of claim 1 , further comprising: determining a refinement to apply to the search query; and wherein generating expanded search queries comprises generating expanded search queries according to the determined expansion type and the determined refinement. | 10. The method of claim 1 , further comprising: determining a refinement to apply to the search query; and wherein generating expanded search queries comprises generating expanded search queries according to the determined expansion type and the determined refinement. 11. The method of claim 10 , wherein the refinement is a selected one of a media type, a source, and an advertisement. | 0.931075 |
9,063,974 | 1 | 9 | 1. A machine-implemented method for processing a query, comprising: determining, by a microprocessor, that execution of the query involves a scan operation; in response to determining that execution of the query involves a scan operation, generating, by the microprocessor, a scan operation command that includes, as parameters of the scan operation command, address data that is used to identify input data to be read by a coprocessor and one or more values that are used to compare against the input data; wherein the microprocessor is separate from the coprocessor; causing, by the microprocessor, the scan operation command to be stored in memory; processing, by the coprocessor, the scan operation command by: reading the scan operation command from the memory; causing the input data to be read from a location that is indicated by the address data; performing a comparison between the input data with the one or more values; generating a result data based on the comparison; causing the result data to be stored. | 1. A machine-implemented method for processing a query, comprising: determining, by a microprocessor, that execution of the query involves a scan operation; in response to determining that execution of the query involves a scan operation, generating, by the microprocessor, a scan operation command that includes, as parameters of the scan operation command, address data that is used to identify input data to be read by a coprocessor and one or more values that are used to compare against the input data; wherein the microprocessor is separate from the coprocessor; causing, by the microprocessor, the scan operation command to be stored in memory; processing, by the coprocessor, the scan operation command by: reading the scan operation command from the memory; causing the input data to be read from a location that is indicated by the address data; performing a comparison between the input data with the one or more values; generating a result data based on the comparison; causing the result data to be stored. 9. The method of claim 1 , wherein: the coprocessor is a first coprocessor of a plurality of coprocessors that are connected in a series; causing the result data to be stored comprises causing the result data to be sent to a buffer of a second coprocessor of the plurality of coprocessors; the method further comprising: reading, by the second coprocessor, the result data from the buffer while the first coprocessor is executing a portion of the query, and based on the result data, generating, by the second coprocessor, second result data. | 0.569157 |
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. | 0.886926 |
9,753,923 | 22 | 23 | 22. The system of claim 19 , wherein determining one of the affinity scores comprises: determining an intersecting population of the users between one of the populations mapped to one of the events or topics, and another one of the populations mapped to the one of the events or topics; determining the affinity score based on the intersecting population. | 22. The system of claim 19 , wherein determining one of the affinity scores comprises: determining an intersecting population of the users between one of the populations mapped to one of the events or topics, and another one of the populations mapped to the one of the events or topics; determining the affinity score based on the intersecting population. 23. The system of claim 22 , wherein determining one of the affinity scores comprises calculating a weighted sum over the users in the intersecting population. | 0.951701 |
8,509,525 | 11 | 14 | 11. A computer system for identifying documents sharing a common underlying structure, comprising: a non-transitory computer-readable storage medium comprising executable computer program code for: detecting occurrences of a plurality of predetermined image features in a plurality of document images, wherein at least one of the plurality of predetermined image features is common among instances of a form; indexing the plurality of document images in an image index based on the detected image features; building a graph of connected nodes for the plurality of document images by searching the image index; identifying the documents sharing the common underlying structure using the graph; reproducing the common underlying structure shared by the identified documents; and generating improved images of the identified documents by overlaying the reproduced common underlying structure on document images of the identified documents; and a processor for executing the computer program code. | 11. A computer system for identifying documents sharing a common underlying structure, comprising: a non-transitory computer-readable storage medium comprising executable computer program code for: detecting occurrences of a plurality of predetermined image features in a plurality of document images, wherein at least one of the plurality of predetermined image features is common among instances of a form; indexing the plurality of document images in an image index based on the detected image features; building a graph of connected nodes for the plurality of document images by searching the image index; identifying the documents sharing the common underlying structure using the graph; reproducing the common underlying structure shared by the identified documents; and generating improved images of the identified documents by overlaying the reproduced common underlying structure on document images of the identified documents; and a processor for executing the computer program code. 14. The system of claim 11 , wherein the plurality of predetermined image features comprises one or more of the following: stable features that are at least partially invariant to one or more of the following: scale, orientation, illumination, contrast, and image quality; line segments detected in the document images; and text phrases recognized in the document images. | 0.607822 |
7,792,667 | 21 | 27 | 21. A tangible computer readable storage medium containing executable instructions which, if executed in a processing system, cause the system to perform a method for identifying a significant phrase in a document, the method comprising: reading a sequence of words from the document; determining a score for each word in the sequence based on the length of each word; comparing the score for each word in the sequence against a threshold score; indicating that the sequence of words is a significant phrase if the number of words in the sequence that have the score greater than the threshold score equals or exceeds a predetermined number and the number of words in the sequence satisfies the verbosity setting; retrieving a sentence from the document, the sentence containing the sequence of words, if the sequence of words is a significant phrase; and searching an abstract of the document to determine whether the sentence is included in the abstract. | 21. A tangible computer readable storage medium containing executable instructions which, if executed in a processing system, cause the system to perform a method for identifying a significant phrase in a document, the method comprising: reading a sequence of words from the document; determining a score for each word in the sequence based on the length of each word; comparing the score for each word in the sequence against a threshold score; indicating that the sequence of words is a significant phrase if the number of words in the sequence that have the score greater than the threshold score equals or exceeds a predetermined number and the number of words in the sequence satisfies the verbosity setting; retrieving a sentence from the document, the sentence containing the sequence of words, if the sequence of words is a significant phrase; and searching an abstract of the document to determine whether the sentence is included in the abstract. 27. The tangible computer readable storage medium according to claim 21 , wherein determining the score for each word in the sequence further includes: determining whether the word exists in a predetermined table; and retrieving the score for the word from the predetermined table. | 0.767769 |
9,407,608 | 8 | 13 | 8. A system for adjusting tuning settings based on an attribute of a client, the system comprising: a device intermediary to a client and a content server, the device configured to identify a policy for evaluating the client responsive to a first request of the client to access the content server, the policy specifying an expression comprising a clause to be evaluated by the client to identify an attribute of the client, the attribute identifying at least one of an application of the client, network data, a characteristic of a network to which the client is connected or user preferences; a server intermediary to at least the client and the content server, the server comprising tuning settings for improving performance of communications of response data from the content server to the client; wherein the device is configured to transmit, responsive to identifying the policy for evaluating the client, a second request to the client to have the client evaluate the clause and receive from the client, a response to the second request, the response comprising the attribute of the client; and wherein the server is configured to receive the attribute of the client from the device and adjust the tuning settings based on the attribute of the client to improve the performance of communications of the response data from the content server to the client. | 8. A system for adjusting tuning settings based on an attribute of a client, the system comprising: a device intermediary to a client and a content server, the device configured to identify a policy for evaluating the client responsive to a first request of the client to access the content server, the policy specifying an expression comprising a clause to be evaluated by the client to identify an attribute of the client, the attribute identifying at least one of an application of the client, network data, a characteristic of a network to which the client is connected or user preferences; a server intermediary to at least the client and the content server, the server comprising tuning settings for improving performance of communications of response data from the content server to the client; wherein the device is configured to transmit, responsive to identifying the policy for evaluating the client, a second request to the client to have the client evaluate the clause and receive from the client, a response to the second request, the response comprising the attribute of the client; and wherein the server is configured to receive the attribute of the client from the device and adjust the tuning settings based on the attribute of the client to improve the performance of communications of the response data from the content server to the client. 13. The system of claim 8 , wherein the server is configured to adjust the tuning settings based on the user preferences. | 0.785461 |
10,115,038 | 1 | 17 | 1. A computer implemented method for integrating affective based user data with traditional cognitive data in computer adaptive learning comprising: presenting to a learner one or more instructional components linked to full instructional component metadata for the adaptive learning system to reference; capturing, by a camera in communication with one or more processors, affective data in a learning profile for the learner in reaction to the one or more instructional components, wherein the affective data includes facial expression data captured while the one or more instructional components are presented, and wherein the affective data is captured at least once every three seconds; inferring emotional states of the learner, using the one or more processors to analyze the facial expression data which was captured; capturing cognitive data for the learner based on the learner's correct or incorrect answer to the one or more instructional components, wherein the cognitive data is indicative of the learner's performance in responding to the one or more instructional components; combining the cognitive data and the affective data and performing algorithmic analysis to determine the adaptive instruction based on a positive or negative adaptivity score, wherein the negative adaptivity score is based on at least one of a negative emotional state and a negative cognitive score based on incorrect answers to the one or more instructional components and indicates a need to provide an alternative learning pathway relating to the one or more instructional components, and the positive adaptivity score is based on at least one of a positive emotional state and a positive cognitive score based on correct answers to the one or more instructional components and indicates that the individual is prepared for advancement to a higher level of learning; generating a learner profile, populating the learner profile with the adaptivity score; and generating supplemental learner profile data by combining the cognitive data and affective data and comparing the supplemental learner profile data to a normed learner population data to select an adaptive instruction to provide to the learner; populating the learner profile with the cognitive score to provide a learner profile containing one or more preferred learning modalities for the learner, presenting to the learner the adaptive instruction, wherein the adaptive instruction comprises alternative remedial instructional components relating to the one or more instructional components in the event of a negative adaptivity score, and advanced instructional components in the event of a positive adaptivity score, wherein the remedial instructional components and the advanced instructional components are linked to full instructional component metadata; and wherein the step of presenting to the learner the adaptive instruction further takes into account the supplemental learner profile data, including cognitive data, affective data, the normed learner population data, and the one or more preferred learning modalities for the learner. | 1. A computer implemented method for integrating affective based user data with traditional cognitive data in computer adaptive learning comprising: presenting to a learner one or more instructional components linked to full instructional component metadata for the adaptive learning system to reference; capturing, by a camera in communication with one or more processors, affective data in a learning profile for the learner in reaction to the one or more instructional components, wherein the affective data includes facial expression data captured while the one or more instructional components are presented, and wherein the affective data is captured at least once every three seconds; inferring emotional states of the learner, using the one or more processors to analyze the facial expression data which was captured; capturing cognitive data for the learner based on the learner's correct or incorrect answer to the one or more instructional components, wherein the cognitive data is indicative of the learner's performance in responding to the one or more instructional components; combining the cognitive data and the affective data and performing algorithmic analysis to determine the adaptive instruction based on a positive or negative adaptivity score, wherein the negative adaptivity score is based on at least one of a negative emotional state and a negative cognitive score based on incorrect answers to the one or more instructional components and indicates a need to provide an alternative learning pathway relating to the one or more instructional components, and the positive adaptivity score is based on at least one of a positive emotional state and a positive cognitive score based on correct answers to the one or more instructional components and indicates that the individual is prepared for advancement to a higher level of learning; generating a learner profile, populating the learner profile with the adaptivity score; and generating supplemental learner profile data by combining the cognitive data and affective data and comparing the supplemental learner profile data to a normed learner population data to select an adaptive instruction to provide to the learner; populating the learner profile with the cognitive score to provide a learner profile containing one or more preferred learning modalities for the learner, presenting to the learner the adaptive instruction, wherein the adaptive instruction comprises alternative remedial instructional components relating to the one or more instructional components in the event of a negative adaptivity score, and advanced instructional components in the event of a positive adaptivity score, wherein the remedial instructional components and the advanced instructional components are linked to full instructional component metadata; and wherein the step of presenting to the learner the adaptive instruction further takes into account the supplemental learner profile data, including cognitive data, affective data, the normed learner population data, and the one or more preferred learning modalities for the learner. 