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9,218,390 | 1 | 10 |
1. A method comprising: deriving, via a query parser derivation computing device, a query parser for parsing an unstructured geographic web-search query into a field-based format, the deriving of the query parser comprising: receiving an input query, wherein the input query comprises a series of tokens; assigning a label to each of a plurality of the tokens; calculating the most probable label sequence for the input query; assigning one or more sentences from a plurality of sentences to each label based at least in part on the most probable label sequence for the input query, wherein: the one or more sentences are different from the labels; and the one or more sentences are assigned so that the respective sentence identifies the respective label as corresponding to one or more of a search term, a geographic expression, a geographic expression relation indication, and/or uninteresting information; creating a conditional random field model based at least in part on i) the tokens, ii) the labels, iii) characterizing a set of one or more feature functions, wherein: the set of one or more feature functions represent a state transition feature and/or one or more features of an output state for an input sequence; and a conditional probability is computed based in part on the set of one or more feature functions; training the one or more state transition features and the one or more output state features on a labeled set, wherein learning the state transition feature is limited on learning the one or more features of the output state; and utilizing, by the query parser, conditional random fields, learned by semi-supervised automated learning and based at least in part on the training, to produce structured information from the unstructured geographic web-search query, wherein the utilizing the conditional random fields to produce the structured information comprises: parsing the unstructured geographic web-search query to produce the structured information from the unstructured geographic web-search query; determining that the parsing the unstructured geographic web-search query results in a multiple interpretation condition, where the parsing identifies at least a first interpretation of the unstructured geographic web-search query corresponding to first parsing results and a second interpretation of the unstructured geographic web-search query corresponding to second parsing results; and based at least in part on user behavior data, disambiguate the first parsing results and the second parsing results to select the first parsing results corresponding to the first interpretation of the unstructured geographic web-search query.
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1. A method comprising: deriving, via a query parser derivation computing device, a query parser for parsing an unstructured geographic web-search query into a field-based format, the deriving of the query parser comprising: receiving an input query, wherein the input query comprises a series of tokens; assigning a label to each of a plurality of the tokens; calculating the most probable label sequence for the input query; assigning one or more sentences from a plurality of sentences to each label based at least in part on the most probable label sequence for the input query, wherein: the one or more sentences are different from the labels; and the one or more sentences are assigned so that the respective sentence identifies the respective label as corresponding to one or more of a search term, a geographic expression, a geographic expression relation indication, and/or uninteresting information; creating a conditional random field model based at least in part on i) the tokens, ii) the labels, iii) characterizing a set of one or more feature functions, wherein: the set of one or more feature functions represent a state transition feature and/or one or more features of an output state for an input sequence; and a conditional probability is computed based in part on the set of one or more feature functions; training the one or more state transition features and the one or more output state features on a labeled set, wherein learning the state transition feature is limited on learning the one or more features of the output state; and utilizing, by the query parser, conditional random fields, learned by semi-supervised automated learning and based at least in part on the training, to produce structured information from the unstructured geographic web-search query, wherein the utilizing the conditional random fields to produce the structured information comprises: parsing the unstructured geographic web-search query to produce the structured information from the unstructured geographic web-search query; determining that the parsing the unstructured geographic web-search query results in a multiple interpretation condition, where the parsing identifies at least a first interpretation of the unstructured geographic web-search query corresponding to first parsing results and a second interpretation of the unstructured geographic web-search query corresponding to second parsing results; and based at least in part on user behavior data, disambiguate the first parsing results and the second parsing results to select the first parsing results corresponding to the first interpretation of the unstructured geographic web-search query. 10. The method of claim 1 , wherein for each label in L = argmax L P ( L Q ) .
| 0.976769 |
7,921,071 | 18 | 21 |
18. A recommendation system, comprising: a recommendation engine configured to generate personalized item recommendations for target users based on item preference data stored for said target users; and a similar items filter configured to filter the personalized item recommendations generated by the recommendation engine based on item similarities data to prevent at least some items that are fuzzy duplicates of each other from being recommended in combination to a target user, said similar items filter thereby increasing a diversity of personalized recommendations presented to target users; said recommendation engine and similar items filter comprising computer hardware that executes software.
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18. A recommendation system, comprising: a recommendation engine configured to generate personalized item recommendations for target users based on item preference data stored for said target users; and a similar items filter configured to filter the personalized item recommendations generated by the recommendation engine based on item similarities data to prevent at least some items that are fuzzy duplicates of each other from being recommended in combination to a target user, said similar items filter thereby increasing a diversity of personalized recommendations presented to target users; said recommendation engine and similar items filter comprising computer hardware that executes software. 21. The recommendation system of claim 18 , wherein the recommendation system is configured to use behavior-based associations between items to generate the personalized item recommendations, and the similar items filter is configured to use attribute-based similarities between the items to filter the personalized recommendations.
| 0.638344 |
8,180,790 | 15 | 17 |
15. A method performed by a search engine device for providing one or more search macros relevant to a search query, the method comprising: receiving a search query from an end user device at the search engine device; accessing, at the search engine device, information regarding a plurality of pre-existing and user-defined search macros available to the search engine device, the plurality of search macros having been previously defined by a plurality of different end users, each search macro comprising a set of user-defined search operators that operate to modify aspects of how searches are performed by the search engine device; determining, at the search engine device, one or more search macros relevant to the search query from the plurality of search macros; and communicating an identification of the one or more search macros from the search engine device to the end user device, wherein the identification of the one or more search macros is presented by the end user device.
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15. A method performed by a search engine device for providing one or more search macros relevant to a search query, the method comprising: receiving a search query from an end user device at the search engine device; accessing, at the search engine device, information regarding a plurality of pre-existing and user-defined search macros available to the search engine device, the plurality of search macros having been previously defined by a plurality of different end users, each search macro comprising a set of user-defined search operators that operate to modify aspects of how searches are performed by the search engine device; determining, at the search engine device, one or more search macros relevant to the search query from the plurality of search macros; and communicating an identification of the one or more search macros from the search engine device to the end user device, wherein the identification of the one or more search macros is presented by the end user device. 17. The method of claim 15 , wherein the method further comprises determining one or more search results based on the search query.
| 0.758303 |
8,370,361 | 12 | 13 |
12. The method of claim 8 , wherein the organization entity is the affiliation string in bibliographic database and the constituent entities are the words or tokens in the affiliation string.
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12. The method of claim 8 , wherein the organization entity is the affiliation string in bibliographic database and the constituent entities are the words or tokens in the affiliation string. 13. The method of claim 12 , wherein the bibliographic database is MEDLINE.
| 0.5 |
9,563,692 | 7 | 12 |
7. A system comprising: one or more computers and one or more storage devices storing instructions that are operable, when executed by the one or more computers, to cause the one or more computers to perform operations comprising: obtaining a plurality of search results responsive to an initial search query, the search results including a first search result that identifies a first resource; determining, using a document-to-query-to-document model that associates the first resource with a plurality of previously submitted queries and associates each of the plurality of previously submitted queries with one or more resources that have been previously identified by search results for the previously submitted query, that the first resource is relevant to a first suggested query of the previously submitted queries different from the initial search query; generating a presentation of the search results responsive to the initial search query, wherein each search result in the presentation includes a link to a respective resource, wherein the first search result in the presentation includes a link that, upon a selection by a user, causes the first suggested query to be submitted to a search engine; and providing the presentation of the search results in response to the initial search query.
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7. A system comprising: one or more computers and one or more storage devices storing instructions that are operable, when executed by the one or more computers, to cause the one or more computers to perform operations comprising: obtaining a plurality of search results responsive to an initial search query, the search results including a first search result that identifies a first resource; determining, using a document-to-query-to-document model that associates the first resource with a plurality of previously submitted queries and associates each of the plurality of previously submitted queries with one or more resources that have been previously identified by search results for the previously submitted query, that the first resource is relevant to a first suggested query of the previously submitted queries different from the initial search query; generating a presentation of the search results responsive to the initial search query, wherein each search result in the presentation includes a link to a respective resource, wherein the first search result in the presentation includes a link that, upon a selection by a user, causes the first suggested query to be submitted to a search engine; and providing the presentation of the search results in response to the initial search query. 12. The system of claim 7 , wherein the first suggested query is associated with search results having an associated measure of diversity with the plurality of search results responsive to the initial query, the measure of diversity satisfying a diversity threshold.
| 0.789889 |
8,666,963 | 1 | 8 |
1. A method, implemented at least in part on at least one hardware computer processor, of performing a search for content on the Internet, the method comprising: receiving voice input provided from a user; generating at least one text search query for a plurality of Internet-accessible search engines that search for content on the Internet, wherein the at least one text search query is generated, at least in part, by performing speech recognition on the voice input using at least one language model; and periodically updating the at least one language model based on frequently searched terms, wherein the at least one text search query comprises at least two text search queries, and wherein the act of generating further comprises: generating a first of the at least two text search queries, at least in part, by performing speech recognition on the voice input using a first language model associated with a first of the plurality of search engines; and generating a second of the at least two text search queries, at least in part, by performing speech recognition on the voice input using a second language model associated with a second of the plurality of search engines.
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1. A method, implemented at least in part on at least one hardware computer processor, of performing a search for content on the Internet, the method comprising: receiving voice input provided from a user; generating at least one text search query for a plurality of Internet-accessible search engines that search for content on the Internet, wherein the at least one text search query is generated, at least in part, by performing speech recognition on the voice input using at least one language model; and periodically updating the at least one language model based on frequently searched terms, wherein the at least one text search query comprises at least two text search queries, and wherein the act of generating further comprises: generating a first of the at least two text search queries, at least in part, by performing speech recognition on the voice input using a first language model associated with a first of the plurality of search engines; and generating a second of the at least two text search queries, at least in part, by performing speech recognition on the voice input using a second language model associated with a second of the plurality of search engines. 8. The method of claim 1 , wherein the first language model is different from the second language model.
| 0.846154 |
8,863,115 | 13 | 15 |
13. A computer program product for executing program code, the computer program product comprising a computer-readable storage medium including computer program code for: receiving a source code file that includes computer code in a host language integrated with inset computer code in a domain specific language, the domain specific language being different from the host language; reading the source code file; responsive to reading computer code in the host language, invoking a set of computing instructions indicated by the computer code in accordance with the host language by interpreting a computing instruction indicated by the computer code as an operation in the host language; and responsive to reading inset computer code in the domain specific language, invoking a set of computing instructions indicated by the inset computer code in accordance with the domain specific language by performing operations including: selecting a domain specific language specification for the inset computer code, the domain specific language specification including instructions written in the host language for executing the inset computer code by relating tokens that include strings of characters from the domain specific language to corresponding tokens that include strings of characters from the host language and relating at least one grammatical rule for operations on tokens from the domain specific language to at least one corresponding grammatical rule for operations on tokens from the host language, and using the domain specific language specification to process the inset computer code.
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13. A computer program product for executing program code, the computer program product comprising a computer-readable storage medium including computer program code for: receiving a source code file that includes computer code in a host language integrated with inset computer code in a domain specific language, the domain specific language being different from the host language; reading the source code file; responsive to reading computer code in the host language, invoking a set of computing instructions indicated by the computer code in accordance with the host language by interpreting a computing instruction indicated by the computer code as an operation in the host language; and responsive to reading inset computer code in the domain specific language, invoking a set of computing instructions indicated by the inset computer code in accordance with the domain specific language by performing operations including: selecting a domain specific language specification for the inset computer code, the domain specific language specification including instructions written in the host language for executing the inset computer code by relating tokens that include strings of characters from the domain specific language to corresponding tokens that include strings of characters from the host language and relating at least one grammatical rule for operations on tokens from the domain specific language to at least one corresponding grammatical rule for operations on tokens from the host language, and using the domain specific language specification to process the inset computer code. 15. The computer program product of claim 13 , wherein invoking the set of computing instructions indicated by the inset computer code in accordance with the domain specific language comprises: passing the inset computer code to a domain specific language inset processor for execution thereby; and passing a binding context to the domain specific language inset processor for the execution of the inset computer code, the binding context describing a memory state of a computing system when the inset computer code is read, and the binding context including values of variables in the host language corresponding to the invocation of the set of computing instructions indicated by the computer code.
| 0.5 |
8,762,156 | 8 | 14 |
8. A machine readable non-transitory storage medium storing executable program instructions which when executed cause a data processing system to perform a method comprising: receiving a speech input from a user of a data processing system; determining a context, of the data processing system, when the speech input was received; recognizing text in the speech input through a speech recognition system that includes an acoustic model and a language model, the recognizing of text producing a first text output; storing the first text output as a parsed data structure having a plurality of tokens each of which represents a word in the first text output; processing each of the tokens with a set of interpreters, each interpreter in the set being designed to search one or more databases to search for matches between one or more items in the databases and at least one of the tokens, each of the interpreters determining from any matches and from the context whether it can repair a token in the first text output, wherein each interpreter is designed to repair an error of a specific type in the first text output; merging selected results from the set of interpreters to produce a final interpreted speech transcription which represents a repaired version of the first text output; providing the final interpreted speech transcription to a selected application, in a set of applications, based on a command in the final interpreted speech transcription, the selected application to execute the command in the final interpreted speech transcription.
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8. A machine readable non-transitory storage medium storing executable program instructions which when executed cause a data processing system to perform a method comprising: receiving a speech input from a user of a data processing system; determining a context, of the data processing system, when the speech input was received; recognizing text in the speech input through a speech recognition system that includes an acoustic model and a language model, the recognizing of text producing a first text output; storing the first text output as a parsed data structure having a plurality of tokens each of which represents a word in the first text output; processing each of the tokens with a set of interpreters, each interpreter in the set being designed to search one or more databases to search for matches between one or more items in the databases and at least one of the tokens, each of the interpreters determining from any matches and from the context whether it can repair a token in the first text output, wherein each interpreter is designed to repair an error of a specific type in the first text output; merging selected results from the set of interpreters to produce a final interpreted speech transcription which represents a repaired version of the first text output; providing the final interpreted speech transcription to a selected application, in a set of applications, based on a command in the final interpreted speech transcription, the selected application to execute the command in the final interpreted speech transcription. 14. The medium as in claim 8 wherein the merging merges only non-overlapping results from the set of interpreters, and overlapping results from the set of interpreters are ranked in a ranked set and one result in the ranked set is selected and merged into the final interpreted speech transcription.
| 0.552395 |
9,672,490 | 1 | 2 |
1. A method for intelligent comparison and matching to identify a system response for a voice query, the method comprising: a. comparing entries of a parameterized voice query with entries of a historical knowledge base by an artificial intelligence engine wherein the parameterized voice query includes commands and parameters identified by: (i) converting a voice query captured through a personal computing device to text using a speech to text converter; (ii) analyzing the text and parsing the text into words; and (iii) referring to a command library to identify the words as one of commands and parameters; b. performing matches between the entries of the parameterized voice query with the entries of the historical knowledge base and, if there are no matches, checking for existence of a module parameter, performing matches between the entries of the parameterized voice query with the entries of a module specific knowledge data model or a plurality of knowledge models, rating the matches between the entries of the parameterized voice query and the entries of a module specific knowledge data model or a plurality of knowledge models to assign a match confidence level, short listing potential matches between the entries of the parameterized voice query and the entries of a module specific knowledge data model or a plurality of knowledge models based on a threshold of match confidence level, in case of multiple potential matches, selecting a match between the entries of the parameterized voice query and the entries of a module specific knowledge data model or a plurality of knowledge models with the highest confidence level as a best match, and selecting a system response ID associated with the best match and passing the response ID to a response solution engine; c. identifying a procurement sub system or a combination of the sub systems corresponding to the system response ID; d. identifying sub system specific pre-built action methods and parameters corresponding to the system response ID; and e. initiating a system response by executing the pre-built action method by passing the parameterized voice query to the pre-built method of the sub system, wherein the module parameter signifies a knowledge data model specific to a module of the system.