17. The method of claim 1 , wherein the step of combining the cognitive data and the affective data and performing algorithmic analysis to determine the adaptive instruction is performed in less than five seconds. | 0.967757 |
7,543,024 | 14 | 25 | 14. A computer-implemented system for monitoring multiple files in disparate formats, the system comprising: a file type identifier module adapted to identify the format of each of a plurality of online resources, at least one of the online resources being in a first format including data in a non-strict architectural structure; a format conversion module adapted to, identify whether each of the online resources of the plurality of online resources is a non-HyperText Markup Language application, for each of the plurality of online resources from the non-HyperText Markup Language application, convert the online resource from the non-HyperText Markup Language application to a HyperText Markup Language application, for each of the online resources of the plurality of online resources, determine whether the online resource meets a minimum level of content structure to allow an Extensible Style Sheet Transform to be used to convert the online resource to the strict formatted file, convert each of the online resources that is determined as meeting the minimum level of content structure to a strict formatted file having a common format, wherein the strict formatted file is an Extensible Markup Language application, and wherein data in the format of each online resource is converted into a strict architectural structure, and convert each of the online resources that is determined as not meeting the minimum level of content structure to a strict formatted file, wherein the strict formatted file is a document object model of the online resource; after conversion to the strict formatted file, an analytic parser adapted to identify relevant data in the strict architectural structure in each strict formatted file; a resource filter adapted to determine whether the identified relevant data has been altered by comparing the identified relevant data in at least one of the strict formatted files to a most recent archived copy of the identified relevant data. | 14. A computer-implemented system for monitoring multiple files in disparate formats, the system comprising: a file type identifier module adapted to identify the format of each of a plurality of online resources, at least one of the online resources being in a first format including data in a non-strict architectural structure; a format conversion module adapted to, identify whether each of the online resources of the plurality of online resources is a non-HyperText Markup Language application, for each of the plurality of online resources from the non-HyperText Markup Language application, convert the online resource from the non-HyperText Markup Language application to a HyperText Markup Language application, for each of the online resources of the plurality of online resources, determine whether the online resource meets a minimum level of content structure to allow an Extensible Style Sheet Transform to be used to convert the online resource to the strict formatted file, convert each of the online resources that is determined as meeting the minimum level of content structure to a strict formatted file having a common format, wherein the strict formatted file is an Extensible Markup Language application, and wherein data in the format of each online resource is converted into a strict architectural structure, and convert each of the online resources that is determined as not meeting the minimum level of content structure to a strict formatted file, wherein the strict formatted file is a document object model of the online resource; after conversion to the strict formatted file, an analytic parser adapted to identify relevant data in the strict architectural structure in each strict formatted file; a resource filter adapted to determine whether the identified relevant data has been altered by comparing the identified relevant data in at least one of the strict formatted files to a most recent archived copy of the identified relevant data. 25. The system of claim 14 further comprising a monitoring module adapted to automatically update a database when the identified relevant data has changed. | 0.614428 |
8,856,730 | 1 | 2 | 1. A computing system comprising: at least one processor; and one or more storage medium having stored computer-executable instructions which, when executed by the at least one processor, implement a method of defining a layout of diagram elements, the method comprising: a computer system, which includes a processor, receiving user input, the user input comprising one or more declarative statements specifying conditional patterns based on attributes of diagram elements, the conditional patterns defining layouts of diagram elements, wherein implementation of the layouts is dependent on conditions defined in the declarative statements and one or more values of one or more of the attributes; the computer system organizing the conditional patterns as a pattern definition, wherein organizing the conditional patterns as a pattern definition comprises at least one of: combining conditional patterns together to create a higher order pattern with a previously defined pattern being included in a new pattern as a definition field, or breaking down a conditional pattern into the two or more patterns which are both applied to a same situation, but which define different aspects of a diagram; and the computer system storing the pattern definition on a computer readable medium, wherein the pattern definition is stored such that the pattern definition is retrievable by an application program that uses the pattern definition to evaluate the conditional patterns using values of attributes of one or more diagram elements, the application further being configured to display representations of the diagram elements according to the layouts when conditions for implementing the layouts are satisfied. | 1. A computing system comprising: at least one processor; and one or more storage medium having stored computer-executable instructions which, when executed by the at least one processor, implement a method of defining a layout of diagram elements, the method comprising: a computer system, which includes a processor, receiving user input, the user input comprising one or more declarative statements specifying conditional patterns based on attributes of diagram elements, the conditional patterns defining layouts of diagram elements, wherein implementation of the layouts is dependent on conditions defined in the declarative statements and one or more values of one or more of the attributes; the computer system organizing the conditional patterns as a pattern definition, wherein organizing the conditional patterns as a pattern definition comprises at least one of: combining conditional patterns together to create a higher order pattern with a previously defined pattern being included in a new pattern as a definition field, or breaking down a conditional pattern into the two or more patterns which are both applied to a same situation, but which define different aspects of a diagram; and the computer system storing the pattern definition on a computer readable medium, wherein the pattern definition is stored such that the pattern definition is retrievable by an application program that uses the pattern definition to evaluate the conditional patterns using values of attributes of one or more diagram elements, the application further being configured to display representations of the diagram elements according to the layouts when conditions for implementing the layouts are satisfied. 2. The system of claim 1 , wherein storing the pattern definition on a computer readable medium comprises storing the pattern definition in a hierarchy of pattern definitions such that the pattern definition is used to implement diagram element layouts in the absence of other layout configuration information. | 0.501608 |
9,361,337 | 8 | 9 | 8. The server of claim 7 wherein said web request controller is structured to receive a request from said client computer for results of said client computer's query and check said results cache for the requested results, and if said requested results are found in said results cache, packetizing said requested results in packets capable of being sent over said wide area network and sending said packets to said client computer. | 8. The server of claim 7 wherein said web request controller is structured to receive a request from said client computer for results of said client computer's query and check said results cache for the requested results, and if said requested results are found in said results cache, packetizing said requested results in packets capable of being sent over said wide area network and sending said packets to said client computer. 9. The server of claim 8 wherein said web request controller is structured to convert said results to XML format before sending the results back to said client computer who sent said query. | 0.941558 |
7,496,895 | 7 | 19 | 7. In a computational device with a block diagram environment, a method of debugging a multi-domain block diagram, the method comprising: providing a plurality of debuggable objects (DBOs) associated with entities found in a block diagram model and its solver, the block diagram model including entities from a plurality of types of modeling domains, the modeling domains being of different types; providing a unified debugger, the unified debugger integrating the plurality of DBOS that are associated with entities from different types of modeling domains into a common diagnostic environment, the common diagnostic environment displaying a plurality of views of the block diagram to a user consistent with a modeling domain of an entity being executed; executing a first entity from a first modeling domain in the block diagram; displaying a first view consistent with the first modeling domain in the common diagnostic environment, the displaying of the first view based on information referenced by a first DBO associated with the first entity; executing a second entity from a second modeling domain in the block diagram; replacing the first view with a second view consistent with the second modeling domain in the common diagnostic environment, the view changing between views of at least two of a time-based block diagram, a statechart, a data flow diagram, a discrete event model and compiled code; and displaying the second view based on information referenced by a second DBO associated with the second executing entity. | 7. In a computational device with a block diagram environment, a method of debugging a multi-domain block diagram, the method comprising: providing a plurality of debuggable objects (DBOs) associated with entities found in a block diagram model and its solver, the block diagram model including entities from a plurality of types of modeling domains, the modeling domains being of different types; providing a unified debugger, the unified debugger integrating the plurality of DBOS that are associated with entities from different types of modeling domains into a common diagnostic environment, the common diagnostic environment displaying a plurality of views of the block diagram to a user consistent with a modeling domain of an entity being executed; executing a first entity from a first modeling domain in the block diagram; displaying a first view consistent with the first modeling domain in the common diagnostic environment, the displaying of the first view based on information referenced by a first DBO associated with the first entity; executing a second entity from a second modeling domain in the block diagram; replacing the first view with a second view consistent with the second modeling domain in the common diagnostic environment, the view changing between views of at least two of a time-based block diagram, a statechart, a data flow diagram, a discrete event model and compiled code; and displaying the second view based on information referenced by a second DBO associated with the second executing entity. 19. The method of claim 7 , further comprising: exposing a collection of data in an entity being executed in the block diagram to a user, the collection of data being sent from a DBO associated with the entity to the unified debugger in response to a request, the unified debugger generating a display of the data to the user. | 0.73366 |
8,070,774 | 25 | 27 | 25. An anchor assembly comprising: an anchor body; an anchor head including a first arm and a second arm extending substantially parallel to one another and away from the anchor body, the first arm and the second arm separated by a gap; a saddle having a longitudinal bore, a transverse bore which intersects the longitudinal bore, and a slot adapted to receive a member wherein the slot bisects a proximal end to the saddle into a first section and a second section; a shaft connected to the first arm and the second arm across the gap wherein said transverse bore of said saddle is mounted about said shaft such that the saddle can move relative to the anchor head; a fastener received in said longitudinal bore of said saddle and adapted to lock the member in said slot of said saddle; and wherein said first section and second section are roof-shaped to prevent splaying apart of the first section and second section when said fastener locks the member in said slot of saddle. | 25. An anchor assembly comprising: an anchor body; an anchor head including a first arm and a second arm extending substantially parallel to one another and away from the anchor body, the first arm and the second arm separated by a gap; a saddle having a longitudinal bore, a transverse bore which intersects the longitudinal bore, and a slot adapted to receive a member wherein the slot bisects a proximal end to the saddle into a first section and a second section; a shaft connected to the first arm and the second arm across the gap wherein said transverse bore of said saddle is mounted about said shaft such that the saddle can move relative to the anchor head; a fastener received in said longitudinal bore of said saddle and adapted to lock the member in said slot of said saddle; and wherein said first section and second section are roof-shaped to prevent splaying apart of the first section and second section when said fastener locks the member in said slot of saddle. 27. The anchor assembly of claim 25 , wherein the first section and second section are reinforced to prevent splaying apart of the first section and second section when said fastener locks the member in said slot of said saddle. | 0.731132 |
9,679,256 | 31 | 40 | 31. One or more non-transitory computer-readable media containing instructions which, as a result of execution by a computing device, configure the computing device so as to cause the computing device to automatically evaluate the linguistic quality of free-response text answers submitted by students in response to examination prompts using discriminative preference ranking of predetermined linguistic text features, said configured computing device generating a trained model weight vector for subsequent use in automatically evaluating said free-response text answers by: accessing a plurality of training linguistic vectors (x 1 , x 2 , x 3 , . . . x n ) each training linguistic vector comprising a plurality of numerical values representing predetermined linguistic features of text comprising sentences within a training text, at least some of said predetermined linguistic features representing at least one of lexical, part-of-speech or parsing of words within said sentences; accessing, for each of a plurality of predetermined pairs of said training linguistic vectors (x i , x j ), predetermined ranking data (r i , r j ) that defines which one of the pair of training linguistic vectors (x i , x j ) is representative of a better training script; accessing an initial weight vector (w i ) comprising a plurality of numerical weights corresponding to the plurality of numerical values in the training vectors; generating a plurality of pairwise difference training vectors (x j −x i ) each difference training vector being calculated as a difference between a pair of said training linguistic vectors ranked by said ranking data; and performing an iterative process to adapt said initial weight vector (w i ) to a trained model weight vector (w m ) by: i) calculating a dot product between a current weight vector and each pairwise difference training vector to generate a respective scalar value for each pairwise difference training vector; ii) determining, for each pairwise difference training vector, if the current weight vector misclassified the pairwise difference training vector in dependence upon a comparison result obtained by comparing the scalar value for the pairwise difference training vector with a predetermined threshold; iii) generating an aggregate vector (ã) by summing the pairwise difference training vectors that said determining step determines are misclassified and normalizing the summed result with a current timing factor; iv) calculating a new weight vector by arithmetically combining numerical values of the current weight vector with respectively, corresponding numerical values of the generated aggregate vector; and v) repeating steps i) through iv) until the current timing factor reaches a predetermined condition, whereupon the then current weight vector becomes said trained model weight vector (w m ); and subsequently using said trained model weight vector to automatically evaluate the linguistic quality of each of plural input free-text answers submitted for evaluation by: generating a linguistic vector for an input free-text answer that is to be evaluated; calculating a dot product between the trained model weight vector and the linguistic vector for the input free-text answer that is to be evaluated to generate a scalar value for the input free-text answer; and outputting an evaluation of the input free-text answer using the scalar value generated for the input free-text answer. | 31. One or more non-transitory computer-readable media containing instructions which, as a result of execution by a computing device, configure the computing device so as to cause the computing device to automatically evaluate the linguistic quality of free-response text answers submitted by students in response to examination prompts using discriminative preference ranking of predetermined linguistic text features, said configured computing device generating a trained model weight vector for subsequent use in automatically evaluating said free-response text answers by: accessing a plurality of training linguistic vectors (x 1 , x 2 , x 3 , . . . x n ) each training linguistic vector comprising a plurality of numerical values representing predetermined linguistic features of text comprising sentences within a training text, at least some of said predetermined linguistic features representing at least one of lexical, part-of-speech or parsing of words within said sentences; accessing, for each of a plurality of predetermined pairs of said training linguistic vectors (x i , x j ), predetermined ranking data (r i , r j ) that defines which one of the pair of training linguistic vectors (x i , x j ) is representative of a better training script; accessing an initial weight vector (w i ) comprising a plurality of numerical weights corresponding to the plurality of numerical values in the training vectors; generating a plurality of pairwise difference training vectors (x j −x i ) each difference training vector being calculated as a difference between a pair of said training linguistic vectors ranked by said ranking data; and performing an iterative process to adapt said initial weight vector (w i ) to a trained model weight vector (w m ) by: i) calculating a dot product between a current weight vector and each pairwise difference training vector to generate a respective scalar value for each pairwise difference training vector; ii) determining, for each pairwise difference training vector, if the current weight vector misclassified the pairwise difference training vector in dependence upon a comparison result obtained by comparing the scalar value for the pairwise difference training vector with a predetermined threshold; iii) generating an aggregate vector (ã) by summing the pairwise difference training vectors that said determining step determines are misclassified and normalizing the summed result with a current timing factor; iv) calculating a new weight vector by arithmetically combining numerical values of the current weight vector with respectively, corresponding numerical values of the generated aggregate vector; and v) repeating steps i) through iv) until the current timing factor reaches a predetermined condition, whereupon the then current weight vector becomes said trained model weight vector (w m ); and subsequently using said trained model weight vector to automatically evaluate the linguistic quality of each of plural input free-text answers submitted for evaluation by: generating a linguistic vector for an input free-text answer that is to be evaluated; calculating a dot product between the trained model weight vector and the linguistic vector for the input free-text answer that is to be evaluated to generate a scalar value for the input free-text answer; and outputting an evaluation of the input free-text answer using the scalar value generated for the input free-text answer. 40. The computer-readable media of claim 31 , wherein the instructions are further to cause the computing device to: generate one or more numerical linguistic vectors by measuring one or more feature types for one or more person-generated input texts; determine one or more script evaluations for the one or more numerical linguistic vectors through computation of a dot product of the weight vector and the numerical linguistic vectors; and output the determined one or more script evaluations. | 0.501008 |
9,235,566 | 13 | 16 | 13. A computer-implemented method for predictively looking up words in an online dictionary, the method comprising: executing, by a computer processor, a module that instructs the processor to respond to each new user input of a text string into a predetermined field by generating and outputting a respective initial presentation of a list of words, wherein: the list includes a first section and a second section; each of the words of the first section is included in the list based on at least a partial definition that is user-selected and at least one of (a) the respective word being a user-selected part of speech and (b) the text string being at least one of: an antonym of the respective word, a hyponym of the respective word, an example of the respective word, and a meronym of the respective word; each of the words of the second section is included in the list based on alphabetical proximity to the input text string; and the module instructs that, for each of the initial presentations of the list, the first section includes more than one word and the second section includes only one word that is alphabetically closest to the text string. | 13. A computer-implemented method for predictively looking up words in an online dictionary, the method comprising: executing, by a computer processor, a module that instructs the processor to respond to each new user input of a text string into a predetermined field by generating and outputting a respective initial presentation of a list of words, wherein: the list includes a first section and a second section; each of the words of the first section is included in the list based on at least a partial definition that is user-selected and at least one of (a) the respective word being a user-selected part of speech and (b) the text string being at least one of: an antonym of the respective word, a hyponym of the respective word, an example of the respective word, and a meronym of the respective word; each of the words of the second section is included in the list based on alphabetical proximity to the input text string; and the module instructs that, for each of the initial presentations of the list, the first section includes more than one word and the second section includes only one word that is alphabetically closest to the text string. 16. The method of claim 13 , wherein the respective word is included in the list based on the text string being an antonym of the respective word. | 0.847917 |
8,484,233 | 1 | 6 | 1. In a computing environment, a system comprising: one or more processors; a unified user interface implemented on the one or more processors and configured to compose queries via a plurality of mechanisms, including a facet-based user interface by which users select filtering criteria corresponding to facets, and a logic-based user interface by which users logically combine object data, wherein the logic-based user interface is query-language independent, and wherein an object includes a set of properties, and wherein the facet-based user interface creates value clusters for each property of the object, the value clusters used by the facet-based user interface to optimize a query; and query logic configured to process the filtering criteria into a query by logically AND-ing selected facets if the users select the filtering criteria corresponding to facets via the facet-based user interface, or configured to logically combine the object data into a query if the users logically combine the object data via the logic-based user interface, or configured to both process the filtering criteria into a query and logically combine the object data into a query if the users switch between both the logic-based user interface and the facet-based user interface of the unified user interface during query composition. | 1. In a computing environment, a system comprising: one or more processors; a unified user interface implemented on the one or more processors and configured to compose queries via a plurality of mechanisms, including a facet-based user interface by which users select filtering criteria corresponding to facets, and a logic-based user interface by which users logically combine object data, wherein the logic-based user interface is query-language independent, and wherein an object includes a set of properties, and wherein the facet-based user interface creates value clusters for each property of the object, the value clusters used by the facet-based user interface to optimize a query; and query logic configured to process the filtering criteria into a query by logically AND-ing selected facets if the users select the filtering criteria corresponding to facets via the facet-based user interface, or configured to logically combine the object data into a query if the users logically combine the object data via the logic-based user interface, or configured to both process the filtering criteria into a query and logically combine the object data into a query if the users switch between both the logic-based user interface and the facet-based user interface of the unified user interface during query composition. 6. The system of claim 1 wherein the queries composed by the logic-based query user interface that is query-language independent are automatically translated to SPARQL. | 0.751479 |
8,670,974 | 11 | 12 | 11. The non-transitory computer-readable medium of claim 10 , further comprising: performing a verification operation on a candidate translation pair from the set of candidate translation pairs; based, at least in part, on results of the verification operation, determining whether to classify the candidate translation pair as a translation pair. | 11. The non-transitory computer-readable medium of claim 10 , further comprising: performing a verification operation on a candidate translation pair from the set of candidate translation pairs; based, at least in part, on results of the verification operation, determining whether to classify the candidate translation pair as a translation pair. 12. The non-transitory computer-readable medium of claim 11 , further comprising determining that no candidate translation pairs should be classified as translation pairs in response to more than a threshold number of candidate translation pairs not passing the verification operation. | 0.887263 |