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1. A method for intelligent comparison and matching to identify a system response for a voice query, the method comprising: a. comparing entries of a parameterized voice query with entries of a historical knowledge base by an artificial intelligence engine wherein the parameterized voice query includes commands and parameters identified by: (i) converting a voice query captured through a personal computing device to text using a speech to text converter; (ii) analyzing the text and parsing the text into words; and (iii) referring to a command library to identify the words as one of commands and parameters; b. performing matches between the entries of the parameterized voice query with the entries of the historical knowledge base and, if there are no matches, checking for existence of a module parameter, performing matches between the entries of the parameterized voice query with the entries of a module specific knowledge data model or a plurality of knowledge models, rating the matches between the entries of the parameterized voice query and the entries of a module specific knowledge data model or a plurality of knowledge models to assign a match confidence level, short listing potential matches between the entries of the parameterized voice query and the entries of a module specific knowledge data model or a plurality of knowledge models based on a threshold of match confidence level, in case of multiple potential matches, selecting a match between the entries of the parameterized voice query and the entries of a module specific knowledge data model or a plurality of knowledge models with the highest confidence level as a best match, and selecting a system response ID associated with the best match and passing the response ID to a response solution engine; c. identifying a procurement sub system or a combination of the sub systems corresponding to the system response ID; d. identifying sub system specific pre-built action methods and parameters corresponding to the system response ID; and e. initiating a system response by executing the pre-built action method by passing the parameterized voice query to the pre-built method of the sub system, wherein the module parameter signifies a knowledge data model specific to a module of the system. 2. A method of claim 1 , wherein the module specific knowledge data model is associated with one of spend analysis, sourcing, performance metric management, contracts, procure to pay, and supplier management.
| 0.836991 |
8,032,448 | 24 | 25 |
24. A method as in claim 21 , wherein the affinity is based on historical transaction data for the customer.
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24. A method as in claim 21 , wherein the affinity is based on historical transaction data for the customer. 25. A method as in claim 24 , wherein the historical transaction data used for the determined affinity is selected from a group comprising: transaction frequency in the associated merchant cluster, transaction money volume in the associated merchant cluster, average transaction amount in the associated merchant cluster.
| 0.5 |
8,204,310 | 16 | 17 |
16. A method implemented at least in part by a processing unit, the method comprising: receiving time sequential, ink data for a handwritten East Asian character; producing online ink data to be conditioned that includes a writing sequence of the handwritten East Asian character; extracting features from the conditioned online ink data, the features comprising at least one of a tangent feature, a curvature feature, a local length feature, a connection point feature, or an imaginary stroke feature; and recognizing the handwritten East Asian character that corresponds to the features being extracted based on a Hidden Markov Model.
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16. A method implemented at least in part by a processing unit, the method comprising: receiving time sequential, ink data for a handwritten East Asian character; producing online ink data to be conditioned that includes a writing sequence of the handwritten East Asian character; extracting features from the conditioned online ink data, the features comprising at least one of a tangent feature, a curvature feature, a local length feature, a connection point feature, or an imaginary stroke feature; and recognizing the handwritten East Asian character that corresponds to the features being extracted based on a Hidden Markov Model. 17. The method of claim 16 , further comprising assessing the handwritten East Asian character based on linking movements of five or more writing directions for each ink data frame.
| 0.5 |
9,430,704 | 9 | 11 |
9. An image processing system comprising: a non-transitory memory for storing: a source image; a binarized image generated from the source image; a connected components module for detecting character targets within the binarized image, the character targets covering contiguous portions of the binarized image; a text unit module, coupled to the connected components module, for forming connected neighbors by grouping the character targets having bounding boxes with a horizontal overlap greater than a horizontal overlap threshold, and for forming a text unit by grouping the character targets of the connected neighbors, the character targets having a character vertical overlap greater than a character vertical overlap threshold, each of the character targets having a character feature within a feature threshold, and the text unit a portion of the source image; an identify baseline module, coupled to the text unit module, for calculating a text unit baseline angle for rotating the text unit to the horizontal; and an optical character recognition module, coupled to the text unit module, for detecting an output text of the text unit for display on a device; and a processor for processing the connected components module, the text unit module, the identify baseline module and the optical character recognition module, wherein the text unit module is for expanding each of the bounding boxes of the character targets horizontally by an extra border distance, the extra border distance calculated by a value α multiplied by the character height plus a border offset.
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9. An image processing system comprising: a non-transitory memory for storing: a source image; a binarized image generated from the source image; a connected components module for detecting character targets within the binarized image, the character targets covering contiguous portions of the binarized image; a text unit module, coupled to the connected components module, for forming connected neighbors by grouping the character targets having bounding boxes with a horizontal overlap greater than a horizontal overlap threshold, and for forming a text unit by grouping the character targets of the connected neighbors, the character targets having a character vertical overlap greater than a character vertical overlap threshold, each of the character targets having a character feature within a feature threshold, and the text unit a portion of the source image; an identify baseline module, coupled to the text unit module, for calculating a text unit baseline angle for rotating the text unit to the horizontal; and an optical character recognition module, coupled to the text unit module, for detecting an output text of the text unit for display on a device; and a processor for processing the connected components module, the text unit module, the identify baseline module and the optical character recognition module, wherein the text unit module is for expanding each of the bounding boxes of the character targets horizontally by an extra border distance, the extra border distance calculated by a value α multiplied by the character height plus a border offset. 11. The system as claimed in claim 9 wherein the text detection module is for generating the binarized image of the source image where the source image is unstructured and cluttered, wherein an unstructured image includes textual elements with variations in size, font, style, stroke size, text color, and/or text background color, and a cluttered image includes text completely or partially overlaid on top of graphical elements.
| 0.640468 |
8,977,584 | 22 | 23 |
22. The method of claim 21 , wherein the dialogue script is derived from a speech-to-text module utilizing recorded speech input.
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22. The method of claim 21 , wherein the dialogue script is derived from a speech-to-text module utilizing recorded speech input. 23. The method of claim 22 , wherein the speech converted from the dialogue script is tagged as being a response to the recorded speech input.
| 0.5 |
9,812,133 | 1 | 4 |
1. A method comprising: receiving, via a processor and from a user, a plurality of speech samples of a same word, wherein the plurality of speech samples of the same word comprises a current speech sample and a plurality of previously recorded speech samples; generating, via the processor, a sample similarity from the plurality of speech samples; making a decision, via the processor, whether to enroll the user in a speech verification system according to a comparison of the sample similarity with a threshold, wherein the decision is a first decision if the sample similarity is above the threshold and the decision is a second decision different from and mutually exclusive with the first decision if the sample similarity is below the threshold; and enrolling the user in the speech verification system based at least in part on the decision.
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1. A method comprising: receiving, via a processor and from a user, a plurality of speech samples of a same word, wherein the plurality of speech samples of the same word comprises a current speech sample and a plurality of previously recorded speech samples; generating, via the processor, a sample similarity from the plurality of speech samples; making a decision, via the processor, whether to enroll the user in a speech verification system according to a comparison of the sample similarity with a threshold, wherein the decision is a first decision if the sample similarity is above the threshold and the decision is a second decision different from and mutually exclusive with the first decision if the sample similarity is below the threshold; and enrolling the user in the speech verification system based at least in part on the decision. 4. The method of claim 1 , wherein the comparison results in a variance that identifies how different the first sample is from the second sample.
| 0.706478 |
9,177,057 | 1 | 10 |
1. A computer-implemented method to provide a plurality of search results, the method comprising: receiving a search query; identifying one or more dominant concepts from the search query; expanding the one or more dominant concepts with a plurality of expanded concepts having a relationship with the one or more dominant concepts in a metabase; receiving a plurality of search results based on the search query; analyzing the search results using the expanded concepts, wherein analyzing the search results using the expanded concepts comprises identifying a strength of relationship of at least one search result to at least one expanded concept; varying a prominence of the search results such that one or more higher ranked search results are included within a listing on a search results page and one or more lower ranked search results are included in one or more tabs on the search results page, wherein varying the prominence of the search results comprises providing the lower ranked search results within the one or more tabs based on the strength of relationship of the one or more lower ranked search results to the at least one expanded concept and a strength of relationship of the at least one expanded concept to the one or more dominant concepts, wherein the one or more lower ranked search results are associated with a first ranking lower than a second ranking associated with the one or more higher ranked search results; and providing the one or more higher ranked search results listed on the search results page and the one or more lower ranked search results within the one or more tabs on the search results page.
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1. A computer-implemented method to provide a plurality of search results, the method comprising: receiving a search query; identifying one or more dominant concepts from the search query; expanding the one or more dominant concepts with a plurality of expanded concepts having a relationship with the one or more dominant concepts in a metabase; receiving a plurality of search results based on the search query; analyzing the search results using the expanded concepts, wherein analyzing the search results using the expanded concepts comprises identifying a strength of relationship of at least one search result to at least one expanded concept; varying a prominence of the search results such that one or more higher ranked search results are included within a listing on a search results page and one or more lower ranked search results are included in one or more tabs on the search results page, wherein varying the prominence of the search results comprises providing the lower ranked search results within the one or more tabs based on the strength of relationship of the one or more lower ranked search results to the at least one expanded concept and a strength of relationship of the at least one expanded concept to the one or more dominant concepts, wherein the one or more lower ranked search results are associated with a first ranking lower than a second ranking associated with the one or more higher ranked search results; and providing the one or more higher ranked search results listed on the search results page and the one or more lower ranked search results within the one or more tabs on the search results page. 10. The computer-implemented method of claim 1 , further comprising establishing a strength threshold for the plurality of expanded concepts having a relationship with the one or more dominant concepts and selecting expanded concepts that are above the established strength threshold.
| 0.501754 |
9,063,637 | 14 | 15 |
14. A system that provides an editing view and a semantic zoom view comprising: a display; an input device configured to receive input, and a processor configured to using the display, provide an editing view of a document comprising a page at a zoom level wherein a content of the document can be edited based on input from the input device, receive a first input from the input device indicating a zoom-out request wherein the zoom-out request exceeds a threshold level, using the display, provide a semantic view of the document in response the zoom-out request exceeding the threshold level, the semantic view comprising a plurality of thumbnail pages in a thumbnail pane and a plurality of headings in a heading pane, wherein the plurality of headings are derived from the document, receive a second input from input device indicating a selection of one of the plurality of headings in the heading pane, and using the display, provide a subset of the plurality of thumbnail pages in the thumbnail pane in response to receiving the second input, wherein the subset of the plurality of thumbnail pages comprises a thumbnail page corresponding to the selection of one of the plurality of headings in the heading pane and a plurality of preceding and succeeding thumbnail pages to the thumbnail page.
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14. A system that provides an editing view and a semantic zoom view comprising: a display; an input device configured to receive input, and a processor configured to using the display, provide an editing view of a document comprising a page at a zoom level wherein a content of the document can be edited based on input from the input device, receive a first input from the input device indicating a zoom-out request wherein the zoom-out request exceeds a threshold level, using the display, provide a semantic view of the document in response the zoom-out request exceeding the threshold level, the semantic view comprising a plurality of thumbnail pages in a thumbnail pane and a plurality of headings in a heading pane, wherein the plurality of headings are derived from the document, receive a second input from input device indicating a selection of one of the plurality of headings in the heading pane, and using the display, provide a subset of the plurality of thumbnail pages in the thumbnail pane in response to receiving the second input, wherein the subset of the plurality of thumbnail pages comprises a thumbnail page corresponding to the selection of one of the plurality of headings in the heading pane and a plurality of preceding and succeeding thumbnail pages to the thumbnail page. 15. The system of claim 14 wherein the display comprises a touch-screen, and the input device comprises the touch-screen.
| 0.821534 |
8,117,225 | 28 | 33 |
28. The computer program product of claim 15 , wherein the computer program product is capable of cooperating with at least one mobile application adapted for accessing at least one of the different online applications utilizing a mobile device, said computer program product further being capable of allowing the at least one mobile application to provide at least a portion of a functionality of the at least one of the different online applications.
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28. The computer program product of claim 15 , wherein the computer program product is capable of cooperating with at least one mobile application adapted for accessing at least one of the different online applications utilizing a mobile device, said computer program product further being capable of allowing the at least one mobile application to provide at least a portion of a functionality of the at least one of the different online applications. 33. The computer program product of claim 28 , wherein the computer program product is operable such that the portion of the functionality includes buffering.
| 0.749206 |
8,738,608 | 71 | 72 |
71. The method of claim 41 , wherein the plurality of data cells of the plurality of attribute data tunnels consist of data independent of the domain and the encoding for their respective attribute whereby distinct instances of said attribute may differ in domain or encoding.
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71. The method of claim 41 , wherein the plurality of data cells of the plurality of attribute data tunnels consist of data independent of the domain and the encoding for their respective attribute whereby distinct instances of said attribute may differ in domain or encoding. 72. The method of claim 71 , wherein the plurality of data cells of the plurality of attribute data tunnels are connective data cells, and the connective data cells consist of data independent of the domain and the encoding for their respective attribute whereby distinct instances of said attribute may differ in domain or encoding.
| 0.51173 |
8,150,830 | 24 | 28 |
24. A computer program product, tangibly embodied on a machine readable medium, the computer program product comprising instructions that, when read by a machine, operate to cause data processing apparatus to: provide an association of a term and a primary resource identifier, the association input by a user; receive a search query input from a user; determine whether the search query matches the term; if the search query matches the term, present the primary resource identifier without initiating a search; and if the search query does not match the term, initiate a search and present a search result based on the search query.
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24. A computer program product, tangibly embodied on a machine readable medium, the computer program product comprising instructions that, when read by a machine, operate to cause data processing apparatus to: provide an association of a term and a primary resource identifier, the association input by a user; receive a search query input from a user; determine whether the search query matches the term; if the search query matches the term, present the primary resource identifier without initiating a search; and if the search query does not match the term, initiate a search and present a search result based on the search query. 28. The computer program product of claim 24 , further comprising instructions to: receive a user input associating the term and the primary resource identifier through a selection of a control.
| 0.671186 |
9,146,894 | 8 | 9 |
8. The method of claim 1 , wherein said selecting is further based upon a predicted click through likelihood, wherein the predicted click through likelihood represents a likelihood that the user, if presented the recommendation module that identifies one of the plurality of candidate entities, will visit a page of that one of the plurality of candidate entities or create a connection to that one of the plurality of candidate entities within a social graph of the social networking system.
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8. The method of claim 1 , wherein said selecting is further based upon a predicted click through likelihood, wherein the predicted click through likelihood represents a likelihood that the user, if presented the recommendation module that identifies one of the plurality of candidate entities, will visit a page of that one of the plurality of candidate entities or create a connection to that one of the plurality of candidate entities within a social graph of the social networking system. 9. The method of claim 8 , wherein said selecting comprises: determining a score for each of the plurality of candidate entities, wherein each of the scores is based upon the weight of the candidate entity and the predicted click through likelihood of the candidate entity; and selecting the candidate entity associated with the largest score as the first entity.
| 0.5 |
8,949,195 | 14 | 16 |
14. A computer program product, comprising a non-transitory computer-readable medium having a computer-readable program code embodied therein, the computer-readable program code including instructions adapted to be executed by one or more processors to: receive, via a user interface, at least one selection of a log detail level from a plurality of log detail levels that range from a minimum log detail level to a maximum log detail level; receive, via the user interface, at least one selection of a use case from a plurality of use cases that comprise at least two of an export use case, a save use case, an import use case, a check-in use case, and a check-out use case; create a multi-dimensional logging artifact based on at least one selected log detail level and at least one selected use case; and utilize the multi-dimensional logging artifact with log data to create refined log data, wherein the refined log data is based on the at least one selected log detail level and the at least one selected use case, and wherein the refined log data assists a user in evaluating an enterprise application.