8,175,865 | 1 | 7 | 1. A method of text script generation for a corpus-based text-to-speech system configured with a computing device for text script searching and processing and a memory device for corpus storage, comprising: (a) searching in a source corpus being stored in said memory device and having L sentences, selecting N sentences with a best integrated efficiency as N best cases, and setting iteration k to be 1, k, L and N being natural numbers, N≦L; (b) for each case n of the N best cases, 1≦n≦N, searching in said source corpus and selecting by the computing device, M k+1 best sentences with the best integrated efficiency from the unselected sentences in said source corpus, 1≦M k+1 ≦L; (c) searching in said source corpus and keeping N best cases out of the total unselected sentences for next iteration, and increasing iteration k by 1; and (d) if a termination criterion being reached, setting the best case in the N traced cases as the text script, otherwise, returning to step (b); wherein said best integrated efficiency depends on a function of combining the covering rate efficiency of unit types, the hit rate efficiency of unit types, and the text script size. | 1. A method of text script generation for a corpus-based text-to-speech system configured with a computing device for text script searching and processing and a memory device for corpus storage, comprising: (a) searching in a source corpus being stored in said memory device and having L sentences, selecting N sentences with a best integrated efficiency as N best cases, and setting iteration k to be 1, k, L and N being natural numbers, N≦L; (b) for each case n of the N best cases, 1≦n≦N, searching in said source corpus and selecting by the computing device, M k+1 best sentences with the best integrated efficiency from the unselected sentences in said source corpus, 1≦M k+1 ≦L; (c) searching in said source corpus and keeping N best cases out of the total unselected sentences for next iteration, and increasing iteration k by 1; and (d) if a termination criterion being reached, setting the best case in the N traced cases as the text script, otherwise, returning to step (b); wherein said best integrated efficiency depends on a function of combining the covering rate efficiency of unit types, the hit rate efficiency of unit types, and the text script size. 7. The method of text script generation for a corpus-based text-to-speech system according to claim 1 , wherein said hit rate efficiency of unit types is of the form η H = X ′ X X S , X is the set of all unit instances in said source corpus, X S is the set of all unit instances in the selected text script, and X′ is the set of all unit instances gathered by the set of unit types covered by X S . | 0.501211 |
8,447,766 | 8 | 9 | 8. The method of claim 2 , wherein ranking the plurality of results comprises, for each group: calculating a relevance score for each document portion placed in said group; and defining a group relevance score by summing the relevance scores of the respective document portions in said group; said ranking further comprising sorting the group relevance scores. | 8. The method of claim 2 , wherein ranking the plurality of results comprises, for each group: calculating a relevance score for each document portion placed in said group; and defining a group relevance score by summing the relevance scores of the respective document portions in said group; said ranking further comprising sorting the group relevance scores. 9. The method of claim 8 , wherein the step of defining the group relevance score further comprises: further summing the relevance scores of the document portions in all other groups; and deducting said further sum from the sum of the relevance scores of the respective document portions in said group. | 0.890738 |
9,191,510 | 1 | 8 | 1. A method for analyzing electronic customer communication data and generating behavioral assessment data, the method comprising the acts of: receiving electronic customer communication data of two or more types by a contact center, wherein at least one of the two or more types of electronic customer communication data comprises social media data, update status, media feed, social media review, or a social media data stream; identifying a customer associated with the electronic customer communication data received by the contact center; analyzing the electronic customer communication data by applying a predetermined linguistic-based psychological behavioral model to the electronic customer communication data for that identified customer; generating behavioral assessment data by the contact center based on said analyzing, the behavioral assessment data providing a personality type for the analyzed electronic customer communication data for that identified customer; and displaying instructions to a contact center agent via a reporting engine, wherein the instructions are based on the generated behavioral assessment data. | 1. A method for analyzing electronic customer communication data and generating behavioral assessment data, the method comprising the acts of: receiving electronic customer communication data of two or more types by a contact center, wherein at least one of the two or more types of electronic customer communication data comprises social media data, update status, media feed, social media review, or a social media data stream; identifying a customer associated with the electronic customer communication data received by the contact center; analyzing the electronic customer communication data by applying a predetermined linguistic-based psychological behavioral model to the electronic customer communication data for that identified customer; generating behavioral assessment data by the contact center based on said analyzing, the behavioral assessment data providing a personality type for the analyzed electronic customer communication data for that identified customer; and displaying instructions to a contact center agent via a reporting engine, wherein the instructions are based on the generated behavioral assessment data. 8. The method of claim 1 , further comprising storing the behavioral assessment data, wherein the behavioral assessment data includes identifying indicia associated with a customer. | 0.938056 |
5,555,343 | 1 | 20 | 1. A text processor for a text-to-speech converter comprising: a parser for accepting a text stream, for parsing the text stream to detect an unspoken character having a first characteristic, an unspoken character having a second characteristic, and spoken characters, and for not altering the spoken characters in the text stream; a text generator, responsive to detection of an unspoken character having the first characteristic, for generating a pre-designated text sequence, and for replacing, in the text stream, said unspoken character having said first characteristic with said pre-designated text sequence; and a speech command generator, responsive to detection of an unspoken character having a second characteristic, for generating pre-designated speech commands. | 1. A text processor for a text-to-speech converter comprising: a parser for accepting a text stream, for parsing the text stream to detect an unspoken character having a first characteristic, an unspoken character having a second characteristic, and spoken characters, and for not altering the spoken characters in the text stream; a text generator, responsive to detection of an unspoken character having the first characteristic, for generating a pre-designated text sequence, and for replacing, in the text stream, said unspoken character having said first characteristic with said pre-designated text sequence; and a speech command generator, responsive to detection of an unspoken character having a second characteristic, for generating pre-designated speech commands. 20. A text processor according to claim 1, further comprising a text-to-speech interface which receives spoken text parsed by said parser, text generated by said text generator, and speech commands generated by said speech command generator. | 0.821481 |
10,127,901 | 11 | 12 | 11. A computer storage device, having computer-executable instructions that, when executed by at least one processor, perform a method for converting text-to-speech, the method comprising: receiving text input into a plurality of first level recurrent neural networks; determining, by a first recurrent neural network in the plurality of first level recurrent neural networks, one or more properties of the text input from the group consisting of: part-of-speech properties, phonemes, linguistic prosody properties, contextual properties, and semantic properties; determining, by a second recurrent neural network in the plurality of first level recurrent neural networks, one or more properties of the text input from the group consisting of: part-of-speech properties, phonemes, linguistic prosody properties, contextual properties, and semantic properties, wherein the determined one or properties by the second recurrent neural network is different from the determined one or more properties by the first recurrent neural network; receiving, by a recurrent neural network in a second level, the determined properties from the first recurrent neural network in the plurality of first level recurrent neural networks and the second recurrent neural networks in the plurality of first level recurrent neural networks; determining by the recurrent neural network in the second level, phonetic properties for the text input based on the properties received from the first recurrent neural network in the plurality of first level recurrent neural networks and the second neural network in the plurality of first level recurrent neural networks, wherein the recurrent neural network in the second level is different from the first recurrent neural network in the plurality of first level recurrent neural networks and the second recurrent neural network in the plurality of first level recurrent neural networks; and based on the determined phonetic properties, generating a generation sequence for synthetization by an audio synthesizer. | 11. A computer storage device, having computer-executable instructions that, when executed by at least one processor, perform a method for converting text-to-speech, the method comprising: receiving text input into a plurality of first level recurrent neural networks; determining, by a first recurrent neural network in the plurality of first level recurrent neural networks, one or more properties of the text input from the group consisting of: part-of-speech properties, phonemes, linguistic prosody properties, contextual properties, and semantic properties; determining, by a second recurrent neural network in the plurality of first level recurrent neural networks, one or more properties of the text input from the group consisting of: part-of-speech properties, phonemes, linguistic prosody properties, contextual properties, and semantic properties, wherein the determined one or properties by the second recurrent neural network is different from the determined one or more properties by the first recurrent neural network; receiving, by a recurrent neural network in a second level, the determined properties from the first recurrent neural network in the plurality of first level recurrent neural networks and the second recurrent neural networks in the plurality of first level recurrent neural networks; determining by the recurrent neural network in the second level, phonetic properties for the text input based on the properties received from the first recurrent neural network in the plurality of first level recurrent neural networks and the second neural network in the plurality of first level recurrent neural networks, wherein the recurrent neural network in the second level is different from the first recurrent neural network in the plurality of first level recurrent neural networks and the second recurrent neural network in the plurality of first level recurrent neural networks; and based on the determined phonetic properties, generating a generation sequence for synthetization by an audio synthesizer. 12. The computer storage device of claim 11 , wherein the one or more properties received are the part-of-speech properties and phonemes. | 0.62973 |
7,783,639 | 1 | 7 | 1. A method performed by a device, the method comprising: identifying, by a processor of the device, a plurality of documents, where a first one of the identified documents is linked by a second one of the identified documents and the second document is one of a plurality of documents in an affiliated set of documents; calculating, by the processor, a first value for each document in the affiliated set of documents based on a ranking score of the document and a number of outbound links from the document; calculating, by the processor, a second value as a maximum of the first values for the documents in the affiliated set of documents; assigning, by the processor, a ranking score to the first document based the second value, where assigning the ranking score includes: determining whether the documents in the affiliated set of documents are weakly affiliated or strongly affiliated, and setting the amount that the second document contributes to the ranking score of the first document as a function that acts as a summation operator over the affiliated set of documents when the affiliated set is weakly affiliated and as a maximum operator over the affiliated set of documents when the affiliated set is strongly affiliated, where the function is defined as:
(CONTRIB(D 1 ) a +CONTRIB(D 2 ) a + . . . +CONTRIB(D k ) a ) 1/a , where CONTRIB for document D k represents an individual ranking score contribution for document k in the affiliated set, and a is defined as 1 ⅇ + ( 1 - ⅇ ) γ , where e is a constant and γ represents a continuous measure of the affiliation of the documents in the affiliated set; and storing, by the processor, the ranking score. | 1. A method performed by a device, the method comprising: identifying, by a processor of the device, a plurality of documents, where a first one of the identified documents is linked by a second one of the identified documents and the second document is one of a plurality of documents in an affiliated set of documents; calculating, by the processor, a first value for each document in the affiliated set of documents based on a ranking score of the document and a number of outbound links from the document; calculating, by the processor, a second value as a maximum of the first values for the documents in the affiliated set of documents; assigning, by the processor, a ranking score to the first document based the second value, where assigning the ranking score includes: determining whether the documents in the affiliated set of documents are weakly affiliated or strongly affiliated, and setting the amount that the second document contributes to the ranking score of the first document as a function that acts as a summation operator over the affiliated set of documents when the affiliated set is weakly affiliated and as a maximum operator over the affiliated set of documents when the affiliated set is strongly affiliated, where the function is defined as:
(CONTRIB(D 1 ) a +CONTRIB(D 2 ) a + . . . +CONTRIB(D k ) a ) 1/a , where CONTRIB for document D k represents an individual ranking score contribution for document k in the affiliated set, and a is defined as 1 ⅇ + ( 1 - ⅇ ) γ , where e is a constant and γ represents a continuous measure of the affiliation of the documents in the affiliated set; and storing, by the processor, the ranking score. 7. The method of claim 1 , further comprising: identifying affiliation among the documents in the affiliated set of documents based on traffic patterns between the documents. | 0.809628 |
8,374,859 | 1 | 4 | 1. An automatic answering device configured to answer automatically to a user utterance, the automatic answering device comprising: an input unit configured to accept a user utterance; and an output unit configured to output a reply sentence in a form recognizable by a user, in response to the user utterance accepted by the input unit, wherein the reply sentence is determined based on a conversation scenario which is a set of inputted sentences and reply sentences, the inputted sentences each corresponding to a user utterance assumed to be uttered by the user, the reply sentences each being a reply from the automatic answering device to the inputted sentence, and data of the conversation scenario have a data structure that enables the inputted sentences and the reply sentences to be expressed in a state transition diagram in which each of the inputted sentences is defined as a morphism and the reply sentence corresponding to the inputted sentence is defined as an object. | 1. An automatic answering device configured to answer automatically to a user utterance, the automatic answering device comprising: an input unit configured to accept a user utterance; and an output unit configured to output a reply sentence in a form recognizable by a user, in response to the user utterance accepted by the input unit, wherein the reply sentence is determined based on a conversation scenario which is a set of inputted sentences and reply sentences, the inputted sentences each corresponding to a user utterance assumed to be uttered by the user, the reply sentences each being a reply from the automatic answering device to the inputted sentence, and data of the conversation scenario have a data structure that enables the inputted sentences and the reply sentences to be expressed in a state transition diagram in which each of the inputted sentences is defined as a morphism and the reply sentence corresponding to the inputted sentence is defined as an object. 4. The automatic answering device according to claim 1 , wherein the conversation scenario includes a scenario which describes a composition of a plurality of morphisms as a single morphism, and in the scenario, a reply sentence corresponding to a last morphism of the plurality of morphisms is identical to a reply sentence corresponding to the single morphism formed of the composition of the plurality of morphisms. | 0.785641 |
8,983,832 | 9 | 16 | 9. A system for enhancing a speech sound, said system comprising: a feature detector configured to: identify a first consonant-vowel (CV) speech sound from among a plurality of CV sounds; identify a second CV speech sound, that is different than the first CV speech sound, from among the plurality of CV sounds; locate, in a speech signal, a first feature that at least partially encodes the first speech sound, wherein the first feature includes a first time value and a first frequency value that together locate the first feature within the first speech sound; locate a second feature within the second speech sound, the second feature at least partially encoding the second speech sound, wherein the second feature includes a second time value and a second frequency value that together locate the second feature within the second speech sound and that are different than the first time value and the first frequency value, respectively; a speech enhancer configured to enhance said speech signal by modifying, based on the first time value and the first frequency value, a contribution of the first feature to the first speech sound, and modifying, based on the second time value and the second frequency value, a contribution of the second feature to the second speech sound based on the second time value and the second frequency value; and an output to provide the enhanced speech signal to a listener. | 9. A system for enhancing a speech sound, said system comprising: a feature detector configured to: identify a first consonant-vowel (CV) speech sound from among a plurality of CV sounds; identify a second CV speech sound, that is different than the first CV speech sound, from among the plurality of CV sounds; locate, in a speech signal, a first feature that at least partially encodes the first speech sound, wherein the first feature includes a first time value and a first frequency value that together locate the first feature within the first speech sound; locate a second feature within the second speech sound, the second feature at least partially encoding the second speech sound, wherein the second feature includes a second time value and a second frequency value that together locate the second feature within the second speech sound and that are different than the first time value and the first frequency value, respectively; a speech enhancer configured to enhance said speech signal by modifying, based on the first time value and the first frequency value, a contribution of the first feature to the first speech sound, and modifying, based on the second time value and the second frequency value, a contribution of the second feature to the second speech sound based on the second time value and the second frequency value; and an output to provide the enhanced speech signal to a listener. 16. The system of claim 9 , wherein each of the first speech sound and the second speech sound comprises at least one of /pa, ta, ka, ba, da, ga, fa, θa, sa, ∫a, δa, va, ca/. | 0.881633 |
8,972,372 | 1 | 10 | 1. A computer implemented method of providing search results, the method comprising: receiving a first specification that comprises an input-output pair including a first data entity and a second data entity; for each module of program code of a plurality of modules of program code, supplying to a constraint solver one or more input-output constraints based on the input-output pair of the first specification and one or more code constraints based on the module of program code; receiving from the constraint solver, for each module of program code, a result indicating whether the code constraints based on the module of program code are satisfiable with the input-output constraints; and generating search results referencing one or more modules of program code having a positive result from the constraint solver and providing the search results to a user. | 1. A computer implemented method of providing search results, the method comprising: receiving a first specification that comprises an input-output pair including a first data entity and a second data entity; for each module of program code of a plurality of modules of program code, supplying to a constraint solver one or more input-output constraints based on the input-output pair of the first specification and one or more code constraints based on the module of program code; receiving from the constraint solver, for each module of program code, a result indicating whether the code constraints based on the module of program code are satisfiable with the input-output constraints; and generating search results referencing one or more modules of program code having a positive result from the constraint solver and providing the search results to a user. 10. The method of claim 1 , wherein the first data entity is an extensible markup language (XML) file type and the second data entity is a Structure Query Language (SQL) file type. | 0.867061 |
7,546,525 | 7 | 8 | 7. The system of claim 6 , further comprising an undo module that enables undoing a performed command and adding the undone command to a redo map, the redo map facilitates redoing the undone command by selecting the command from the redo map. | 7. The system of claim 6 , further comprising an undo module that enables undoing a performed command and adding the undone command to a redo map, the redo map facilitates redoing the undone command by selecting the command from the redo map. 8. The system of claim 7 , the undo module enables undoing a performed commands and redoing undone commands in a non-linear fashion. | 0.952 |
10,102,454 | 1 | 6 | 1. A computer-implemented method comprising: utilizing two or more classifiers to calculate, for an input image, probability scores for a plurality of classes based on visual information extracted from the input image and semantic relationships in a classification hierarchy, wherein each of the two or more classifiers is associated with a given one of two or more levels in the classification hierarchy with each level in the classification hierarchy comprising a subset of the plurality of classes; classifying the input image based on the calculated probability scores; training the two or more classifiers to calculate probability scores for respective subset of the plurality of classes; and performing label inference to refine classification probabilities in the two or more classifiers based on semantic relationships in the classification hierarchy; wherein training the two or more classifiers and performing label inference comprises at least one of: taking as input a graph structure having initial values for nodes corresponding to classification probabilities in the two or more classifier and outputting the graph structure with modified values for the nodes; utilizing a multi-task learning based loss function that jointly optimizes classifiers associated with each of the two or more levels in the classification hierarchy; and utilizing a random walk process that smooths classification probabilities over two or more classes in a same semantic path in the classification hierarchy. | 1. A computer-implemented method comprising: utilizing two or more classifiers to calculate, for an input image, probability scores for a plurality of classes based on visual information extracted from the input image and semantic relationships in a classification hierarchy, wherein each of the two or more classifiers is associated with a given one of two or more levels in the classification hierarchy with each level in the classification hierarchy comprising a subset of the plurality of classes; classifying the input image based on the calculated probability scores; training the two or more classifiers to calculate probability scores for respective subset of the plurality of classes; and performing label inference to refine classification probabilities in the two or more classifiers based on semantic relationships in the classification hierarchy; wherein training the two or more classifiers and performing label inference comprises at least one of: taking as input a graph structure having initial values for nodes corresponding to classification probabilities in the two or more classifier and outputting the graph structure with modified values for the nodes; utilizing a multi-task learning based loss function that jointly optimizes classifiers associated with each of the two or more levels in the classification hierarchy; and utilizing a random walk process that smooths classification probabilities over two or more classes in a same semantic path in the classification hierarchy. 6. The method of claim 1 , wherein the multi-task learning based loss function trains the two or more classifiers such that misclassification of the input image based on the calculated probability scores falls within a semantically-related category of classes for a correct classification of the input image. | 0.802564 |
9,547,687 | 1 | 2 | 1. A computer-implemented method, the method comprising: obtaining data from a user interface, the data including a first set of fields and corresponding values; receiving a first executable statement, the first executable statement referencing a second set of fields in a dataset, the first executable statement having instructions to cause the query processor to perform operations on data in the dataset; generating a second executable statement based on the first set of fields and the first executable statement, comprising: determining a mapping between the first set of fields and the second set of fields, comprising: identifying a first data type of a first field in the first set of fields, identifying a second data type of a second field in the second set of fields, identifying a conversion command to convert from the first data type to the second data type, and adding the conversion command to the second executable statement, specifying a derived dataset using the corresponding values and the mapping, and generating instructions to cause the query processor to perform the operations on the derived dataset; and sending the second executable statement to the query processor. | 1. A computer-implemented method, the method comprising: obtaining data from a user interface, the data including a first set of fields and corresponding values; receiving a first executable statement, the first executable statement referencing a second set of fields in a dataset, the first executable statement having instructions to cause the query processor to perform operations on data in the dataset; generating a second executable statement based on the first set of fields and the first executable statement, comprising: determining a mapping between the first set of fields and the second set of fields, comprising: identifying a first data type of a first field in the first set of fields, identifying a second data type of a second field in the second set of fields, identifying a conversion command to convert from the first data type to the second data type, and adding the conversion command to the second executable statement, specifying a derived dataset using the corresponding values and the mapping, and generating instructions to cause the query processor to perform the operations on the derived dataset; and sending the second executable statement to the query processor. 2. The method of claim 1 , further comprising: receiving result data from the query processor associated with the execution of the operations in the second executable statement; determining that the data from the user interface is invalid based on the result data. | 0.625 |
8,972,372 | 21 | 26 | 21. A computer implemented method of providing search results, comprising: identifying a plurality of documents that are indexed according to a first scheme and associated with a first set of information; generating a second scheme that associates, based on predefined mapping information, the first set of information with a second set of information; indexing the plurality of documents according to the second scheme and storing the documents in a repository according to the second scheme; in response to receiving a specification that comprises an input-output pair including a first data entity and a second data entity, identifying one or more modules of program code, within the plurality of documents, that have code constraints specified by the second scheme that are satisfiable by a constraint solver with one or more input-output constraints based on the input-output pair; and generating search results referencing the one or more modules of program code that are satisfiable by the constraint solver with the input-output constraints and providing the search results to a user. | 21. A computer implemented method of providing search results, comprising: identifying a plurality of documents that are indexed according to a first scheme and associated with a first set of information; generating a second scheme that associates, based on predefined mapping information, the first set of information with a second set of information; indexing the plurality of documents according to the second scheme and storing the documents in a repository according to the second scheme; in response to receiving a specification that comprises an input-output pair including a first data entity and a second data entity, identifying one or more modules of program code, within the plurality of documents, that have code constraints specified by the second scheme that are satisfiable by a constraint solver with one or more input-output constraints based on the input-output pair; and generating search results referencing the one or more modules of program code that are satisfiable by the constraint solver with the input-output constraints and providing the search results to a user. 26. The method of claim 21 , wherein the first scheme is adapted to index documents according to a plurality of keywords associated with the one or more documents and the second scheme is adapted to index documents using an information hierarchy with a plurality of specifications, document indices, and lexicons for classifying details associated with an intended function of source code in the one or more documents. | 0.532438 |