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14. A computer program product, comprising a non-transitory computer-readable medium having a computer-readable program code embodied therein, the computer-readable program code including instructions adapted to be executed by one or more processors to: receive, via a user interface, at least one selection of a log detail level from a plurality of log detail levels that range from a minimum log detail level to a maximum log detail level; receive, via the user interface, at least one selection of a use case from a plurality of use cases that comprise at least two of an export use case, a save use case, an import use case, a check-in use case, and a check-out use case; create a multi-dimensional logging artifact based on at least one selected log detail level and at least one selected use case; and utilize the multi-dimensional logging artifact with log data to create refined log data, wherein the refined log data is based on the at least one selected log detail level and the at least one selected use case, and wherein the refined log data assists a user in evaluating an enterprise application. 16. The computer program product of claim 14 , wherein the log data is log data that is created after the utilization of the multi-dimensional artifact.
| 0.863799 |
8,561,069 | 14 | 22 |
14. The computer-based system of claim 12 , wherein the User Interface enables an executable workflow composition of the task according to a sequence of invoking the Service Discovery, followed by the Service Filter, followed by the Task Specifier, followed by the Task Executer, or any combinations thereof.
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14. The computer-based system of claim 12 , wherein the User Interface enables an executable workflow composition of the task according to a sequence of invoking the Service Discovery, followed by the Service Filter, followed by the Task Specifier, followed by the Task Executer, or any combinations thereof. 22. The computer-based system of claim 14 , wherein the User Interface is graphical and comprises a first window pane to display discovered and filtered services, a second window pane to display a current task composition by the user, controls to navigate a displayed history of the task composition, and a control execute the task composition.
| 0.79883 |
7,672,959 | 1 | 9 |
1. An update detecting system for detecting an update in a target file, the target file accessible by a server computer having a processing unit, and memory operatively coupled to the processing unit, comprising: a client computer having a central processing unit and memory operatively coupled to the central processing unit, the client computer and the server computer configured to communication over a communications network; the server computer configured to access the target file and transmit the target file to the server computer upon request by the client computer; a difference extractor configured to extract differences between text data included in said target file and text data included in a copy file obtained by copying said target file; a morphological analyzing unit configured to divide said differences into words to generate a word group, and configured to determine if one or more words in the word group matches a predetermined specific key word or if a group of words in the word group matches a regular expression; and a determining unit configured to determine that an update was is performed in said target file based on whether the morphological analyzing unit determined a match.
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1. An update detecting system for detecting an update in a target file, the target file accessible by a server computer having a processing unit, and memory operatively coupled to the processing unit, comprising: a client computer having a central processing unit and memory operatively coupled to the central processing unit, the client computer and the server computer configured to communication over a communications network; the server computer configured to access the target file and transmit the target file to the server computer upon request by the client computer; a difference extractor configured to extract differences between text data included in said target file and text data included in a copy file obtained by copying said target file; a morphological analyzing unit configured to divide said differences into words to generate a word group, and configured to determine if one or more words in the word group matches a predetermined specific key word or if a group of words in the word group matches a regular expression; and a determining unit configured to determine that an update was is performed in said target file based on whether the morphological analyzing unit determined a match. 9. The update detecting apparatus according to claim 1 , wherein the text data included in said target file can be viewed, and when the text data included in said target file is viewed in the case where said determining unit determines that an update was performed, a display mode of text data corresponding to said difference is changed to a predetermined display mode.
| 0.5 |
9,996,637 | 1 | 4 |
1. A method for formally verifying a hardware/software co-design, the method comprising: providing in a co-design, a first model, and a second model, wherein the first model is one of a hardware model, and the second model is one of a software model, or vice versa; performing an abstraction on the first model, wherein the abstraction comprises refining the first model to a lower abstraction level; specifying a safety property comprising one or more conditions to be satisfied by a composed hardware/software model; combining the abstraction of the first model and the safety property to obtain an abstracted first model; translating the abstracted first model and a corresponding interface model into a Property Specification Language, wherein the Property Specification Language is capable of describing a model environment for the second model; based on the described model environment, composing, by a model checker, the abstracted first model and the second model to obtain the composed hardware/software model, wherein the model checker automatically composes the abstracted first model and the second model using a construct in the Property Specification Language; verifying whether the composed hardware/software model satisfies the safety property; in response to the composed hardware/software model not satisfying the safety property, projecting, by the model checker, a counterexample on the first model, wherein the counterexample is projected on variables of the abstracted first model, the interface model, and the second model such that a sequence of model states is obtained as a consequence of projecting the counterexample; verifying whether the counterexample projected on the first model comprises a real error trace in the first model; based on the counterexample being a real error trace, signaling that the hardware/software co-design violates the safety property; and based on the counterexample not being a real error trace, refining the abstraction of the first model to eliminate the error trace.
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1. A method for formally verifying a hardware/software co-design, the method comprising: providing in a co-design, a first model, and a second model, wherein the first model is one of a hardware model, and the second model is one of a software model, or vice versa; performing an abstraction on the first model, wherein the abstraction comprises refining the first model to a lower abstraction level; specifying a safety property comprising one or more conditions to be satisfied by a composed hardware/software model; combining the abstraction of the first model and the safety property to obtain an abstracted first model; translating the abstracted first model and a corresponding interface model into a Property Specification Language, wherein the Property Specification Language is capable of describing a model environment for the second model; based on the described model environment, composing, by a model checker, the abstracted first model and the second model to obtain the composed hardware/software model, wherein the model checker automatically composes the abstracted first model and the second model using a construct in the Property Specification Language; verifying whether the composed hardware/software model satisfies the safety property; in response to the composed hardware/software model not satisfying the safety property, projecting, by the model checker, a counterexample on the first model, wherein the counterexample is projected on variables of the abstracted first model, the interface model, and the second model such that a sequence of model states is obtained as a consequence of projecting the counterexample; verifying whether the counterexample projected on the first model comprises a real error trace in the first model; based on the counterexample being a real error trace, signaling that the hardware/software co-design violates the safety property; and based on the counterexample not being a real error trace, refining the abstraction of the first model to eliminate the error trace. 4. The method according to claim 1 , wherein refining the first model is performed by means of interpolation, particularly Craig interpolation.
| 0.764803 |
9,270,548 | 19 | 20 |
19. A non-transitory computer-readable medium encoded with computer-executable instructions that, when executed, cause a data processing system to perform the steps of: monitoring calls from a client system to a server system for properties associated with an object, each call having a context; storing call data related to the calls as a property-retrieval history, including storing the context of each call; analyzing a policy associated with at least one context based on the property-retrieval history; updating the policy associated with the at least one context based on the analysis; and transferring data corresponding to the at least one context based on the policy.
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19. A non-transitory computer-readable medium encoded with computer-executable instructions that, when executed, cause a data processing system to perform the steps of: monitoring calls from a client system to a server system for properties associated with an object, each call having a context; storing call data related to the calls as a property-retrieval history, including storing the context of each call; analyzing a policy associated with at least one context based on the property-retrieval history; updating the policy associated with the at least one context based on the analysis; and transferring data corresponding to the at least one context based on the policy. 20. The computer-readable medium of claim 19 , wherein updating the policy includes defining default properties to be returned in response to a call based on the context of the call.
| 0.5 |
9,678,993 | 16 | 18 |
16. The method of claim 10 , wherein the method further comprises maintaining in a transaction history title information and behavioral data associated with the similar media file files.
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16. The method of claim 10 , wherein the method further comprises maintaining in a transaction history title information and behavioral data associated with the similar media file files. 18. The method of claim 16 , wherein the transaction history comprises, for each received input annotation, media files downloaded from the first database associated with the input annotation.
| 0.5 |
7,627,466 | 17 | 19 |
17. A computer-readable storage medium having computer-executable instructions for presenting user selectable tasks to a user via a user interface, said computer executable instructions comprising means for: receiving a natural language input from a user; analyzing the natural language input relative to an initial mapping criteria extracted from one or more sets of available tasks to identify a set of a plurality of tasks having a highest probability of match to the natural language input; populating one or more variables of a plurality of the highest probability tasks with one or more data elements extracted from the natural language input; computing a score for each of the highest probability tasks as a function of a probability of match and the populated variables associated with each of those tasks; sorting the highest probability tasks as a function of the score associated with each of those tasks; and presenting the sorted tasks to the user via a graphical user interface.
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17. A computer-readable storage medium having computer-executable instructions for presenting user selectable tasks to a user via a user interface, said computer executable instructions comprising means for: receiving a natural language input from a user; analyzing the natural language input relative to an initial mapping criteria extracted from one or more sets of available tasks to identify a set of a plurality of tasks having a highest probability of match to the natural language input; populating one or more variables of a plurality of the highest probability tasks with one or more data elements extracted from the natural language input; computing a score for each of the highest probability tasks as a function of a probability of match and the populated variables associated with each of those tasks; sorting the highest probability tasks as a function of the score associated with each of those tasks; and presenting the sorted tasks to the user via a graphical user interface. 19. The computer-readable storage medium of claim 17 further comprising computer-executable instructions for initiating execution of the tasks selected via the user interface.
| 0.731595 |
8,066,747 | 14 | 18 |
14. A method to implant in a patient a dynamic spine stabilization, motion preservation system comprising the steps of: accessing the surgical site; implanting first and second anchor systems in a first vertebra; implanting third and fourth anchor systems in a second vertebra; positioning a first horizontal anchor system relative to the first and second anchor systems with a vertical rod system connected to the first horizontal anchor system; positioning a second horizontal anchor system relative to the third and fourth anchor systems; deploying vertical rods from a vertical rod system from a position about parallel to the first horizontal rod system to a position about perpendicular to the first horizontal rod system with one vertical rod located laterally on one side of a spinous process and another vertical rod located laterally on another side of a spinous process; and moving the vertical rods into engagement with mounts on the second horizontal anchor system; and locking the first and second anchor systems to the first horizontal rod system; and locking the third and fourth anchor systems to the second horizontal rod system.
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14. A method to implant in a patient a dynamic spine stabilization, motion preservation system comprising the steps of: accessing the surgical site; implanting first and second anchor systems in a first vertebra; implanting third and fourth anchor systems in a second vertebra; positioning a first horizontal anchor system relative to the first and second anchor systems with a vertical rod system connected to the first horizontal anchor system; positioning a second horizontal anchor system relative to the third and fourth anchor systems; deploying vertical rods from a vertical rod system from a position about parallel to the first horizontal rod system to a position about perpendicular to the first horizontal rod system with one vertical rod located laterally on one side of a spinous process and another vertical rod located laterally on another side of a spinous process; and moving the vertical rods into engagement with mounts on the second horizontal anchor system; and locking the first and second anchor systems to the first horizontal rod system; and locking the third and fourth anchor systems to the second horizontal rod system. 18. The method of claim 14 including the step of using one or more cannulas to implant the first horizontal rod system and the second horizontal rod system.
| 0.648649 |
8,527,269 | 17 | 20 |
17. A computer program product for analyzing conversational data, the computer program product comprising a non-transitory computer-readable medium containing instructions, the instructions executable by one or more processors for: receiving first conversational data that is produced by an entity; identifying a first set of lexical features from the first conversational data; reducing the first set of lexical features to generate a first language map; and storing the first language map into a corpus of language maps in association with the entity, the corpus comprising a plurality of language maps that are associated with different entities.
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17. A computer program product for analyzing conversational data, the computer program product comprising a non-transitory computer-readable medium containing instructions, the instructions executable by one or more processors for: receiving first conversational data that is produced by an entity; identifying a first set of lexical features from the first conversational data; reducing the first set of lexical features to generate a first language map; and storing the first language map into a corpus of language maps in association with the entity, the corpus comprising a plurality of language maps that are associated with different entities. 20. The computer program product of claim 17 , the instructions further executable by the one or more processors for: receiving second conversational data produced by an unknown entity; identifying a second set of lexical features from the second conversational data; generating a second language map based on the second set of lexical features; comparing the second conversation language map to the corpus of language maps to identify a language map that best matches the second language map; and identifying the entity associated with the language map that best matches the second language map as the entity of the second conversational data.
| 0.5 |
8,255,460 | 16 | 21 |
16. A computer program product comprising a non-transitory tangible storage medium storing computer program instructions executable to perform a method comprising: storing a script associated with a campaign and a set of user information for a user selected for the campaign, the script specifying a plurality of events; executing a first instruction corresponding to the script, wherein the first instruction is operable to send a first communication to the user from a server; determining a value for a variable associated with an event specified by the script, wherein the determination of the value is based on an interaction with the first communication by the user; determining a second instruction according to the script based on the set of user information; and executing the second instruction to send a second communication to the user from the server.
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16. A computer program product comprising a non-transitory tangible storage medium storing computer program instructions executable to perform a method comprising: storing a script associated with a campaign and a set of user information for a user selected for the campaign, the script specifying a plurality of events; executing a first instruction corresponding to the script, wherein the first instruction is operable to send a first communication to the user from a server; determining a value for a variable associated with an event specified by the script, wherein the determination of the value is based on an interaction with the first communication by the user; determining a second instruction according to the script based on the set of user information; and executing the second instruction to send a second communication to the user from the server. 21. The computer program product of claim 16 , wherein the user information comprises demographic information.
| 0.738095 |
5,410,635 | 12 | 13 |
12. A speech recognition method as claimed in claim 11, wherein each of said first through said N-th reference time axes define first through J(n)-th signal time instants, where J(n) represents an integer dependent on said each of first through said N-th reference time axes, and wherein said mapping function defines for each of said first through said I-th pattern time instants and for said each of first through N-th reference time axes each of a predetermined plurality of consecutive ones of said first through said J(n)-th signal time instants.
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12. A speech recognition method as claimed in claim 11, wherein each of said first through said N-th reference time axes define first through J(n)-th signal time instants, where J(n) represents an integer dependent on said each of first through said N-th reference time axes, and wherein said mapping function defines for each of said first through said I-th pattern time instants and for said each of first through N-th reference time axes each of a predetermined plurality of consecutive ones of said first through said J(n)-th signal time instants. 13. A speech recognition method as claimed in claim 12, wherein said predetermined plurality is equal to three, and wherein said mapping function defines for said each of first through I-th pattern time instants and for said each of first through N-th reference time axes each of j-th, (j31 1)-th, and (j-2)-th signal time instants, where j is variable between 1 and J(n), both inclusive.
| 0.5 |
8,244,539 | 1 | 8 |
1. A method, comprising: performing by one or more computers: receiving an indication of a request for content; identifying the requested content, wherein the requested content includes first audio data having a first set of phonemes; matching one or more of a plurality of advertising files to the requested content, wherein the one or more of the plurality of advertising files includes second audio data having a second set of phonemes, and wherein the matching is based, at least in part, upon a comparison between the first and second sets of phonemes; and causing the one or more of the plurality of advertising files to be delivered in response to the request.
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1. A method, comprising: performing by one or more computers: receiving an indication of a request for content; identifying the requested content, wherein the requested content includes first audio data having a first set of phonemes; matching one or more of a plurality of advertising files to the requested content, wherein the one or more of the plurality of advertising files includes second audio data having a second set of phonemes, and wherein the matching is based, at least in part, upon a comparison between the first and second sets of phonemes; and causing the one or more of the plurality of advertising files to be delivered in response to the request. 8. The method of claim 1 , wherein the one or more of the plurality of advertising files is an Internet advertisement.
| 0.624204 |
8,453,119 | 15 | 17 |
15. The assertion monitor of claim 14 , wherein the filter is configured to receive configuration data generated in response to a simulation of a test case on the design model.
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15. The assertion monitor of claim 14 , wherein the filter is configured to receive configuration data generated in response to a simulation of a test case on the design model. 17. The assertion monitor of claim 15 , wherein the assertion in the specification is written in a formal language.
| 0.5 |
9,516,134 | 1 | 9 |
1. A method comprising: determining, by a computing device, potential members associated with a user based on an electronic mailbox associated with the user; determining a plurality of conversion rates respectively for a plurality of invitations based on a respective number of persons that joined a contact information sharing network after being sent a respective one of the plurality of invitations to join the contact information sharing network; determining, based on the plurality of conversion rates, a highest conversion rate invitation of the plurality of invitations; and sending, to at least a subset of the potential members, the highest conversion rate invitation.
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1. A method comprising: determining, by a computing device, potential members associated with a user based on an electronic mailbox associated with the user; determining a plurality of conversion rates respectively for a plurality of invitations based on a respective number of persons that joined a contact information sharing network after being sent a respective one of the plurality of invitations to join the contact information sharing network; determining, based on the plurality of conversion rates, a highest conversion rate invitation of the plurality of invitations; and sending, to at least a subset of the potential members, the highest conversion rate invitation. 9. The method of claim 1 , further comprising: determining a familial relationship between the user and a potential member based on text of an at least one electronic mail message stored in the electronic mailbox.
| 0.829327 |
7,490,041 | 4 | 6 |
4. The system of claim 1 , the recognition component utilizing an artificial intelligence component providing inference of possible real-time input entry.
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4. The system of claim 1 , the recognition component utilizing an artificial intelligence component providing inference of possible real-time input entry. 6. The system of claim 4 , the artificial intelligence component contemplating and/or accounting for quality-deterioration of the real-time input.
| 0.5 |
9,460,715 | 1 | 2 |
1. One or more computing devices comprising: one or more processors; and one or more computer-readable media storing computer-executable instructions that, when executed by the one or more processors, cause the one or more processors to perform acts comprising: receiving a first audio signal generated by a microphone of a device residing within an environment, the first audio signal including a first voice command from a first user within the environment, the first voice command comprising a first request that the device perform a first operation, the first voice command being associated with a first voice signature; causing the device to perform the first operation at least partly in response to receiving the first voice command; receiving, while the device is performing the first operation, a second audio signal generated by the microphone of the device, the second audio signal including a second voice command comprising a second request that the device perform a second operation related to the first operation being performed by the device, the second voice command being associated with a second voice signature; calculating a similarity between the first voice signature and the second voice signature to determine that the first user uttered the second voice command or that a user within the environment other than the first user uttered the second voice command; causing performance of the second operation at least partly in response to determining that the first user uttered the second voice command; and refraining from causing performance of the second operation at least partly in response to determining that a user within the environment other than the first user uttered the second voice command.
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1. One or more computing devices comprising: one or more processors; and one or more computer-readable media storing computer-executable instructions that, when executed by the one or more processors, cause the one or more processors to perform acts comprising: receiving a first audio signal generated by a microphone of a device residing within an environment, the first audio signal including a first voice command from a first user within the environment, the first voice command comprising a first request that the device perform a first operation, the first voice command being associated with a first voice signature; causing the device to perform the first operation at least partly in response to receiving the first voice command; receiving, while the device is performing the first operation, a second audio signal generated by the microphone of the device, the second audio signal including a second voice command comprising a second request that the device perform a second operation related to the first operation being performed by the device, the second voice command being associated with a second voice signature; calculating a similarity between the first voice signature and the second voice signature to determine that the first user uttered the second voice command or that a user within the environment other than the first user uttered the second voice command; causing performance of the second operation at least partly in response to determining that the first user uttered the second voice command; and refraining from causing performance of the second operation at least partly in response to determining that a user within the environment other than the first user uttered the second voice command. 2. One or more computing devices as recited in claim 1 , the acts further comprising identifying a characteristic, other than the voice signature of the second voice command, associated with the second voice command, and wherein determining to cause performance of the second operation or refrain from causing performance of the second operation is based at least in part on comparing the characteristic to a characteristic associated with the first voice command.
| 0.5 |
8,694,490 | 1 | 7 |
1. A method of representing data comprising: in a processing system, collecting pieces of communication data from a plurality of sources; normalizing the pieces of communication data from the plurality of sources such that each piece of communication data includes multiple common fields; identifying one or more communication threads of the communication data, wherein each communication thread includes two or more pieces of the communication data that are related, regardless of the source, by having similar information in one or more of the common fields; and displaying a representation of multiple pieces of the communication data as a three dimensional collection of cubes, where each cube represents a subset of the multiple pieces of communication data, and wherein a first axis for each cube represents communication threads, a second axis represents a first common field of the common fields and a third axis represents a second common field of the common fields, wherein a portal view is a three dimensional representation of a selected subset, wherein a first portal view axis represents communication threads and a second portal view axis represents time, and wherein the portal view displays communication data in a higher resolution than the three dimensional collection of cubes such that communication data is visible, wherein the cubes are color-coded according to the communication threads.
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1. A method of representing data comprising: in a processing system, collecting pieces of communication data from a plurality of sources; normalizing the pieces of communication data from the plurality of sources such that each piece of communication data includes multiple common fields; identifying one or more communication threads of the communication data, wherein each communication thread includes two or more pieces of the communication data that are related, regardless of the source, by having similar information in one or more of the common fields; and displaying a representation of multiple pieces of the communication data as a three dimensional collection of cubes, where each cube represents a subset of the multiple pieces of communication data, and wherein a first axis for each cube represents communication threads, a second axis represents a first common field of the common fields and a third axis represents a second common field of the common fields, wherein a portal view is a three dimensional representation of a selected subset, wherein a first portal view axis represents communication threads and a second portal view axis represents time, and wherein the portal view displays communication data in a higher resolution than the three dimensional collection of cubes such that communication data is visible, wherein the cubes are color-coded according to the communication threads. 7. The method of claim 1 the method comprising accepting user input that specifies one or more search parameters; and updating the three dimensional collection of cubes to represent at 4o˜pieces of the communication data that are related to the search parameters.
| 0.585174 |
10,002,291 | 1 | 5 |
1. A method of identifying one or more fillable fields of an electronic form, the system comprising: receiving, by an electronic device, an electronic form, wherein the electronic form comprises an image of a document; identifying, by an electronic device, one or more fillable field candidates of the electronic form; determining, for each fillable field candidate, whether the fillable field candidate is a fillable field by: identifying one or more box candidates from the fillable field candidates, and for one or more of the box candidates: generating an out-border for the box candidate, wherein the out-border is a rectangular zone that surrounds an outer portion of the box candidate, generating an in-border for the box candidate, wherein the in-border is a rectangular zone that encompasses at least a portion of the box candidate, determining a histogram of pixels of the box candidate between the in-border and the out-border, determining a ratio of black pixels to white pixels of the histogram, and in response to the ratio exceeding a threshold value, determining that the box candidate is a fillable field, otherwise, determining that the box candidate is not a fillable field; and updating metadata associated with the electronic form by applying, by the electronic device, a sequencing framework to only the fillable fields by: obtaining position information for each fillable field, wherein the position information indicates a position of the fillable field on the document as displayed via a display device, sorting the fillable fields based on the position information to form a sequence of fillable fields, determining a designator to each fillable field, wherein the designator indicates a position of a corresponding fillable field in the sequence, and storing the designator in a data store such that it is associated with the corresponding fillable field.
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1. A method of identifying one or more fillable fields of an electronic form, the system comprising: receiving, by an electronic device, an electronic form, wherein the electronic form comprises an image of a document; identifying, by an electronic device, one or more fillable field candidates of the electronic form; determining, for each fillable field candidate, whether the fillable field candidate is a fillable field by: identifying one or more box candidates from the fillable field candidates, and for one or more of the box candidates: generating an out-border for the box candidate, wherein the out-border is a rectangular zone that surrounds an outer portion of the box candidate, generating an in-border for the box candidate, wherein the in-border is a rectangular zone that encompasses at least a portion of the box candidate, determining a histogram of pixels of the box candidate between the in-border and the out-border, determining a ratio of black pixels to white pixels of the histogram, and in response to the ratio exceeding a threshold value, determining that the box candidate is a fillable field, otherwise, determining that the box candidate is not a fillable field; and updating metadata associated with the electronic form by applying, by the electronic device, a sequencing framework to only the fillable fields by: obtaining position information for each fillable field, wherein the position information indicates a position of the fillable field on the document as displayed via a display device, sorting the fillable fields based on the position information to form a sequence of fillable fields, determining a designator to each fillable field, wherein the designator indicates a position of a corresponding fillable field in the sequence, and storing the designator in a data store such that it is associated with the corresponding fillable field. 5. The method of claim 1 , wherein determining whether the fillable field candidate is a fillable field comprises determining whether the fillable field candidate is a line by: obtaining dimensions for the fillable field candidate, wherein the dimensions includes a height value and a length value; and in response to the length value not exceeding a first threshold value and the height value exceeding a second threshold value, identifying the fillable field candidate as a fillable field.
| 0.621726 |
8,396,878 | 1 | 5 |
1. A computer-implemented method of generating automated tags for a video file, the method comprising: receiving one or more manually generated tags associated with a video file; based at least in part on the one or more manually entered tags, determining a preliminary category for the video file; based on the preliminary category, generating a targeted transcript of the video file, wherein the targeted transcript includes a plurality of words; generating an ontology of the plurality of words based on the targeted transcript; ranking the plurality of words in the ontology based on a plurality of scoring factors; based on the ranking of the plurality of words, generating one or more automated tags associated with the video file; and generating a heat map for the video file, wherein the heat map comprises a graphical display which indicates offset locations of words within the video file with the highest rankings, wherein the plurality of scoring factors consists of two or more of: distribution of words throughout the targeted transcript of the video file, words related to the plurality of words throughout the targeted transcript of the video file, occurrence age of the related words, information associated with the one or more manually entered tags, vernacular meaning of the plurality of words, or colloquial considerations of the meaning of the plurality of words.
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1. A computer-implemented method of generating automated tags for a video file, the method comprising: receiving one or more manually generated tags associated with a video file; based at least in part on the one or more manually entered tags, determining a preliminary category for the video file; based on the preliminary category, generating a targeted transcript of the video file, wherein the targeted transcript includes a plurality of words; generating an ontology of the plurality of words based on the targeted transcript; ranking the plurality of words in the ontology based on a plurality of scoring factors; based on the ranking of the plurality of words, generating one or more automated tags associated with the video file; and generating a heat map for the video file, wherein the heat map comprises a graphical display which indicates offset locations of words within the video file with the highest rankings, wherein the plurality of scoring factors consists of two or more of: distribution of words throughout the targeted transcript of the video file, words related to the plurality of words throughout the targeted transcript of the video file, occurrence age of the related words, information associated with the one or more manually entered tags, vernacular meaning of the plurality of words, or colloquial considerations of the meaning of the plurality of words. 5. The computer-implemented method of generating automated tags for the video file as in claim 1 , further comprising cross-referencing words with the plurality of words to determine correlations between words or to construct phrases, wherein the cross-referencing of the word or words is configured to increase the ranking of the word or words.
| 0.831378 |
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.
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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.
| 0.5 |
10,002,526 | 1 | 2 |
1. A method comprising: receiving, by a communication module from a data communication subsystem of an appliance, an appliance message that was transmitted from a component of the appliance through the data communication subsystem; receiving, by the communication module, a plurality of appliance messages transmitted through the data communication subsystem, the appliance message being one of the plurality of appliance messages; filtering, by the communication module, the plurality of appliance messages for a predetermined message type to obtain the appliance message; generating, by the communication module, appliance data based on the appliance message; deleting other received appliance messages of the plurality of appliance messages; transmitting, by the communication module to an Internet-of-Things (IoT) platform adapted to determine an identity of the appliance, the appliance data; and receiving, by the communication module from the IoT platform, appliance-specific data based on the identity of the appliance; wherein the communication module is capable of controlling the appliance as an IoT device only after, and not before, the receiving of the appliance-specific data.
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1. A method comprising: receiving, by a communication module from a data communication subsystem of an appliance, an appliance message that was transmitted from a component of the appliance through the data communication subsystem; receiving, by the communication module, a plurality of appliance messages transmitted through the data communication subsystem, the appliance message being one of the plurality of appliance messages; filtering, by the communication module, the plurality of appliance messages for a predetermined message type to obtain the appliance message; generating, by the communication module, appliance data based on the appliance message; deleting other received appliance messages of the plurality of appliance messages; transmitting, by the communication module to an Internet-of-Things (IoT) platform adapted to determine an identity of the appliance, the appliance data; and receiving, by the communication module from the IoT platform, appliance-specific data based on the identity of the appliance; wherein the communication module is capable of controlling the appliance as an IoT device only after, and not before, the receiving of the appliance-specific data. 2. The method of claim 1 , wherein: the component of the appliance that transmitted the appliance message is a controller of the appliance; and the appliance message was transmitted from the controller of the appliance to a functional component of the appliance to control a function of the appliance that does not involve the communication module.
| 0.720257 |
7,802,194 | 1 | 9 |
1. A method for a structured business query language, comprising: providing a structured business query construct based on a business context of a business productivity application during runtime of the business productivity application in response to a character sequence inputted directly into the business productivity application; receiving, in response to providing the structured business query construct, an input query from within the business productivity application; accessing a business object in response to the input query; and replacing the character sequence within the application in response to the input query by inserting the business object into the business productivity application in place of the character sequence.
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1. A method for a structured business query language, comprising: providing a structured business query construct based on a business context of a business productivity application during runtime of the business productivity application in response to a character sequence inputted directly into the business productivity application; receiving, in response to providing the structured business query construct, an input query from within the business productivity application; accessing a business object in response to the input query; and replacing the character sequence within the application in response to the input query by inserting the business object into the business productivity application in place of the character sequence. 9. The method of claim 1 , wherein receiving the input query comprises: receiving a selection of an item of a hierarchical selection tree.
| 0.759582 |
5,404,395 | 37 | 40 |
37. A switching system having an internal numbering plan for use in a telecommunications network having a network numbering plan, comprising: means, responsive to receipt, from a user directly served by the switching system, of a first symbol sequence that is included in the internal numbering plan, for interpreting the received first symbol sequence at the switching system to be a feature access code of a feature of the switching system, and further responsive to receipt of a second call-control symbol sequence that is included in the network numbering plan and that is assigned, within the network numbering plan, to the switching system, for interpreting the received second symbol sequence to be an equivalent of said feature access code of the internal numbering plan of the switching system, said feature access code being a different symbol sequence from the received second symbol sequence; and means responsive to the interpretation of the first symbol sequence, for invoking in the switching system said feature, and further responsive to the interpretation of the second symbol sequence, for invoking in the switching system a same feature as is invoked by receipt of said feature access code from the user directly served by the switching system.
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37. A switching system having an internal numbering plan for use in a telecommunications network having a network numbering plan, comprising: means, responsive to receipt, from a user directly served by the switching system, of a first symbol sequence that is included in the internal numbering plan, for interpreting the received first symbol sequence at the switching system to be a feature access code of a feature of the switching system, and further responsive to receipt of a second call-control symbol sequence that is included in the network numbering plan and that is assigned, within the network numbering plan, to the switching system, for interpreting the received second symbol sequence to be an equivalent of said feature access code of the internal numbering plan of the switching system, said feature access code being a different symbol sequence from the received second symbol sequence; and means responsive to the interpretation of the first symbol sequence, for invoking in the switching system said feature, and further responsive to the interpretation of the second symbol sequence, for invoking in the switching system a same feature as is invoked by receipt of said feature access code from the user directly served by the switching system. 40. The switching system of claim 37 wherein: the interpreting means comprise means for storing definitions of symbol sequences, including (a) a definition of the feature access code in the internal numbering plan and (b) a definition of the received second symbol sequence in the network numbering plan which definition duplicates the definition of the feature access code in the internal numbering plan; and means responsive to the receipt of the second symbol sequence, for finding the definition of the received second symbol sequence among the stored definitions.
| 0.5 |
9,015,206 | 8 | 9 |
8. The method of claim 1 , the content comprising streaming media content, the streaming media content comprising at least one of audio streaming media content and video streaming media content.
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8. The method of claim 1 , the content comprising streaming media content, the streaming media content comprising at least one of audio streaming media content and video streaming media content. 9. The method of claim 8 , the streaming media content comprising pre-recorded media.
| 0.557292 |
8,870,575 | 2 | 9 |
2. The language learning system according to claim 1 , wherein the feature extraction module performs a phonetic segmentation operation on a plurality of training sentences to obtain a plurality of pronunciation units of the training sentences, and the feature extraction module obtains the training data from the pronunciation units of the training sentences, wherein the feature extraction module performs the phonetic segmentation operation on the learning sentence to obtain one or more pronunciation units of the learning sentence.