8,725,771 | 1 | 5 | 1. A computer-implemented method for searching through a corpus, the corpus comprising a plurality of documents, and the method comprising: using a computer to parse a text of each of the plurality of documents into sentences; using the computer to identify named entities within the sentences; using the computer to identify semantic pairs in the documents, wherein each semantic pair comprises two named entities appearing together in a respective one of the sentences; using the computer to receive a search request, the search request including at least one name of an entity of interest; using the computer to provide at least one search result in response to the search request wherein the at least one search result includes an identification of a document from the corpus and an indication of one or more responsive semantic pairs in the identified document, wherein the one or more responsive semantic pairs each contain the at least one name of an entity of interest and at least one associated entity determined to be related to the at least one named entity of interest. | 1. A computer-implemented method for searching through a corpus, the corpus comprising a plurality of documents, and the method comprising: using a computer to parse a text of each of the plurality of documents into sentences; using the computer to identify named entities within the sentences; using the computer to identify semantic pairs in the documents, wherein each semantic pair comprises two named entities appearing together in a respective one of the sentences; using the computer to receive a search request, the search request including at least one name of an entity of interest; using the computer to provide at least one search result in response to the search request wherein the at least one search result includes an identification of a document from the corpus and an indication of one or more responsive semantic pairs in the identified document, wherein the one or more responsive semantic pairs each contain the at least one name of an entity of interest and at least one associated entity determined to be related to the at least one named entity of interest. 5. The method of claim 1 , wherein the step of providing at least one search result includes providing a list of documents in an order based on a determined relevance of each respective result to the at least one name of an entity of interest. | 0.725113 |
8,156,508 | 16 | 18 | 16. The non-transitory computer-accessible memory medium of claim 15 , wherein there are more than one more specific tasks and wherein the verification step comprises, for each of the one or more specific tasks, examining one or more constraints for determining the applicability of the more specific task. | 16. The non-transitory computer-accessible memory medium of claim 15 , wherein there are more than one more specific tasks and wherein the verification step comprises, for each of the one or more specific tasks, examining one or more constraints for determining the applicability of the more specific task. 18. The non-transitory computer-accessible memory medium of claim 16 , wherein the one or more constraints are defined in a task description of the more specific task as one or more constraints on the parameter values of the parameters for the task. | 0.877461 |
9,165,045 | 2 | 3 | 2. The method of claim 1 , further comprising: tagging information in the first set of medical documents according to a first set of data fields; tagging information in the second set of medical documents according to a second set of data fields; and inserting the tagged information into the common set of data fields in the table. | 2. The method of claim 1 , further comprising: tagging information in the first set of medical documents according to a first set of data fields; tagging information in the second set of medical documents according to a second set of data fields; and inserting the tagged information into the common set of data fields in the table. 3. The method of claim 2 , wherein a second data field in the first set of data fields and the first data field in the second set of data fields correspond to the common data field in the set of common data fields. | 0.957808 |
7,725,830 | 1 | 13 | 1. A system for assembling a narration from one or more digital display media components, comprising: an input device, the input device operable to allow a user to select the one or more digital display media components and regions thereof; an output device, the output device operable to display the one or more digital display media components; a storage device, the storage device operable to record narration relating to the one or more digital display media components and simultaneously to record manual user indications of one or more locations in each of the digital display media components; and a digital multimedia production process, the digital multimedia production process operable to automatically perform cinematic display manipulations to form a digital multimedia production in accordance with the recorded narration and selected digital display media components and regions thereof, the automatically performed cinematic display manipulations including; analyzing the user's indications of the digital display media components and regions; determining cinematic rules corresponding to the user's indications with the digital display media components and regions; applying the cinematic rules to the digital display media components and regions; and performing cinematic image manipulation between the digital display media components or regions based on the applied cinematic rules. | 1. A system for assembling a narration from one or more digital display media components, comprising: an input device, the input device operable to allow a user to select the one or more digital display media components and regions thereof; an output device, the output device operable to display the one or more digital display media components; a storage device, the storage device operable to record narration relating to the one or more digital display media components and simultaneously to record manual user indications of one or more locations in each of the digital display media components; and a digital multimedia production process, the digital multimedia production process operable to automatically perform cinematic display manipulations to form a digital multimedia production in accordance with the recorded narration and selected digital display media components and regions thereof, the automatically performed cinematic display manipulations including; analyzing the user's indications of the digital display media components and regions; determining cinematic rules corresponding to the user's indications with the digital display media components and regions; applying the cinematic rules to the digital display media components and regions; and performing cinematic image manipulation between the digital display media components or regions based on the applied cinematic rules. 13. The system of claim 1 , the cinematic display manipulations further including inserting one or more cinematic transitions between the one or more digital media display components. | 0.87274 |
7,953,590 | 13 | 17 | 13. A translation system, comprising: a plurality of input channels where each input channel is assigned an expected language and is configured to receive speech from a single speaker; a memory configured to record speech from each channel to be translated and to store one or more training models and rules for translating speech received from the input channels; an automatic speech recognition engine and machine translator configured to concurrently translate speech for each channel separately and independently from each of any other channel from an input language to another language; and a language detector configured to monitor each of the input channels to determine whether speech received by a particular input channel is in a language that matches the expected language assigned to the particular input channel. | 13. A translation system, comprising: a plurality of input channels where each input channel is assigned an expected language and is configured to receive speech from a single speaker; a memory configured to record speech from each channel to be translated and to store one or more training models and rules for translating speech received from the input channels; an automatic speech recognition engine and machine translator configured to concurrently translate speech for each channel separately and independently from each of any other channel from an input language to another language; and a language detector configured to monitor each of the input channels to determine whether speech received by a particular input channel is in a language that matches the expected language assigned to the particular input channel. 17. The system as recited in claim 13 , wherein the plurality of input channels each includes a separate microphone. | 0.864169 |
8,156,452 | 13 | 14 | 13. A method for designing a system, comprising: importing a design in hardware description language (HDL) into a block library of a system designer setting a sample time for sampling signals of a simulation model, wherein the sample time is associated with a clock signal in the design for signal sampling; generating a simulation model template based on the sample time; presenting a user with a plurality of selectable simulation model types; receiving a user selected simulation model type; generating a simulation model of the design in HDL to be represented from the simulation model template, wherein the simulation model is operable to be rendered in response to the user selected simulation model type; and connecting the simulation model of the design in HDL to an available component in the block library to form the system wherein at least one of the setting, generating, presenting, receiving, and connecting procedures are performed by a processor. | 13. A method for designing a system, comprising: importing a design in hardware description language (HDL) into a block library of a system designer setting a sample time for sampling signals of a simulation model, wherein the sample time is associated with a clock signal in the design for signal sampling; generating a simulation model template based on the sample time; presenting a user with a plurality of selectable simulation model types; receiving a user selected simulation model type; generating a simulation model of the design in HDL to be represented from the simulation model template, wherein the simulation model is operable to be rendered in response to the user selected simulation model type; and connecting the simulation model of the design in HDL to an available component in the block library to form the system wherein at least one of the setting, generating, presenting, receiving, and connecting procedures are performed by a processor. 14. The method of claim 13 , wherein the component performs a mathematical operation. | 0.620536 |
8,051,071 | 19 | 25 | 19. A system, comprising: a memory device to store computer-executable instructions; and one or more processors, to execute the computer-executable instructions, to: determine an amount or rate that a document moves positions in search result rankings over time; generate a score for the document based on the amount or rate that the document moves in the search result rankings over time; and rank the document with regard to at least one other document based on the score. | 19. A system, comprising: a memory device to store computer-executable instructions; and one or more processors, to execute the computer-executable instructions, to: determine an amount or rate that a document moves positions in search result rankings over time; generate a score for the document based on the amount or rate that the document moves in the search result rankings over time; and rank the document with regard to at least one other document based on the score. 25. The system of claim 19 , where the score is a first score, where the one or more processors are further to: determine a set of search terms relating to a particular topic or news item; determine that the document is associated with the set of search terms; identify another document that is not associated with the set of search terms; generate a second score for the other document, where the first score is higher than the second score; and rank the document with regard to at least the other document based on the first and second scores. | 0.652423 |
8,483,436 | 4 | 5 | 4. The method of claim 1 , wherein the model comprises a skeletal model having joints and bones. | 4. The method of claim 1 , wherein the model comprises a skeletal model having joints and bones. 5. The method of claim 4 , wherein adjusting the model comprises: adjusting a joint of the skeletal model to the estimated location or position. | 0.914388 |
7,660,804 | 1 | 4 | 1. A method in a computing device with a processor and a memory for generating wrappers for hierarchically organized documents, each document having a document tree with nodes, the method comprising: generating by the processor, for each of a plurality of clusters of documents, a wrapper by repeating the following until all the documents have been selected: selecting a document that has not yet been selected for creation of a wrapper tree having nodes; creating the wrapper tree for the document tree of the selected document; for each document whose distance from its document tree to the wrapper tree is within a threshold distance, selecting the document; and adjusting the wrapper tree based on the document tree of the selected document; and establishing the wrapper for the documents selected for creation and adjustment of the wrapper tree based on the adjusted wrapper tree wherein a wrapper tree is created and adjusted for each cluster of documents whose document trees are within a threshold distance of the wrapper tree at the time of selection of the document, and wherein distance is represented by the following equation: Ψ ( T w , T d ) = ( C w ( T w , T d ) W ( T w ) + C d ( T w , T d ) W ( T w ) ) / 2 ( 3 ) where Ψ(T w ,T d ) represents the distance between wrapper tree T w and document tree T d , C w (T w ,T d ) represents count of nodes of the wrapper tree T w that do not match document nodes of document tree T d , C d (T w ,T d ) represents count of nodes of document tree T d that do not match wrapper nodes of the wrapper tree T w , and W(T w ) represents a weight of wrapper tree T w . | 1. A method in a computing device with a processor and a memory for generating wrappers for hierarchically organized documents, each document having a document tree with nodes, the method comprising: generating by the processor, for each of a plurality of clusters of documents, a wrapper by repeating the following until all the documents have been selected: selecting a document that has not yet been selected for creation of a wrapper tree having nodes; creating the wrapper tree for the document tree of the selected document; for each document whose distance from its document tree to the wrapper tree is within a threshold distance, selecting the document; and adjusting the wrapper tree based on the document tree of the selected document; and establishing the wrapper for the documents selected for creation and adjustment of the wrapper tree based on the adjusted wrapper tree wherein a wrapper tree is created and adjusted for each cluster of documents whose document trees are within a threshold distance of the wrapper tree at the time of selection of the document, and wherein distance is represented by the following equation: Ψ ( T w , T d ) = ( C w ( T w , T d ) W ( T w ) + C d ( T w , T d ) W ( T w ) ) / 2 ( 3 ) where Ψ(T w ,T d ) represents the distance between wrapper tree T w and document tree T d , C w (T w ,T d ) represents count of nodes of the wrapper tree T w that do not match document nodes of document tree T d , C d (T w ,T d ) represents count of nodes of document tree T d that do not match wrapper nodes of the wrapper tree T w , and W(T w ) represents a weight of wrapper tree T w . 4. The method of claim 1 wherein when multiple wrapper trees have been generated, identifying a wrapper to use for extracting data from a document tree based on distance between the document tree and the wrapper trees. | 0.502283 |