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2. The language learning system according to claim 1 , wherein the feature extraction module performs a phonetic segmentation operation on a plurality of training sentences to obtain a plurality of pronunciation units of the training sentences, and the feature extraction module obtains the training data from the pronunciation units of the training sentences, wherein the feature extraction module performs the phonetic segmentation operation on the learning sentence to obtain one or more pronunciation units of the learning sentence. 9. The language learning system according to claim 2 , wherein the feature extraction module groups the training data into a plurality of training data groups according to combinations of each pronunciation unit and the previous pronunciation unit in the training sentences, wherein the decision tree generation module generates the assessment decision trees according to the training data groups, and each of the assessment decision trees is corresponding to one of the training data groups.
| 0.837624 |
9,807,464 | 1 | 5 |
1. A computer-implemented method for television related searching, the method comprising: causing a media program to be presented on a display device; identifying metadata related to the media program being presented on the display device; extracting a plurality of keywords from the identified metadata; in response to extracting the plurality of keywords from the identified metadata, automatically generating a plurality of search suggestions based on the plurality of extracted keywords and based on search results responsive to each of the plurality of search suggestions, wherein additional search suggestions are generated in the plurality of search suggestions in response to determining that a television programming change has occurred; obtaining a plurality of search results responsive to at least a portion of the plurality of search suggestions, wherein each of the plurality of search results is associated with a content type of a plurality of content types, and wherein a first search result of the plurality of search results is associated with a television channel content type and includes a first identifier corresponding to a television program that is scheduled to be broadcast at a future time, a second search result of the plurality of search results is associated with an application content type and includes a second identifier corresponding to an application for execution on the display device, and a third search result of the plurality of search results is associated with a web page content type and includes a third identifier corresponding to web content for presentation on the display device; causing a first portion of the plurality of search suggestions and a second portion of the plurality of search results that includes the first search result, the second search result, and the third search result to be presented together on the display device in an overlay that is positioned over the media program in response to being triggered by information in the metadata, wherein: the first search result includes the first identifier that, in response to receiving a selection of the first identifier, causes the television program that is scheduled to be broadcast at the future time to be recorded at the future time; the second search result includes the second identifier that, in response to receiving a selection of the second identifier, determines whether the application has been installed, causes a prompt to install the application to be presented in response to determining that the application has not been installed, and causes the overlay to be removed and the application to be launched in response to determining that the application has been installed; the third search result includes the third identifier that, in response to receiving a selection of the third identifier, causes the web page content corresponding to the third search result to be presented within the overlay in place of the first portion of the plurality of search suggestions and the second portion of the plurality of search results that includes the first search result, the second search result, and the third search result; and the overlay is automatically removed in response to a time period elapsing in which one of the first portion of the plurality of search suggestions and the second portion of the plurality of search results has not been selected; receiving a user selection of the first identifier corresponding to the first search result of the plurality of search results; and in response to receiving the user selection of the first identifier corresponding to the first search result, causing content corresponding to the selected first identifier to be recorded at the future time by transmitting a record command to a recording device associated with the display device.
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1. A computer-implemented method for television related searching, the method comprising: causing a media program to be presented on a display device; identifying metadata related to the media program being presented on the display device; extracting a plurality of keywords from the identified metadata; in response to extracting the plurality of keywords from the identified metadata, automatically generating a plurality of search suggestions based on the plurality of extracted keywords and based on search results responsive to each of the plurality of search suggestions, wherein additional search suggestions are generated in the plurality of search suggestions in response to determining that a television programming change has occurred; obtaining a plurality of search results responsive to at least a portion of the plurality of search suggestions, wherein each of the plurality of search results is associated with a content type of a plurality of content types, and wherein a first search result of the plurality of search results is associated with a television channel content type and includes a first identifier corresponding to a television program that is scheduled to be broadcast at a future time, a second search result of the plurality of search results is associated with an application content type and includes a second identifier corresponding to an application for execution on the display device, and a third search result of the plurality of search results is associated with a web page content type and includes a third identifier corresponding to web content for presentation on the display device; causing a first portion of the plurality of search suggestions and a second portion of the plurality of search results that includes the first search result, the second search result, and the third search result to be presented together on the display device in an overlay that is positioned over the media program in response to being triggered by information in the metadata, wherein: the first search result includes the first identifier that, in response to receiving a selection of the first identifier, causes the television program that is scheduled to be broadcast at the future time to be recorded at the future time; the second search result includes the second identifier that, in response to receiving a selection of the second identifier, determines whether the application has been installed, causes a prompt to install the application to be presented in response to determining that the application has not been installed, and causes the overlay to be removed and the application to be launched in response to determining that the application has been installed; the third search result includes the third identifier that, in response to receiving a selection of the third identifier, causes the web page content corresponding to the third search result to be presented within the overlay in place of the first portion of the plurality of search suggestions and the second portion of the plurality of search results that includes the first search result, the second search result, and the third search result; and the overlay is automatically removed in response to a time period elapsing in which one of the first portion of the plurality of search suggestions and the second portion of the plurality of search results has not been selected; receiving a user selection of the first identifier corresponding to the first search result of the plurality of search results; and in response to receiving the user selection of the first identifier corresponding to the first search result, causing content corresponding to the selected first identifier to be recorded at the future time by transmitting a record command to a recording device associated with the display device. 5. The method of claim 1 , wherein obtaining the plurality of search results further comprises performing a search that inputs each of a predetermined number of the plurality of search suggestions to a search engine system and wherein the plurality of obtained search results are ranked in the overlay based on relevancy.
| 0.5 |
8,335,679 | 1 | 6 |
1. A method for local computer-aided translation using remotely-generated translation predictions, the method comprising the steps of: (a) receiving, by a remote translation server, a request for a translation of a document; (b) translating, by the remote translation server, a first portion of the document; (c) receiving, by a first one of a plurality of local machines, the translation of the first portion of the document; (d) receiving, by the first one local machine, a modification to the translated first portion of the document, and storing the modification to the translated first portion of the document in a local cache; (e) transmitting, by the first one local machine, the modification to the translated first portion of the document; (f) identifying, by the remote translation server, the modification to the translated first portion of the document as useful in translating a second portion of the document, prior to receiving the request to translate a second portion of the document; (g) generating, by the remote translation server, a translation of the second portion of the document using the modification to the translated first portion of the document, responsive to the identification of the utility of the modification to the first portion of the document in the translation of the second portion of the document; and (h) transmitting, by the remote translation server to the first one local machine, the translation of the second portion of the document.
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1. A method for local computer-aided translation using remotely-generated translation predictions, the method comprising the steps of: (a) receiving, by a remote translation server, a request for a translation of a document; (b) translating, by the remote translation server, a first portion of the document; (c) receiving, by a first one of a plurality of local machines, the translation of the first portion of the document; (d) receiving, by the first one local machine, a modification to the translated first portion of the document, and storing the modification to the translated first portion of the document in a local cache; (e) transmitting, by the first one local machine, the modification to the translated first portion of the document; (f) identifying, by the remote translation server, the modification to the translated first portion of the document as useful in translating a second portion of the document, prior to receiving the request to translate a second portion of the document; (g) generating, by the remote translation server, a translation of the second portion of the document using the modification to the translated first portion of the document, responsive to the identification of the utility of the modification to the first portion of the document in the translation of the second portion of the document; and (h) transmitting, by the remote translation server to the first one local machine, the translation of the second portion of the document. 6. The method of claim 1 , wherein step (c) further comprises receiving an updated version of the translation and replacing the received translation.
| 0.534375 |
8,315,482 | 9 | 17 |
9. A system comprising: a platform configured to: provide an input panel having user-selectable modes comprising at least a text input mode and a shape input mode, wherein a user selects from the user-selectable modes before entering digital ink to the input panel; receive the digital ink as input to the input panel after the user selects from the user-selectable modes; provide the digital ink to a recognition service that recognizes the digital ink; receive a recognition result from the recognition service, the recognition result comprising recognized text or a recognized non-textual shape; in a first instance when the user has placed the input panel into the text input mode before entering the digital ink and the recognition result comprises the recognized text, provide the recognized text to a text processing application; in a second instance when the user has placed the input panel into the shape input mode before entering the digital ink and the recognition result comprises the recognized non-textual shape, provide the recognized non-textual shape to a non-textual shape processing application that is different from the text processing application; and in a third instance when the user has placed the input panel into the shape input mode before the digital ink is received and the recognition result comprises the recognized text, provide the recognized text as a keyword to the non-textual shape processing application to use in a keyword search to locate another non-textual shape that is related to the keyword; and at least one processing device configured to execute the platform.
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9. A system comprising: a platform configured to: provide an input panel having user-selectable modes comprising at least a text input mode and a shape input mode, wherein a user selects from the user-selectable modes before entering digital ink to the input panel; receive the digital ink as input to the input panel after the user selects from the user-selectable modes; provide the digital ink to a recognition service that recognizes the digital ink; receive a recognition result from the recognition service, the recognition result comprising recognized text or a recognized non-textual shape; in a first instance when the user has placed the input panel into the text input mode before entering the digital ink and the recognition result comprises the recognized text, provide the recognized text to a text processing application; in a second instance when the user has placed the input panel into the shape input mode before entering the digital ink and the recognition result comprises the recognized non-textual shape, provide the recognized non-textual shape to a non-textual shape processing application that is different from the text processing application; and in a third instance when the user has placed the input panel into the shape input mode before the digital ink is received and the recognition result comprises the recognized text, provide the recognized text as a keyword to the non-textual shape processing application to use in a keyword search to locate another non-textual shape that is related to the keyword; and at least one processing device configured to execute the platform. 17. The system according to claim 9 , wherein the text processing application and the non-textual shape processing application are different applications.
| 0.795213 |
8,280,892 | 1 | 9 |
1. A computer-implemented method comprising: accessing a document stored in one or more tangible media, the document comprising a plurality of text units, a text unit comprising a plurality of words, the plurality of words comprising a plurality of keywords; performing the following for each text unit using a processor: ranking the plurality of words of the each text unit according to a ranking technique; selecting one or more highly ranked words as the keywords of the each text unit; establishing relatedness among the keywords of each text unit; and selecting one or more keywords according to the established relatedness as one or more candidate tags to yield a candidate tag set for the each text unit; using the processor, determining relatedness between the candidate tags of each candidate tag set and the candidate tags of other candidate tag sets; and using the processor, assigning at least one candidate tag to the document according to the determined relatedness.
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1. A computer-implemented method comprising: accessing a document stored in one or more tangible media, the document comprising a plurality of text units, a text unit comprising a plurality of words, the plurality of words comprising a plurality of keywords; performing the following for each text unit using a processor: ranking the plurality of words of the each text unit according to a ranking technique; selecting one or more highly ranked words as the keywords of the each text unit; establishing relatedness among the keywords of each text unit; and selecting one or more keywords according to the established relatedness as one or more candidate tags to yield a candidate tag set for the each text unit; using the processor, determining relatedness between the candidate tags of each candidate tag set and the candidate tags of other candidate tag sets; and using the processor, assigning at least one candidate tag to the document according to the determined relatedness. 9. The method of claim 1 , the assigning the at least one candidate tag to the document according to the determined relatedness further comprising: assigning the at least one candidate tag that is most highly related to the other candidate tags.
| 0.835128 |
8,666,040 | 13 | 14 |
13. A method of analyzing speech application performance comprising: determining a call path for each of a plurality of calls listed within a log of an interactive voice response system having a speech application, wherein each call path is defined by an ordered set of dialog nodes of the speech application; determining a number of occurrences of a selected type of event at each dialog node of the call path for each of the plurality of calls; identifying call paths of the plurality of calls that correspond to search criteria specifying, at least in part, a number of occurrences of the selected type of event; and presenting a graphic illustration of an identified call path.
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13. A method of analyzing speech application performance comprising: determining a call path for each of a plurality of calls listed within a log of an interactive voice response system having a speech application, wherein each call path is defined by an ordered set of dialog nodes of the speech application; determining a number of occurrences of a selected type of event at each dialog node of the call path for each of the plurality of calls; identifying call paths of the plurality of calls that correspond to search criteria specifying, at least in part, a number of occurrences of the selected type of event; and presenting a graphic illustration of an identified call path. 14. The method of claim 13 , wherein identifying call paths of the plurality of calls further comprises selecting at least one call path according to the number of occurrences of the selected type of event for a particular dialog node.
| 0.5 |
8,390,839 | 3 | 4 |
3. The image formation system according to claim 2 , wherein said apparatus selection unit selects said image formation apparatus to process the selected document by dragging the document icon corresponding to said selected document onto an apparatus icon corresponding to said selected image formation apparatus by said input unit, said notification unit provides said second notification to the user, when the document icon corresponding to said selected document is dragged on the apparatus icon corresponding to said image formation apparatus by said input unit, the performance information of said image formation apparatus corresponding to said dragged apparatus icon for the type of the selected document.
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3. The image formation system according to claim 2 , wherein said apparatus selection unit selects said image formation apparatus to process the selected document by dragging the document icon corresponding to said selected document onto an apparatus icon corresponding to said selected image formation apparatus by said input unit, said notification unit provides said second notification to the user, when the document icon corresponding to said selected document is dragged on the apparatus icon corresponding to said image formation apparatus by said input unit, the performance information of said image formation apparatus corresponding to said dragged apparatus icon for the type of the selected document. 4. The image formation system according to claim 3 , wherein said notification unit provides said second notification by vibrating said input unit in an amplitude corresponding to the performance information of said image formation apparatus corresponding to said dragged apparatus icon for the type of the selected document.
| 0.5 |
8,943,394 | 1 | 6 |
1. An apparatus allowing agent intervention in an automated call center application configured to handle multiple simultaneous calls, the method comprising: a processor-based automated dialog system receiving spoken input from the caller in a dialog between the caller, translating the spoken input into a series of words to form a hypothesis regarding the caller input; a notification module configured to automatically notify the agent upon recognition of a potential problem indicated by one of: a repeat by the caller of a phrase, a pause of a duration exceeding a defined time limit, and an increase in volume by the caller over a defined threshold volume; a call monitoring module monitoring the dialog between a caller and an automated dialog system, and allowing the agent to intervene in the event that a confidence level in the hypothesis formed by the automated dialog system does not exceed a defined threshold confidence level; a graphical user interface component displaying dialog information for a plurality of conversations comprising the multiple simultaneous calls through respective tabbed subwindows; a user interface providing the agent with information for each conversation of the plurality of conversations, regarding the conversation flow between the caller and the automated dialog system, obtained semantic information for the dialog, and waveform information for the recognized utterances within the dialog of a respective conversation, and wherein the notification module provides an automatic notification for any of the plurality of conversations as a potential problem occurs between the automated dialog system and a respective caller, and wherein each tabbed subwindow includes respective display areas displaying information for the conversation flow in a first display window showing dialog flow, obtained semantic information in a second display window showing a plurality of active slots associated with a current state of the respective conversation and respective slot values for each of the plurality of active slots, and waveform information in a third display window showing actual waveforms of caller utterances provided by waveform files, for a respective call.
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1. An apparatus allowing agent intervention in an automated call center application configured to handle multiple simultaneous calls, the method comprising: a processor-based automated dialog system receiving spoken input from the caller in a dialog between the caller, translating the spoken input into a series of words to form a hypothesis regarding the caller input; a notification module configured to automatically notify the agent upon recognition of a potential problem indicated by one of: a repeat by the caller of a phrase, a pause of a duration exceeding a defined time limit, and an increase in volume by the caller over a defined threshold volume; a call monitoring module monitoring the dialog between a caller and an automated dialog system, and allowing the agent to intervene in the event that a confidence level in the hypothesis formed by the automated dialog system does not exceed a defined threshold confidence level; a graphical user interface component displaying dialog information for a plurality of conversations comprising the multiple simultaneous calls through respective tabbed subwindows; a user interface providing the agent with information for each conversation of the plurality of conversations, regarding the conversation flow between the caller and the automated dialog system, obtained semantic information for the dialog, and waveform information for the recognized utterances within the dialog of a respective conversation, and wherein the notification module provides an automatic notification for any of the plurality of conversations as a potential problem occurs between the automated dialog system and a respective caller, and wherein each tabbed subwindow includes respective display areas displaying information for the conversation flow in a first display window showing dialog flow, obtained semantic information in a second display window showing a plurality of active slots associated with a current state of the respective conversation and respective slot values for each of the plurality of active slots, and waveform information in a third display window showing actual waveforms of caller utterances provided by waveform files, for a respective call. 6. The apparatus of claim 1 wherein the conversation flow comprises state information for a current state and past states of the dialog between the caller and the automated dialog system.
| 0.705047 |
9,805,126 | 1 | 13 |
1. A method comprising, by one or more computer systems of a social-networking system: receiving, from a client device of a first user of the social-networking system, a search query comprising one or more characters inputted by the first user, the one or more characters being received as the first user inputs the one or more characters at the client device into a user interface of the social-networking system; identifying one or more entities associated with the social-networking system matching the one or more characters of the search query; ranking each of the identified entities matching the search query based on a calculated likelihood that the user will interact with a search result corresponding to the identified entity, wherein the calculated likelihood that the user will interact with the search result corresponding to the identified entity is based at least in part on a historical behavior of the user in selecting one or more prior search results related to the identified entity; boosting one or more ranks of one or more of the identified entities matching the search query based on: one or more business objectives of the social-networking system, wherein at least one of the business objectives comprises encouraging the first user to interact with the identified entity based on a level of social relevance of the identified entity with respect to the first user; and a level of the user's interaction with one or more features of the social-networking system associated with the identified entity, wherein the boosting is designed to increase the level of the user's interaction with the one or more features of the social-networking system associated with the identified entity; and sending, to the client device of the first user and in response to the received search query, one or more search results for presentation to the user as the user inputs the one or more characters at the client device into the user interface, each search result corresponding to one of the identified entities, the search results being presented according to the ranking and boosting of the corresponding identified entities, the presentation of the search results to the user enabling the user to select one or more of the search results to interact with.
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1. A method comprising, by one or more computer systems of a social-networking system: receiving, from a client device of a first user of the social-networking system, a search query comprising one or more characters inputted by the first user, the one or more characters being received as the first user inputs the one or more characters at the client device into a user interface of the social-networking system; identifying one or more entities associated with the social-networking system matching the one or more characters of the search query; ranking each of the identified entities matching the search query based on a calculated likelihood that the user will interact with a search result corresponding to the identified entity, wherein the calculated likelihood that the user will interact with the search result corresponding to the identified entity is based at least in part on a historical behavior of the user in selecting one or more prior search results related to the identified entity; boosting one or more ranks of one or more of the identified entities matching the search query based on: one or more business objectives of the social-networking system, wherein at least one of the business objectives comprises encouraging the first user to interact with the identified entity based on a level of social relevance of the identified entity with respect to the first user; and a level of the user's interaction with one or more features of the social-networking system associated with the identified entity, wherein the boosting is designed to increase the level of the user's interaction with the one or more features of the social-networking system associated with the identified entity; and sending, to the client device of the first user and in response to the received search query, one or more search results for presentation to the user as the user inputs the one or more characters at the client device into the user interface, each search result corresponding to one of the identified entities, the search results being presented according to the ranking and boosting of the corresponding identified entities, the presentation of the search results to the user enabling the user to select one or more of the search results to interact with. 13. The method of claim 1 , wherein ranking each of the identified entities is further based on a level of content relevance of the identified entity to the search query.
| 0.849291 |
9,262,941 | 43 | 44 |
43. The non-transitory computer-readable storage medium of claim 21 , wherein one or more of the vowel space metrics are calculated based on a first vowel space characteristic, formant F 1 , and a second vowel space characteristic, formant F 2 ; wherein the vowel space metrics comprise a within category vowel space dispersion.
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43. The non-transitory computer-readable storage medium of claim 21 , wherein one or more of the vowel space metrics are calculated based on a first vowel space characteristic, formant F 1 , and a second vowel space characteristic, formant F 2 ; wherein the vowel space metrics comprise a within category vowel space dispersion. 44. The non-transitory computer-readable storage medium of claim 43 , wherein the within category vowel space dispersion is calculated according to: dispersion = 1 3 * ( ∑ D IY i , I Y _ N IY + ∑ D AA i , A _ A N AA + ∑ D OW i , O W _ N OW ) , where N IY is a number of IY vowel tokens, N AA is a number of AA vowel tokens, N OW is a number of OW vowel tokens, D IY i , IY is a distance from an IY vowel token i to mean F 1 and F 2 values for vowel IY, D AA i , AA is a distance from an AA vowel token i to mean F 1 and F 2 values for vowel AA, and D OW i , OW is a distance from an OW vowel token i to mean F 1 and F 2 values for vowel OW.
| 0.5 |
9,542,438 | 15 | 16 |
15. The handheld computing device of claim 14 , wherein the processor-executable instructions, when executed by the processor, cause the processor to: obtain, from a search engine, a compiled list of commonly-used terms; and identify the first suggested first terms from the compiled list.
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15. The handheld computing device of claim 14 , wherein the processor-executable instructions, when executed by the processor, cause the processor to: obtain, from a search engine, a compiled list of commonly-used terms; and identify the first suggested first terms from the compiled list. 16. The handheld computing device of claim 15 , wherein the processor-executable instructions, when executed by the processor, cause the processor to: obtain the other suggested first terms from the compiled list.
| 0.5 |
9,646,081 | 1 | 4 |
1. A computer implemented method for providing information related to a task in a case management system configured to process a plurality of cases, the computer implemented method comprising: identifying among the plurality of cases, by a server, a plurality of case clusters, wherein each of the plurality of case clusters is associated with a case similarity factor shared by at least two cases of the plurality of cases, and the case similarity factor is related to at least one of geographical case specific parameters and contextual case specific parameters; for a case cluster of the plurality of case clusters, identifying, by the server, a plurality of task clusters, wherein each of the plurality of task clusters is associated with a task similarity factor shared by at least two tasks of the task cluster, and tasks of the plurality of task clusters are performed on cases of the case cluster, and wherein for the case cluster of the plurality of case clusters, the task similarity factor is related to the case similarity factor and task specific parameters and the identifying the plurality of task clusters comprises: based on the task similarity factor, using task metadata, case metadata, and semantic entities to categorize the tasks into the plurality of task clusters, wherein the semantic entities are extracted from case documents used in performing the tasks; analyzing, by the server, reports and documents used to perform the at least two tasks of the task cluster sharing the task similarity factor; and when performing a task sharing the task similarity factor with the at least two tasks, providing, by the server, at least one report based on the reports and at least one summary based on the documents.
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1. A computer implemented method for providing information related to a task in a case management system configured to process a plurality of cases, the computer implemented method comprising: identifying among the plurality of cases, by a server, a plurality of case clusters, wherein each of the plurality of case clusters is associated with a case similarity factor shared by at least two cases of the plurality of cases, and the case similarity factor is related to at least one of geographical case specific parameters and contextual case specific parameters; for a case cluster of the plurality of case clusters, identifying, by the server, a plurality of task clusters, wherein each of the plurality of task clusters is associated with a task similarity factor shared by at least two tasks of the task cluster, and tasks of the plurality of task clusters are performed on cases of the case cluster, and wherein for the case cluster of the plurality of case clusters, the task similarity factor is related to the case similarity factor and task specific parameters and the identifying the plurality of task clusters comprises: based on the task similarity factor, using task metadata, case metadata, and semantic entities to categorize the tasks into the plurality of task clusters, wherein the semantic entities are extracted from case documents used in performing the tasks; analyzing, by the server, reports and documents used to perform the at least two tasks of the task cluster sharing the task similarity factor; and when performing a task sharing the task similarity factor with the at least two tasks, providing, by the server, at least one report based on the reports and at least one summary based on the documents. 4. The method of claim 1 , wherein analyzing, by the server, the reports and the documents used to perform the at least two tasks of the task cluster sharing the task similarity factor includes: monitoring, by the server, when the documents satisfying a search query included in a request for the documents is identified; detecting, by the server, a request to access a selected document of the documents; and comparing semantic entities in the selected document with the search query and with case metadata of the case with which the task is associated to determine why the selected document is relevant to the task.
| 0.5 |
9,953,279 | 1 | 10 |
1. A computer implemented method for improving a business intelligence ecosystem, the method comprising: receiving, from a user device, a selection of a business intelligence artifact; determining one or more execution profiles for the selected business intelligence artifact; determining an initial examination score for the selected business intelligence artifact; identifying, via one or more improvement modules on a server, one or more candidate improvements to the business intelligence ecosystem based on a configurable set of rules; applying, via the one or more improvement modules on a server, one or more of the identified candidate improvements to modify the business intelligence ecosystem in which one or more of the candidate improvements was identified; executing the selected business intelligence artifact in the modified business intelligence ecosystem, wherein the selected business intelligence artifact is executed at least partially based on one or more of the execution profiles; determining examination data for the business intelligence artifact executed in the modified business intelligence ecosystem; reverting, via the one or more improvement modules, modifications to the modified business intelligence ecosystem by reverting the applied one or more candidate improvements; and identifying, via the one or more improvement modules, one or more qualified selected improvements based on a comparison of the examination data and the initial examination score, wherein at least one of the qualified selected improvements comprises at least one of the identified candidate improvements.
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1. A computer implemented method for improving a business intelligence ecosystem, the method comprising: receiving, from a user device, a selection of a business intelligence artifact; determining one or more execution profiles for the selected business intelligence artifact; determining an initial examination score for the selected business intelligence artifact; identifying, via one or more improvement modules on a server, one or more candidate improvements to the business intelligence ecosystem based on a configurable set of rules; applying, via the one or more improvement modules on a server, one or more of the identified candidate improvements to modify the business intelligence ecosystem in which one or more of the candidate improvements was identified; executing the selected business intelligence artifact in the modified business intelligence ecosystem, wherein the selected business intelligence artifact is executed at least partially based on one or more of the execution profiles; determining examination data for the business intelligence artifact executed in the modified business intelligence ecosystem; reverting, via the one or more improvement modules, modifications to the modified business intelligence ecosystem by reverting the applied one or more candidate improvements; and identifying, via the one or more improvement modules, one or more qualified selected improvements based on a comparison of the examination data and the initial examination score, wherein at least one of the qualified selected improvements comprises at least one of the identified candidate improvements. 10. The method of claim 1 further comprising: generating a set of instructions associated with applying at least one of the qualified selected improvements to the business intelligence ecosystem; and receiving a confirmation that at least one of the qualified selected improvements to the business intelligence ecosystem was applied at least partially based on the generated instructions.
| 0.675585 |
9,311,058 | 17 | 19 |
17. An apparatus comprising: a computing platform comprising at least one processor configured to: generate one or more digital signals representative of a rule in a Jabba language construct descriptive of one or more named terms that shall correspond to at least one of the following: a match expression; a sequence; an alternation; a call; or any combination thereof; electronically incorporate said one or more named terms into a language external to a compiler to enable at least one matching operation with respect to one or more sequences of candidate atoms via said one or more named terms; and store said one or more named terms as part of said language in a non-transitory memory.
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17. An apparatus comprising: a computing platform comprising at least one processor configured to: generate one or more digital signals representative of a rule in a Jabba language construct descriptive of one or more named terms that shall correspond to at least one of the following: a match expression; a sequence; an alternation; a call; or any combination thereof; electronically incorporate said one or more named terms into a language external to a compiler to enable at least one matching operation with respect to one or more sequences of candidate atoms via said one or more named terms; and store said one or more named terms as part of said language in a non-transitory memory. 19. The apparatus of claim 17 , wherein said one or more candidate atoms comprises a data structure that shall be represented via at least one of the following: a fragment of text; a class; a set of key-value attributes; or any combination thereof.
| 0.5 |
7,792,353 | 1 | 6 |
1. A method of machine learning, comprising: (a) obtaining a training set that includes training samples and corresponding assigned classification labels; (b) training an automated classifier against the training set; (c) selecting at least one of the training samples; (d) requesting confirmation/re-labeling of said at least one training sample; (e) in response to step (d), receiving a reply classification label for said at least one training sample; (f) retraining the automated classifier using the reply classification label; and, predicting a label for said at least one training sample, wherein said at least one training sample is selected in step (c) based on a comparison between the assigned classification label and the predicted label for said at least one training sample.
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1. A method of machine learning, comprising: (a) obtaining a training set that includes training samples and corresponding assigned classification labels; (b) training an automated classifier against the training set; (c) selecting at least one of the training samples; (d) requesting confirmation/re-labeling of said at least one training sample; (e) in response to step (d), receiving a reply classification label for said at least one training sample; (f) retraining the automated classifier using the reply classification label; and, predicting a label for said at least one training sample, wherein said at least one training sample is selected in step (c) based on a comparison between the assigned classification label and the predicted label for said at least one training sample. 6. A method according to claim 1 , said requesting step (d) comprises providing the assigned classification label and the predicted label for said at least one training sample to a user and allowing the user to select one of the assigned classification label and the predicted label for said at least one training sample.
| 0.754211 |
9,213,694 | 5 | 6 |
5. The method according to claim 4 , further comprising extracting fractional counts from the post-edits of the machine translated sentence pair and adding the extracted fractional counts to a fractional count table.
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5. The method according to claim 4 , further comprising extracting fractional counts from the post-edits of the machine translated sentence pair and adding the extracted fractional counts to a fractional count table. 6. The method according to claim 5 , further comprising adjusting probability distributions for the source sentence unit of the post-edits of the machine translated sentence pair.
| 0.5 |
9,536,223 | 9 | 10 |
9. A system for visually finding N-grams in near-time, comprising: a computer processor; and memory comprising modules configured to execute on the computer processor to enable the computer processor to: in response to a user request, generate digital information processable by a client device to display a GUI, wherein the GUI includes a first element for receiving user input specifying one or more query terms, a second element for receiving a user selection specifying a dataset, a third element for displaying a velocity graph, and a fourth element for displaying a list of candidate n-grams; transmit the digital information for generating the GUI to the client device; in response to receiving, from the client device, a user input specifying a particular one or more query terms and a user selection specifying a particular dataset: search for and retrieve from the dataset, documents that include the one or more query terms; detect candidate n-grams of the particular one or more query terms, wherein the detecting includes applying programmatic heuristics based on co-occurrence with the particular one or more query terms in the retrieved documents; select one or more of the candidate n-grams, wherein the selecting is based on co-occurrence statistics; and generate information processable by the client device to display, in the third element, a velocity graph that displays the number of retrieved documents as a function of time and, in the fourth element, a list of the selected candidate n-grams of the particular one or more query terms; and transmit, to the client device, the digital information for displaying the velocity graph and the candidate n-grams.