8,443,336 | 1 | 3 | 1. A system having a central processing unit for model-based testing of an industrial system, comprising: a generic test case layer that includes virtual functions for initializing a test environment, establishing preconditions, checking preconditions, and checking postconditions; a test environment layer that includes additional virtual functions needed to defines the initial state of the industrial system being tested, and a library that accesses functionality of the test environment; a use case definition layer for generating UML diagrams and annotations that model the industrial system and that defines the functions for establishing preconditions, checking preconditions, executing the test cases, and checking postconditions, wherein the annotations comprise annotating UML activity diagrams by attaching one or more notes containing data variable definitions to an activity to which said data serves as input and using-custom stereotypes in notes anchored to a particular activity in the UML activity diagrams; and a test case layer that interprets said UML diagrams to generate test cases for testing said UML model. | 1. A system having a central processing unit for model-based testing of an industrial system, comprising: a generic test case layer that includes virtual functions for initializing a test environment, establishing preconditions, checking preconditions, and checking postconditions; a test environment layer that includes additional virtual functions needed to defines the initial state of the industrial system being tested, and a library that accesses functionality of the test environment; a use case definition layer for generating UML diagrams and annotations that model the industrial system and that defines the functions for establishing preconditions, checking preconditions, executing the test cases, and checking postconditions, wherein the annotations comprise annotating UML activity diagrams by attaching one or more notes containing data variable definitions to an activity to which said data serves as input and using-custom stereotypes in notes anchored to a particular activity in the UML activity diagrams; and a test case layer that interprets said UML diagrams to generate test cases for testing said UML model. 3. The system of claim 1 , further comprising a test data layer containing definitions for all data parameters used by said test case layer. | 0.910026 |
8,126,712 | 26 | 27 | 26. An information communication system connected with a plurality of information communication terminals via a network, one of the information communication terminals includes: a speech recognition module configured to recognize at least speech information sent via the network to identify a plurality of words, based on the recognized speech information; a storage medium configured to store keyword extraction condition setting data in which conditions for extracting keywords are set; a keyword extraction module configured to read the keyword extraction condition setting data to extract a plurality of keywords from the plurality of words; a related information acquisition module configured to acquire related information related to the plurality of keywords; and a related information output module configured to provide related information to a monitor, wherein the keyword extraction module includes: a subject extraction processing module configured to: associate the plurality of words extracted from the speech information sent via the network with knowledge network data in which the plurality of keywords and a route among the plurality of keywords are described in a network form, generate a plurality of word pairs in a predetermined order from the plurality of keywords, extract the shortest route connecting words in each word pair from the knowledge network data, give point values to words on each of the shortest routes, count the point values given to the respective words, and extract a word having a relatively high point value as a subject word. | 26. An information communication system connected with a plurality of information communication terminals via a network, one of the information communication terminals includes: a speech recognition module configured to recognize at least speech information sent via the network to identify a plurality of words, based on the recognized speech information; a storage medium configured to store keyword extraction condition setting data in which conditions for extracting keywords are set; a keyword extraction module configured to read the keyword extraction condition setting data to extract a plurality of keywords from the plurality of words; a related information acquisition module configured to acquire related information related to the plurality of keywords; and a related information output module configured to provide related information to a monitor, wherein the keyword extraction module includes: a subject extraction processing module configured to: associate the plurality of words extracted from the speech information sent via the network with knowledge network data in which the plurality of keywords and a route among the plurality of keywords are described in a network form, generate a plurality of word pairs in a predetermined order from the plurality of keywords, extract the shortest route connecting words in each word pair from the knowledge network data, give point values to words on each of the shortest routes, count the point values given to the respective words, and extract a word having a relatively high point value as a subject word. 27. The information communication system according to claim 26 , wherein the information terminal further includes: a warning information acquisition module configured to read operation condition setting data stored in the storage medium to acquire warning information for displaying a warning via the network, when the plurality of keywords extracted by the keyword extraction module include a specific keyword. | 0.707801 |
9,529,904 | 11 | 14 | 11. A computer program product for updating ontology when a set of evidences and a set of constraints are given as inputs, the computer program product comprising: one or more computer-readable storage media and program instructions stored on the one or more computer-readable storage media, the program instructions comprising: program instructions to categorize one or more new concepts included in a set of evidences into one of three sets, a) a definitely relevant set, b) a possibly relevant set, and c) an irrelevant set, wherein i) concepts included in the definitely relevant set meet or exceed a first categorization threshold, ii) concepts included in the irrelevant set are below a second categorization threshold, and iii) concepts included in the possibly relevant set are (a) below the first categorization threshold and (b) meet or exceed the second categorization threshold; program instructions to add a categorized new concept included in the definitely relevant set to an first ontology; program instructions to add a categorized new concept included in the possibly relevant set to a residual ontology; program instructions to match one or more new concepts included in the set of evidences to an old concept included in the first ontology or to an old concept included in the residual ontology, wherein an old concept existed as part of the first ontology or the residual ontology before the respective addition of the new concepts to the first ontology or the residual ontology; program instructions to determine to increase an associated confidence measure of the old concept, included in the first ontology or the residual ontology, based at least in part, on the matching; program instructions to determine to expand the first ontology or the residual ontology by respectively exchanging one or more old concepts between the first ontology and the residual ontology; and program instructions to remove one or more old concepts from the first ontology or the residual ontology based, at least in part, on a set of constraints, wherein the constraints dictate size and performance requirements of the first ontology. | 11. A computer program product for updating ontology when a set of evidences and a set of constraints are given as inputs, the computer program product comprising: one or more computer-readable storage media and program instructions stored on the one or more computer-readable storage media, the program instructions comprising: program instructions to categorize one or more new concepts included in a set of evidences into one of three sets, a) a definitely relevant set, b) a possibly relevant set, and c) an irrelevant set, wherein i) concepts included in the definitely relevant set meet or exceed a first categorization threshold, ii) concepts included in the irrelevant set are below a second categorization threshold, and iii) concepts included in the possibly relevant set are (a) below the first categorization threshold and (b) meet or exceed the second categorization threshold; program instructions to add a categorized new concept included in the definitely relevant set to an first ontology; program instructions to add a categorized new concept included in the possibly relevant set to a residual ontology; program instructions to match one or more new concepts included in the set of evidences to an old concept included in the first ontology or to an old concept included in the residual ontology, wherein an old concept existed as part of the first ontology or the residual ontology before the respective addition of the new concepts to the first ontology or the residual ontology; program instructions to determine to increase an associated confidence measure of the old concept, included in the first ontology or the residual ontology, based at least in part, on the matching; program instructions to determine to expand the first ontology or the residual ontology by respectively exchanging one or more old concepts between the first ontology and the residual ontology; and program instructions to remove one or more old concepts from the first ontology or the residual ontology based, at least in part, on a set of constraints, wherein the constraints dictate size and performance requirements of the first ontology. 14. The computer program product of claim 11 , wherein each of the categorized new concepts and old concepts included in one or both of the first ontology or the residual ontology respectively include a description of the provenance of that concept and a match between that concept and other concepts, wherein each of the concepts are one of either a single term, or a hierarchy of terms in a subsumption relationship. | 0.651667 |
7,987,421 | 30 | 31 | 30. One or more computer-readable media containing a program according to claim 29 , wherein: the location software to locate a first layout information file includes location software to locate a layout information file specifying how first content and the first layout string are to be presented to the user; the program further comprises obtaining software to obtain the first content from a first content provider; and the presentation software to present the first layout string to the user includes presentation software to present the first content and the first layout string to the user according to the first layout information file. | 30. One or more computer-readable media containing a program according to claim 29 , wherein: the location software to locate a first layout information file includes location software to locate a layout information file specifying how first content and the first layout string are to be presented to the user; the program further comprises obtaining software to obtain the first content from a first content provider; and the presentation software to present the first layout string to the user includes presentation software to present the first content and the first layout string to the user according to the first layout information file. 31. One or more computer-readable media containing a program according to claim 30 , wherein the location software includes location software to locate the one of the plurality of layout strings files storing the first layout string in a selected language. | 0.873518 |
9,910,931 | 16 | 19 | 16. The system of claim 13 , wherein the navigation key entry received on the user interface is selected from an enter key, a tab key, a space key, a right arrow key, a left arrow key and combinations thereof. | 16. The system of claim 13 , wherein the navigation key entry received on the user interface is selected from an enter key, a tab key, a space key, a right arrow key, a left arrow key and combinations thereof. 19. The system of claim 16 , wherein the processor is further configured to recognize the left arrow key as a prompt to clear the free text entry in the user interface. | 0.924256 |
6,105,043 | 15 | 16 | 15. The method of claim 3 above, further comprising the steps of displaying a user interface on the monitor and accepting user input via the displayed user interface to specify a type of search to be performed by the dynamic SQL query. | 15. The method of claim 3 above, further comprising the steps of displaying a user interface on the monitor and accepting user input via the displayed user interface to specify a type of search to be performed by the dynamic SQL query. 16. The method of claim 15 above, wherein the type of search is specified by the user. | 0.97888 |