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9. A system for visually finding N-grams in near-time, comprising: a computer processor; and memory comprising modules configured to execute on the computer processor to enable the computer processor to: in response to a user request, generate digital information processable by a client device to display a GUI, wherein the GUI includes a first element for receiving user input specifying one or more query terms, a second element for receiving a user selection specifying a dataset, a third element for displaying a velocity graph, and a fourth element for displaying a list of candidate n-grams; transmit the digital information for generating the GUI to the client device; in response to receiving, from the client device, a user input specifying a particular one or more query terms and a user selection specifying a particular dataset: search for and retrieve from the dataset, documents that include the one or more query terms; detect candidate n-grams of the particular one or more query terms, wherein the detecting includes applying programmatic heuristics based on co-occurrence with the particular one or more query terms in the retrieved documents; select one or more of the candidate n-grams, wherein the selecting is based on co-occurrence statistics; and generate information processable by the client device to display, in the third element, a velocity graph that displays the number of retrieved documents as a function of time and, in the fourth element, a list of the selected candidate n-grams of the particular one or more query terms; and transmit, to the client device, the digital information for displaying the velocity graph and the candidate n-grams. 10. The system of claim 9 , wherein the module is further configured to enable the computer processor to: in response to receiving, from the client device, a user input specifying a user selected set of candidate n-grams from the selected candidate n-grams: search for and retrieve from the dataset, a second set of documents that include the user selected set of candidate n-grams; generate information processable by the client device to display, in the third element, an updated velocity graph that displays the number of retrieved documents that include the user selected set of candidate n-grams as a function of time; and transmit, to the client device, the digital information for displaying the updated velocity graph.
| 0.687876 |
9,208,450 | 15 | 19 |
15. An article of manufacture for processing electronic documents, the article of manufacture comprising: at least one non-transitory processor readable storage medium; and instructions stored on the at least one medium; wherein the instructions are configured to be readable from the at least one medium by at least one processor and thereby cause the at least one processor to operate so as to: obtain an electronic document being sent over a network toward a destination; analyze text content of the electronic documents to identify whether the electronic document matches any of a plurality of predefined document templates, wherein the electronic document conforms to a structure of at least one of the plurality of predefined document templates, and wherein the analyzing comprises executing at least one machine learning algorithm, the at least one machine learning algorithm trained using at least one sample electronic document having a predefined template; obtain a document loss prevention (DLP) policy based on the at least one predefined document template associated with the electronic document, wherein the DLP policy defines at least one rule to block sending of at least one of the electronic documents if the at least one of the electronic documents matches any of the plurality of predefined document templates; and selectively allow the electronic document to continue toward the destination based on the DLP policy.
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15. An article of manufacture for processing electronic documents, the article of manufacture comprising: at least one non-transitory processor readable storage medium; and instructions stored on the at least one medium; wherein the instructions are configured to be readable from the at least one medium by at least one processor and thereby cause the at least one processor to operate so as to: obtain an electronic document being sent over a network toward a destination; analyze text content of the electronic documents to identify whether the electronic document matches any of a plurality of predefined document templates, wherein the electronic document conforms to a structure of at least one of the plurality of predefined document templates, and wherein the analyzing comprises executing at least one machine learning algorithm, the at least one machine learning algorithm trained using at least one sample electronic document having a predefined template; obtain a document loss prevention (DLP) policy based on the at least one predefined document template associated with the electronic document, wherein the DLP policy defines at least one rule to block sending of at least one of the electronic documents if the at least one of the electronic documents matches any of the plurality of predefined document templates; and selectively allow the electronic document to continue toward the destination based on the DLP policy. 19. The article of manufacture of claim 15 , wherein the instructions are further configured to cause the at least one processor to operate further so as to analyze a predefined native document template of the electronic document to identify whether the predefined native template thereof matches any of a plurality of predefined document templates.
| 0.5 |
8,024,193 | 61 | 68 |
61. An apparatus comprising: means for identifying instances in a plurality of speech segments; means for creating feature vectors derived from a machine perception transformation of time-domain samples corresponding to the instances in the plurality of speech segments onto a feature space, wherein the machine perception transformation is correlated with human perception by using the time-domain samples retaining both amplitude and phase information of the speech segments, which were provided in sound data for a speech synthesis system; means for clustering the feature vectors using a similarity measure in the feature space; and means for replacing the clustered instances corresponding to the clustered feature vectors within a radius by a single instance.
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61. An apparatus comprising: means for identifying instances in a plurality of speech segments; means for creating feature vectors derived from a machine perception transformation of time-domain samples corresponding to the instances in the plurality of speech segments onto a feature space, wherein the machine perception transformation is correlated with human perception by using the time-domain samples retaining both amplitude and phase information of the speech segments, which were provided in sound data for a speech synthesis system; means for clustering the feature vectors using a similarity measure in the feature space; and means for replacing the clustered instances corresponding to the clustered feature vectors within a radius by a single instance. 68. The apparatus of claim 61 wherein creating feature vectors comprises: constructing a matrix W from the instances; and decomposing the matrix W.
| 0.730769 |
10,102,856 | 7 | 11 |
7. A method, comprising: operating an electronic device in a passive experience mode, the passive experience mode configured to provide a first response to a first speech not including a hardware activation phrase; determining, by a processor, characteristics of an environment of the electronic device the characteristics of the environment including a determination that a user is involved in activity related to playback of media content in the environment; and adjusting operation of the electronic device from the passive experience mode to an active experience mode based on the characteristics including that the user is involved in activity related to the playback of the media content in the environment, the active experience mode providing a second response to second speech based on the second speech including the hardware activation phrase.
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7. A method, comprising: operating an electronic device in a passive experience mode, the passive experience mode configured to provide a first response to a first speech not including a hardware activation phrase; determining, by a processor, characteristics of an environment of the electronic device the characteristics of the environment including a determination that a user is involved in activity related to playback of media content in the environment; and adjusting operation of the electronic device from the passive experience mode to an active experience mode based on the characteristics including that the user is involved in activity related to the playback of the media content in the environment, the active experience mode providing a second response to second speech based on the second speech including the hardware activation phrase. 11. The method of claim 7 , further comprising: determining characteristics of the second speech, wherein the adjusting operation of the electronic device from the passive experience mode to the active experience mode is further based on the characteristics of the second speech.
| 0.599138 |
8,825,583 | 1 | 2 |
1. A utility data processing system for processing data relating to consumption of a utility, the system comprising: a non-transitory machine readable fact memory for storage of facts relating to utility consumption received from fact sources; at least one fact source module for deriving facts from utility consumption data and adding the derived facts to the non-transitory machine readable fact memory, wherein one of the at least one fact source modules comprises an appliance identification module configured to identify one or more appliances using data based on utility consumption by the one or more appliances and to add the identity of the one or more appliances as one or more facts in the non-transitory machine readable fact memory; an inference module for inferring new facts relating to utility consumption from one or more facts stored in the non-transitory machine readable fact memory, wherein the inference module is configured to instruct the appliance identification module to search for one or more further unidentified appliances, wherein the search for each of the one or more further unidentified appliances is identified based on the one or more appliances stored as one or more facts that are likely to be present in view of the presence of an identified appliance by analyzing the utility consumption data, and wherein the search comprises matching signature profiles of candidate appliances stored in a database with measured profiles of events of electricity consumption, wherein a signature profile is represented by a series of events associated with the rate of change in power demand from one time point to the next; and an interface module.
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1. A utility data processing system for processing data relating to consumption of a utility, the system comprising: a non-transitory machine readable fact memory for storage of facts relating to utility consumption received from fact sources; at least one fact source module for deriving facts from utility consumption data and adding the derived facts to the non-transitory machine readable fact memory, wherein one of the at least one fact source modules comprises an appliance identification module configured to identify one or more appliances using data based on utility consumption by the one or more appliances and to add the identity of the one or more appliances as one or more facts in the non-transitory machine readable fact memory; an inference module for inferring new facts relating to utility consumption from one or more facts stored in the non-transitory machine readable fact memory, wherein the inference module is configured to instruct the appliance identification module to search for one or more further unidentified appliances, wherein the search for each of the one or more further unidentified appliances is identified based on the one or more appliances stored as one or more facts that are likely to be present in view of the presence of an identified appliance by analyzing the utility consumption data, and wherein the search comprises matching signature profiles of candidate appliances stored in a database with measured profiles of events of electricity consumption, wherein a signature profile is represented by a series of events associated with the rate of change in power demand from one time point to the next; and an interface module. 2. The system of claim 1 , wherein the appliance identification module comprises: a profile generator for generating a utility consumption profile from utility consumption data, the utility consumption data comprising a plurality of utility consumption values measured at a corresponding plurality of measurement points; and an event identifier for identifying an event within the utility consumption profile that matches the profile of a known event associated with operation of a known device, said known event stored in a database of utility consumption profiles.
| 0.5 |
5,519,867 | 13 | 17 |
13. The apparatus of claim 12 in which an actual scheduling priority, a default scheduling priority, and a maximum scheduling priority are associated with said application, said scheduling classes defining one or more scheduling priorities, said object-oriented class library including methods for setting each of said actual, default, and maximum scheduling priorities of said application to one of said scheduling priorities.
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13. The apparatus of claim 12 in which an actual scheduling priority, a default scheduling priority, and a maximum scheduling priority are associated with said application, said scheduling classes defining one or more scheduling priorities, said object-oriented class library including methods for setting each of said actual, default, and maximum scheduling priorities of said application to one of said scheduling priorities. 17. The apparatus of claim 13, wherein said scheduling classes comprise an object-oriented class defining a long-term scheduling priority adapted for use with threads of execution that execute for long periods of time.
| 0.617544 |
8,595,005 | 1 | 10 |
1. A computerized method for recognizing one or more emotions from a primary audio signal, comprising the acts of: extracting, using a computer, at least two features from the primary audio signal, wherein at least one of the two features is a Mel-Frequency Cepstral Coefficient (MFCC), and at least one of the two features is a statistical feature; performing, using the computer, a first comparison of the at least one statistical feature to a first reference sample; assigning, using the computer, at least one first emotional state score to the primary audio signal based on the first comparison; dividing, using the computer, the at least one MFCC feature into at least two points; sorting, using the computer, the at least two points; performing, using the computer, a second comparison of the sorted at least two points to a second reference sample; assigning, using the computer, at least one second emotional state score to the primary audio signal based on the second comparison; and, evaluating, using the computer, the at least one first emotional state score and the at least one second emotional state score to assign at least one probable emotional state to the primary audio signal.
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1. A computerized method for recognizing one or more emotions from a primary audio signal, comprising the acts of: extracting, using a computer, at least two features from the primary audio signal, wherein at least one of the two features is a Mel-Frequency Cepstral Coefficient (MFCC), and at least one of the two features is a statistical feature; performing, using the computer, a first comparison of the at least one statistical feature to a first reference sample; assigning, using the computer, at least one first emotional state score to the primary audio signal based on the first comparison; dividing, using the computer, the at least one MFCC feature into at least two points; sorting, using the computer, the at least two points; performing, using the computer, a second comparison of the sorted at least two points to a second reference sample; assigning, using the computer, at least one second emotional state score to the primary audio signal based on the second comparison; and, evaluating, using the computer, the at least one first emotional state score and the at least one second emotional state score to assign at least one probable emotional state to the primary audio signal. 10. The computerized method of claim 1 wherein dividing further comprises altering one of MFCC subsection length and overlap to optimize one of processing speed and emotion recognition accuracy.
| 0.660839 |
9,542,491 | 1 | 5 |
1. One or more computer-readable storage hardware device storing computer-useable instructions that, when used by one or more computing devices, cause the one or more computing devices to utilize keystroke logging to determine items for presentation, the instructions configured to: receive a search query including submitted content and keystroke logging information, the keystroke logging information being captured between engagement with a search query input region and execution of a search query; and determine at least one item for presentation in response to the search query based, at least in part, on the keystroke logging information, the at least one item comprising a search result, the determining comprising: ranking a plurality of potential search results in response to the search query based, at least in part, on the keystroke logging information; and determining the at least one item for presentation based on the ranking.
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1. One or more computer-readable storage hardware device storing computer-useable instructions that, when used by one or more computing devices, cause the one or more computing devices to utilize keystroke logging to determine items for presentation, the instructions configured to: receive a search query including submitted content and keystroke logging information, the keystroke logging information being captured between engagement with a search query input region and execution of a search query; and determine at least one item for presentation in response to the search query based, at least in part, on the keystroke logging information, the at least one item comprising a search result, the determining comprising: ranking a plurality of potential search results in response to the search query based, at least in part, on the keystroke logging information; and determining the at least one item for presentation based on the ranking. 5. The one or more computer-readable storage hardware device of claim 1 , wherein at least a portion of the keystroke logging information is discernable from the submitted content.
| 0.560976 |
9,430,652 | 1 | 4 |
1. A computer-implemented method for tokenizing data comprising: receiving, by a computing device, a data value to be tokenized; identifying, by the computing device, one or more use rules associated with the received data value, wherein each use rule defines a limitation on use of the received data value; modifying, by the computing device, the received data value to include a use rule identifier representing the identified one or more use rules to produce a modified data value; identifying, by the computing device, one or more token tables based on the use rule identifier; accessing, by the computing device, the identified one or more token tables for use in tokenizing the modified data value; and tokenizing, by the computing device, the modified data value using the accessed one or more token tables by querying at least one accessed token table with a portion of the modified data value including at least a portion of the use rule identifier to identify a token value mapped to the portion of the modified data value by the at least one access token table and replacing the portion of the modified data value with the identified token value to create tokenized data.
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1. A computer-implemented method for tokenizing data comprising: receiving, by a computing device, a data value to be tokenized; identifying, by the computing device, one or more use rules associated with the received data value, wherein each use rule defines a limitation on use of the received data value; modifying, by the computing device, the received data value to include a use rule identifier representing the identified one or more use rules to produce a modified data value; identifying, by the computing device, one or more token tables based on the use rule identifier; accessing, by the computing device, the identified one or more token tables for use in tokenizing the modified data value; and tokenizing, by the computing device, the modified data value using the accessed one or more token tables by querying at least one accessed token table with a portion of the modified data value including at least a portion of the use rule identifier to identify a token value mapped to the portion of the modified data value by the at least one access token table and replacing the portion of the modified data value with the identified token value to create tokenized data. 4. The method of claim 1 , wherein identifying one or more token tables based on the one or more use rules comprises: generating one or more token table based on the one or more access rules.
| 0.79724 |
7,953,580 | 3 | 5 |
3. The method of claim 2 wherein the formal domain assumptions comprise domain constraints.
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3. The method of claim 2 wherein the formal domain assumptions comprise domain constraints. 5. The method of claim 3 further comprising: fetching the domain assumptions from specifications specifying the managed objects included in the current static network element managed object model; and adding the domain assumptions.
| 0.5 |
7,676,743 | 61 | 66 |
61. A method of fitting graphical objects within a plurality of separate graphical frames in an application, each frame being associated with at least one fitting attribute with a value for fitting one or more of the graphical objects in the frame, comprising: associating the frames within a group, the group having permissible variances by which each at least one value can be modified; receiving information specifying a change to a given value of a particular fitting attribute of a first frame of the group; modifying said given value for the first frame in the group in accordance with the specified change; and in response to that modification, automatically modifying corresponding values in multiple other frames in the group in accordance with a common scaling factor that is based on the change to said given value, wherein modifying said given value and modifying the corresponding values in the other frames in the group proportionally changes the size of at least one graphical object in each of the first frame and said other frames without changing the size of the frames of said plurality of separate graphical frames.
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61. A method of fitting graphical objects within a plurality of separate graphical frames in an application, each frame being associated with at least one fitting attribute with a value for fitting one or more of the graphical objects in the frame, comprising: associating the frames within a group, the group having permissible variances by which each at least one value can be modified; receiving information specifying a change to a given value of a particular fitting attribute of a first frame of the group; modifying said given value for the first frame in the group in accordance with the specified change; and in response to that modification, automatically modifying corresponding values in multiple other frames in the group in accordance with a common scaling factor that is based on the change to said given value, wherein modifying said given value and modifying the corresponding values in the other frames in the group proportionally changes the size of at least one graphical object in each of the first frame and said other frames without changing the size of the frames of said plurality of separate graphical frames. 66. The method of claim 61 , further comprising using an algorithm to automatically optimize the at least one value for each frame in the group.
| 0.68 |
8,654,940 | 1 | 6 |
1. A method comprising acts of: identifying a translation table that includes a plurality of entries, each entry including a text exchange item and a corresponding conversational item; receiving an automatic output message, wherein the automatic output message was generated by a speech enabled application in response to a text exchange message that was entered into a text exchange client; detecting at least one conversational item in the automatic output message, which corresponds to an entry included in the translation table; in the automatic output message, substituting a corresponding text exchange item for the at least one detected conversational item to create a substitute output message, wherein the corresponding text exchange item comprises at least one letter and/or at least one emoticon; and sending the substitute output message to the text exchange client.