8,868,591 | 9 | 11 | 9. A non-transitory computer readable storage medium storing computer instructions executable by a processor to perform a method of selecting at least one variant of a given query suggestion, including: determining, using canonicalization rules, canonical representations of the given query suggestion and unique queries that do not include the given query suggestion as a prefix, wherein the unique queries are existing queries submitted to one or more search engines in the past; comparing the canonical representation of the given query suggestion to the canonical representations of the unique queries to produce similarity scores between the given query suggestion and the unique queries, the similarity scores based on similarity between the canonical representation of the given query suggestion and the canonical representations of the unique queries; selecting one or more of the unique queries having similarity scores that satisfy a threshold as candidate variants of the given query suggestion, wherein selecting the one or more of the unique queries as candidate variants includes selecting at least one unique query which has a canonical representation identical to that of the given query suggestion; selecting one or more of the candidate variants as selected variants of the given query suggestion using query utility scores for the candidate variants, wherein a query utility score for a candidate variant is based on user response to the candidate variant during one or more prior queries; and storing data associating the given query suggestion with the selected variants for use in determining an alternative suggestion for the given query suggestion from among the selected variants. | 9. A non-transitory computer readable storage medium storing computer instructions executable by a processor to perform a method of selecting at least one variant of a given query suggestion, including: determining, using canonicalization rules, canonical representations of the given query suggestion and unique queries that do not include the given query suggestion as a prefix, wherein the unique queries are existing queries submitted to one or more search engines in the past; comparing the canonical representation of the given query suggestion to the canonical representations of the unique queries to produce similarity scores between the given query suggestion and the unique queries, the similarity scores based on similarity between the canonical representation of the given query suggestion and the canonical representations of the unique queries; selecting one or more of the unique queries having similarity scores that satisfy a threshold as candidate variants of the given query suggestion, wherein selecting the one or more of the unique queries as candidate variants includes selecting at least one unique query which has a canonical representation identical to that of the given query suggestion; selecting one or more of the candidate variants as selected variants of the given query suggestion using query utility scores for the candidate variants, wherein a query utility score for a candidate variant is based on user response to the candidate variant during one or more prior queries; and storing data associating the given query suggestion with the selected variants for use in determining an alternative suggestion for the given query suggestion from among the selected variants. 11. The non-transitory computer readable storage medium of claim 9 , wherein selecting the one or more of the candidate variants as selected variants includes: sorting the candidate variants using the query utility scores to create a ranking; and selecting the one or more unique queries as selected variants based at least in part on the ranking. | 0.643004 |
8,396,736 | 22 | 25 | 22. One or more non-transitory computer storage media storing computer executable instructions which when executed by a computer system perform a method for generating a process model representation for each of a plurality of activity chunks in a process such that each process model representation is arranged to be displayed on a single page, the method comprising: receiving, by one or more processors of the computer system, a Work Product/Activities/Roles (WAR) template for a process, the WAR template defining each work product that is used by the process, each activity performed by the process, and each role that performs an activity in the process, each activity being associated with one of a plurality of activity chunks defined for the process; generating, by one or more processors of the computer system, and for each of the activity chunks, a process activity template, each process activity template defining: any inputs used by the activity chunk; entry criteria defining when the activity chunk is to be performed; which role is responsible for performing each activity in the activity chunk; any outputs produced by the activity chunk; and exit criteria defining when the activity chunk is completed; generating, by one or more processors of the computer system, and for each process activity template, a process model representation, each process model representation being arranged to be displayed on a single page, each process model representation comprising: a graphical representation of the flow between the activities in the represented activity chunk; and a graphical representation of the inputs, entry criteria, roles involved in performing the activities of the represented activity chunk, outputs, and exit criteria. | 22. One or more non-transitory computer storage media storing computer executable instructions which when executed by a computer system perform a method for generating a process model representation for each of a plurality of activity chunks in a process such that each process model representation is arranged to be displayed on a single page, the method comprising: receiving, by one or more processors of the computer system, a Work Product/Activities/Roles (WAR) template for a process, the WAR template defining each work product that is used by the process, each activity performed by the process, and each role that performs an activity in the process, each activity being associated with one of a plurality of activity chunks defined for the process; generating, by one or more processors of the computer system, and for each of the activity chunks, a process activity template, each process activity template defining: any inputs used by the activity chunk; entry criteria defining when the activity chunk is to be performed; which role is responsible for performing each activity in the activity chunk; any outputs produced by the activity chunk; and exit criteria defining when the activity chunk is completed; generating, by one or more processors of the computer system, and for each process activity template, a process model representation, each process model representation being arranged to be displayed on a single page, each process model representation comprising: a graphical representation of the flow between the activities in the represented activity chunk; and a graphical representation of the inputs, entry criteria, roles involved in performing the activities of the represented activity chunk, outputs, and exit criteria. 25. The one or more computer storage media of claim 22 , wherein each process model representation also includes a graphical representation of the location of the represented activity chunk within the process. | 0.756977 |
9,014,511 | 14 | 15 | 14. A method comprising: receiving by a computer a user query; identifying by the computer one or more trigger words in the user query; selecting by the computer one or more corresponding tags from a landmark database corresponding to the one or more trigger words; supplementing by the computer the user query with the one or more corresponding tags, generating a supplemented user query; retrieving by the computer one or more landmarks based on the supplemented user query; generating by the computer a user interface including the one or more retrieved landmarks; and causing one or more summary lists of visual clusters for the retrieved landmarks to be displayed on the user interface, wherein each summary list corresponds to one of the retrieved landmarks; and causing popularity information to be displayed on the one or more summary lists on the user interface. | 14. A method comprising: receiving by a computer a user query; identifying by the computer one or more trigger words in the user query; selecting by the computer one or more corresponding tags from a landmark database corresponding to the one or more trigger words; supplementing by the computer the user query with the one or more corresponding tags, generating a supplemented user query; retrieving by the computer one or more landmarks based on the supplemented user query; generating by the computer a user interface including the one or more retrieved landmarks; and causing one or more summary lists of visual clusters for the retrieved landmarks to be displayed on the user interface, wherein each summary list corresponds to one of the retrieved landmarks; and causing popularity information to be displayed on the one or more summary lists on the user interface. 15. The method of claim 14 , wherein each summary list includes descriptive information about the visual clusters. | 0.891013 |
8,340,971 | 10 | 11 | 10. The method of claim 1 , wherein extracting the first portion of the turn-by-turn details of the plurality of dialogs further comprises partitioning the plurality of dialogs into two plurality of sub-dialogs. | 10. The method of claim 1 , wherein extracting the first portion of the turn-by-turn details of the plurality of dialogs further comprises partitioning the plurality of dialogs into two plurality of sub-dialogs. 11. The method of claim 10 , wherein each at least one sub-dialog ends in one of the following ways: proceed to next dialog, transfer to agent or another system, end call by system or hang-up by caller. | 0.918285 |
7,689,562 | 1 | 2 | 1. An access control system providing access control to at least one information resource associated with at least one application within a computer network, the access control system comprising: a plurality of context sources being relevant to the at least one application and providing context information; a constraint specification console providing an interface to specify application specific constraints based on the context sources; a rule engine capable of handling facts and applying inference rules on those facts, the rule engine storing the rules; an application specific constraint enforcement point configured to receive access requests, hence querying facts and further being responsible to make access decisions regarding the information resource based on those facts and on application specific constraints; and a rule engine adaptor acting as connecting component to interconnect the rule engine with the context sources, the constraint specification console and the enforcement point, respectively, and as intermediary in communication of the rule engine with the context sources, the constraint specification console and the enforcement point, respectively, so as to allow access control to the at least one information resource based on specified application specific constraints with regard to context information originating from the context sources, context information being provided by a context source being expressed as the facts within the rule engine. | 1. An access control system providing access control to at least one information resource associated with at least one application within a computer network, the access control system comprising: a plurality of context sources being relevant to the at least one application and providing context information; a constraint specification console providing an interface to specify application specific constraints based on the context sources; a rule engine capable of handling facts and applying inference rules on those facts, the rule engine storing the rules; an application specific constraint enforcement point configured to receive access requests, hence querying facts and further being responsible to make access decisions regarding the information resource based on those facts and on application specific constraints; and a rule engine adaptor acting as connecting component to interconnect the rule engine with the context sources, the constraint specification console and the enforcement point, respectively, and as intermediary in communication of the rule engine with the context sources, the constraint specification console and the enforcement point, respectively, so as to allow access control to the at least one information resource based on specified application specific constraints with regard to context information originating from the context sources, context information being provided by a context source being expressed as the facts within the rule engine. 2. An access control system according to claim 1 , wherein the rule engine adaptor is configured to translate context information from any one of the context sources into facts that can be interpreted by the rule engine. | 0.868735 |
7,483,831 | 17 | 18 | 17. The method of claim 16 , wherein the process includes generating a current candidate frequency-wise gain as a function of a broadband gain adjustment of a prior candidate frequency-wise gain. | 17. The method of claim 16 , wherein the process includes generating a current candidate frequency-wise gain as a function of a broadband gain adjustment of a prior candidate frequency-wise gain. 18. The method of claim 17 , wherein the process includes performing one or more frequency-wise gain adjustments on a prior candidate frequency-wise gain. | 0.951111 |
9,619,024 | 1 | 2 | 1. A virtual inputting device, comprising: a signal collection unit including a bioelectrical sensor for collecting bioelectrical signals and an acceleration sensor for collecting acceleration signals, the bioelectrical signals and the acceleration signals reflecting a user's gesture; a signal preprocessing unit for performing preprocessing for the bioelectrical signals and the acceleration signals collected by the signal collection unit; a signal segmentation unit for performing segmentation processing for the preprocessed bioelectrical signals and acceleration signals so as to obtain a plurality of gesture segments; a feature extracting unit for extracting feature values from the bioelectrical signals and the acceleration signals for respective gesture segments; a feature combination unit for combining feature values extracted from the feature extracting unit to form a combined feature vector; a gesture recognition unit for performing gesture recognition based on the combined feature vector; and a character mapping unit for obtaining characters corresponding to the recognized gesture according to a predetermined mapping relationship between characters and gestures, wherein the segmentation processing comprises: determining starting points and ending points for the preprocessed bioelectrical signals and the preprocessed acceleration signals respectively; and averaging the starting points so as to obtain a starting point of a gesture segment, and averaging the ending points so as to obtain an ending point of the gesture segment. | 1. A virtual inputting device, comprising: a signal collection unit including a bioelectrical sensor for collecting bioelectrical signals and an acceleration sensor for collecting acceleration signals, the bioelectrical signals and the acceleration signals reflecting a user's gesture; a signal preprocessing unit for performing preprocessing for the bioelectrical signals and the acceleration signals collected by the signal collection unit; a signal segmentation unit for performing segmentation processing for the preprocessed bioelectrical signals and acceleration signals so as to obtain a plurality of gesture segments; a feature extracting unit for extracting feature values from the bioelectrical signals and the acceleration signals for respective gesture segments; a feature combination unit for combining feature values extracted from the feature extracting unit to form a combined feature vector; a gesture recognition unit for performing gesture recognition based on the combined feature vector; and a character mapping unit for obtaining characters corresponding to the recognized gesture according to a predetermined mapping relationship between characters and gestures, wherein the segmentation processing comprises: determining starting points and ending points for the preprocessed bioelectrical signals and the preprocessed acceleration signals respectively; and averaging the starting points so as to obtain a starting point of a gesture segment, and averaging the ending points so as to obtain an ending point of the gesture segment. 2. The virtual input device of claim 1 , further comprising a sending unit for sending the characters corresponding to the recognized gesture to an external device. | 0.849541 |
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