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1. A method comprising acts of: identifying a translation table that includes a plurality of entries, each entry including a text exchange item and a corresponding conversational item; receiving an automatic output message, wherein the automatic output message was generated by a speech enabled application in response to a text exchange message that was entered into a text exchange client; detecting at least one conversational item in the automatic output message, which corresponds to an entry included in the translation table; in the automatic output message, substituting a corresponding text exchange item for the at least one detected conversational item to create a substitute output message, wherein the corresponding text exchange item comprises at least one letter and/or at least one emoticon; and sending the substitute output message to the text exchange client. 6. The method of claim 1 , wherein the speech enabled application is a VoiceXML based application that lacks an inherent text exchange capability.
| 0.844017 |
9,129,226 | 1 | 28 |
1. A combined computer/human method for automatically identifying actionable insights for a process, the method comprising: a computer system automatically and iteratively performing the steps of: calculating metrics for data within a data set, wherein the data in the data set was generated by the process; identifying norms for the process based on the calculated metrics; identifying potentially valuable patterns in the process, comprising: calculating metrics for different samples of the process, and identifying potentially valuable patterns in the process based on which samples of the process have calculated metrics that vary from the calculated metrics for the identified norms for the process, whereby the identified potentially valuable patterns are patterns in the process that may cause deviations from the norms for the process; requesting from statistically untrained humans feedback regarding whether the potentially valuable patterns are patterns that actually produce deviations from the norms; and receiving said feedback from the statistically untrained humans; and iteratively developing actionable insights for changing the process based on said steps taken by the computer system.
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1. A combined computer/human method for automatically identifying actionable insights for a process, the method comprising: a computer system automatically and iteratively performing the steps of: calculating metrics for data within a data set, wherein the data in the data set was generated by the process; identifying norms for the process based on the calculated metrics; identifying potentially valuable patterns in the process, comprising: calculating metrics for different samples of the process, and identifying potentially valuable patterns in the process based on which samples of the process have calculated metrics that vary from the calculated metrics for the identified norms for the process, whereby the identified potentially valuable patterns are patterns in the process that may cause deviations from the norms for the process; requesting from statistically untrained humans feedback regarding whether the potentially valuable patterns are patterns that actually produce deviations from the norms; and receiving said feedback from the statistically untrained humans; and iteratively developing actionable insights for changing the process based on said steps taken by the computer system. 28. The method of claim 1 wherein the step of receiving feedback from statistically untrained humans comprises receiving a suggestion of experiments to gather additional data.
| 0.78125 |
7,908,325 | 27 | 30 |
27. A computer accessible medium, comprising program instructions configured to implement: executing on a first computer system, a first collaboration framework and an instance of an application, wherein executing the instance of the application comprises displaying an instance of a graphical user interface of the application; intercepting, via an operating system event handling mechanism executing on the first computer system, a local user input event targeted to the instance of the application, wherein the instance of the application applies a modification to the instance of the graphical user interface in response to receiving the user input event; in response to said intercepting, the first collaboration framework sending a message including the user input event to each of one or more other collaboration frameworks each executing on one of a respective one or more other computer systems that are each executing a respective other instance of the application, wherein said executing each respective other instance of the application comprises displaying a respective other instance of the graphical user interface of the application on the respective other computer system; wherein the message includes information usable by each of the one or more other collaboration frameworks to deliver the user input event, via an operating system event handling mechanism executing on the respective other computer system, to the respective other instance of the application executing on the respective other device as if the user input event originated locally from the respective other user interface displayed by the respective other application on the respective other device; and wherein the delivered user input event causes each of the respective other application instances to apply the modification to the respective instance of the graphical user interface.
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27. A computer accessible medium, comprising program instructions configured to implement: executing on a first computer system, a first collaboration framework and an instance of an application, wherein executing the instance of the application comprises displaying an instance of a graphical user interface of the application; intercepting, via an operating system event handling mechanism executing on the first computer system, a local user input event targeted to the instance of the application, wherein the instance of the application applies a modification to the instance of the graphical user interface in response to receiving the user input event; in response to said intercepting, the first collaboration framework sending a message including the user input event to each of one or more other collaboration frameworks each executing on one of a respective one or more other computer systems that are each executing a respective other instance of the application, wherein said executing each respective other instance of the application comprises displaying a respective other instance of the graphical user interface of the application on the respective other computer system; wherein the message includes information usable by each of the one or more other collaboration frameworks to deliver the user input event, via an operating system event handling mechanism executing on the respective other computer system, to the respective other instance of the application executing on the respective other device as if the user input event originated locally from the respective other user interface displayed by the respective other application on the respective other device; and wherein the delivered user input event causes each of the respective other application instances to apply the modification to the respective instance of the graphical user interface. 30. The computer accessible medium of claim 27 , wherein as part of said intercepting the program instructions are configured to implement: the first collaboration framework capturing raw keyboard and mouse events; and the first collaboration framework analyzing each captured event to determine whether the event is for the application.
| 0.5 |
4,051,459 | 13 | 14 |
13. In combination in an automated text recording and editing system, plural common system buses including an input bus, and output bus, a command bus and a selector bus, a stored program controlled central processor connected to said system buses, input-output means connected to said system buses, at least one tape controller connected to said system buses, information storage tape means controlled by said tape controller, a memory including a first program storing portion and a second operand storing portion, said second memory portion comprising read and write memory means, means connecting said central processor with said memory for reading commands from said memory first portion to said processor and for coupling data between said processor and said memory, said processor including means for issuing a coded identification signal pattern on said selector bus for enabling a selected one of said input-output means or said tape controller, means included in said central processor for issuing a mode command on said command bus, means included in said central processor for supplying output information on said output bus and for receiving information on said input bus, said input-output means including means for supplying serial text information and control characters to said central processor, said central processor including means for assembling said characters into records and means operative in conjunction with said second portion of said memory for storing said characters on a selected tape, means for entering editing commands generated at said input-output means on a selected tape via said processor, revision means for operating upon said entered editing commands and said stored character records for generating revised character records marked with editing commands, and means connected and responsive to said revision means for storing said marked character records.
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13. In combination in an automated text recording and editing system, plural common system buses including an input bus, and output bus, a command bus and a selector bus, a stored program controlled central processor connected to said system buses, input-output means connected to said system buses, at least one tape controller connected to said system buses, information storage tape means controlled by said tape controller, a memory including a first program storing portion and a second operand storing portion, said second memory portion comprising read and write memory means, means connecting said central processor with said memory for reading commands from said memory first portion to said processor and for coupling data between said processor and said memory, said processor including means for issuing a coded identification signal pattern on said selector bus for enabling a selected one of said input-output means or said tape controller, means included in said central processor for issuing a mode command on said command bus, means included in said central processor for supplying output information on said output bus and for receiving information on said input bus, said input-output means including means for supplying serial text information and control characters to said central processor, said central processor including means for assembling said characters into records and means operative in conjunction with said second portion of said memory for storing said characters on a selected tape, means for entering editing commands generated at said input-output means on a selected tape via said processor, revision means for operating upon said entered editing commands and said stored character records for generating revised character records marked with editing commands, and means connected and responsive to said revision means for storing said marked character records. 14. A combination as in claim 13, further comprising second revision means for operating upon said stored marked records and said stored editing commands for producing further revised character records.
| 0.808349 |
6,084,536 | 2 | 8 |
2. An apparatus as claimed in claim 1, wherein n is odd and equals m+1.
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2. An apparatus as claimed in claim 1, wherein n is odd and equals m+1. 8. An apparatus as claimed in claim 2, wherein the sets of code words belonging to each pair of coding states of the first type are disjunct.
| 0.5 |
9,311,048 | 13 | 14 |
13. A computer-program product tangibly embodied in a non-transitory machine-readable storage medium, including instructions configured to cause one or more data processors to perform actions including: accessing an electronic structure file that corresponds to an assessment and includes structure-file data, the structure-file data including a region of interest where a response to a question on the assessment is to be provided rendering an image of the structure-file data or processed version of the structure-file data, the rendered image representing the region of interest; detecting a first input provided during a presentation of the rendered image corresponding to specification of a position of the region of interest; detecting a second input identifying a target data element corresponding to the question; defining a segment-position specification indicative of the position of the region of interest; generating an electronic template that associates an identifier of the question with the segment-position specification; detecting a content object for processing that includes content-object data; determining that the content object for processing corresponds to the electronic template; extracting, using the segment-position specification of the template, a portion of the content-object data that corresponds to the region of interest, the portion of the content-object data including a response to the question on the assessment; evaluating the portion of the content-object data to identify the response to the question included in portion of the content-object data; determining an evaluation quality metric reflecting a confidence in the identification of the response; and determining whether a quality criterion is satisfied based on the evaluation quality metric; and when it is determined that the quality criterion is not satisfied: facilitating a presentation that includes the portion of the content-object data; and receiving a third input corresponding to an identification of a score corresponding to the response.
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13. A computer-program product tangibly embodied in a non-transitory machine-readable storage medium, including instructions configured to cause one or more data processors to perform actions including: accessing an electronic structure file that corresponds to an assessment and includes structure-file data, the structure-file data including a region of interest where a response to a question on the assessment is to be provided rendering an image of the structure-file data or processed version of the structure-file data, the rendered image representing the region of interest; detecting a first input provided during a presentation of the rendered image corresponding to specification of a position of the region of interest; detecting a second input identifying a target data element corresponding to the question; defining a segment-position specification indicative of the position of the region of interest; generating an electronic template that associates an identifier of the question with the segment-position specification; detecting a content object for processing that includes content-object data; determining that the content object for processing corresponds to the electronic template; extracting, using the segment-position specification of the template, a portion of the content-object data that corresponds to the region of interest, the portion of the content-object data including a response to the question on the assessment; evaluating the portion of the content-object data to identify the response to the question included in portion of the content-object data; determining an evaluation quality metric reflecting a confidence in the identification of the response; and determining whether a quality criterion is satisfied based on the evaluation quality metric; and when it is determined that the quality criterion is not satisfied: facilitating a presentation that includes the portion of the content-object data; and receiving a third input corresponding to an identification of a score corresponding to the response. 14. The computer-program product as recited in claim 13 , wherein the actions further include: the content-object data so as to change a file type, coordinate system, zoom, alignment or skew of, wherein the portion of the content-object data is extracted from the processed content-object data.
| 0.692469 |
8,010,518 | 5 | 6 |
5. A computer-readable storage medium containing a program which, when executed, performs an operation for managing data objects in a content management system (CMS), the operation comprising: accessing a first data object managed by the CMS, wherein the first data object includes a collection of one or more data object fragments, wherein a first fragment of the one or more data object fragments is referenced by a second data object stored in the CMS, and wherein the first data object and the second data object are composed according to respective schemas; receiving a modified version of the first data object to store in the CMS wherein the modified version of the first data object includes a modified version of the first fragment; fragmenting the modified version first data object into the one or more data object fragments; validating the modified version of the first fragment against the schema associated with the second data object; and upon determining that the modified version of the first fragment fails to validate against the schema associated with the second data object, performing a corrective action specified by the CMS, wherein the corrective action comprises generating an unmodified version of the first fragment, and further comprises one of: (i) incorporating the content from the modified version of the first fragment into the first data object, associating the unmodified version of the first fragment with the second data object, and discarding the modified version of the first fragment; and (ii) incorporating the content from the unmodified version of the first fragment into the second data object, associating the modified version of the first fragment with the first data object, and discarding the unmodified version of the first fragment.
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5. A computer-readable storage medium containing a program which, when executed, performs an operation for managing data objects in a content management system (CMS), the operation comprising: accessing a first data object managed by the CMS, wherein the first data object includes a collection of one or more data object fragments, wherein a first fragment of the one or more data object fragments is referenced by a second data object stored in the CMS, and wherein the first data object and the second data object are composed according to respective schemas; receiving a modified version of the first data object to store in the CMS wherein the modified version of the first data object includes a modified version of the first fragment; fragmenting the modified version first data object into the one or more data object fragments; validating the modified version of the first fragment against the schema associated with the second data object; and upon determining that the modified version of the first fragment fails to validate against the schema associated with the second data object, performing a corrective action specified by the CMS, wherein the corrective action comprises generating an unmodified version of the first fragment, and further comprises one of: (i) incorporating the content from the modified version of the first fragment into the first data object, associating the unmodified version of the first fragment with the second data object, and discarding the modified version of the first fragment; and (ii) incorporating the content from the unmodified version of the first fragment into the second data object, associating the modified version of the first fragment with the first data object, and discarding the unmodified version of the first fragment. 6. The computer-readable medium of claim 5 , wherein the schema for the first data object defines the allowable content or structure of the first data object and the schema for the second data object defines the allowable content or structure of the second data object.
| 0.5 |
9,058,382 | 16 | 19 |
16. A computer system for generating a feature from a hierarchy of documents, comprising: a memory storing computer-executable instructions for: accessing a hierarchical organization of the documents, the hierarchical organization specifying parent/child relations between documents, one of the documents being a root document of the hierarchical organization that has no parent document, some of the documents of the hierarchical organization being leaf documents that have no child documents, each document other than the root document and the leaf documents having both a parent document and a child document, the parent/child relations occurring when a parent document contains a reference to a child document; generating a feature for each of the documents in the hierarchical organization of documents; and for each document, generating an aggregate feature from the generated features of the documents to represent the feature for the document by combining the generated feature for that document with the generated aggregate features for child documents of that document to generate an aggregate feature for that document by summing the features for child documents of that document, dividing that sum by the number of child documents to generate a quotient, and multiply the quotient by a weighting factor so that each document in the hierarchical organization with a child document has an aggregate feature derived from the generated feature for that document and the aggregate features of the child documents of that document; and a processor for executing the computer-executable instructions stored in the memory.
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16. A computer system for generating a feature from a hierarchy of documents, comprising: a memory storing computer-executable instructions for: accessing a hierarchical organization of the documents, the hierarchical organization specifying parent/child relations between documents, one of the documents being a root document of the hierarchical organization that has no parent document, some of the documents of the hierarchical organization being leaf documents that have no child documents, each document other than the root document and the leaf documents having both a parent document and a child document, the parent/child relations occurring when a parent document contains a reference to a child document; generating a feature for each of the documents in the hierarchical organization of documents; and for each document, generating an aggregate feature from the generated features of the documents to represent the feature for the document by combining the generated feature for that document with the generated aggregate features for child documents of that document to generate an aggregate feature for that document by summing the features for child documents of that document, dividing that sum by the number of child documents to generate a quotient, and multiply the quotient by a weighting factor so that each document in the hierarchical organization with a child document has an aggregate feature derived from the generated feature for that document and the aggregate features of the child documents of that document; and a processor for executing the computer-executable instructions stored in the memory. 19. The computer system of claim 16 wherein the documents are web pages.
| 0.742857 |
7,509,311 | 14 | 15 |
14. A computer-implemented method for implementing a database comprising: allowing a user to request a view of data from said database, and a completeness level, the completeness level indicating a sampling of data from the view, as selected by the user, that is to be used in collecting the view statistic; collecting a view statistic regarding said view responsive to the request and the completeness level; developing at least one candidate plan for execution of a query; for at least one of said at least one candidate plan, using said view statistic to estimate the cost of said candidate plan; and selecting at least one of said at least one candidate plans as a recommended execution plan, wherein said step of using the view statistic to estimate the cost of said candidate plan comprises using at least one transformation rule to transform a first expression comprising all or part of said candidate plan and having an associated first expression estimate quality indicator into an equivalent second expression having an associated second expression estimate quality indicator, where said second expression comprises a reference to one or more views matching the first expression, and wherein said second expression and said first expression have an equivalent cardinality; and wherein the step of selecting the recommended execution plan comprises one of the first expression and the second expression having the higher of the first expression estimate quality indicator and the second expression estimate quality indicator.
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14. A computer-implemented method for implementing a database comprising: allowing a user to request a view of data from said database, and a completeness level, the completeness level indicating a sampling of data from the view, as selected by the user, that is to be used in collecting the view statistic; collecting a view statistic regarding said view responsive to the request and the completeness level; developing at least one candidate plan for execution of a query; for at least one of said at least one candidate plan, using said view statistic to estimate the cost of said candidate plan; and selecting at least one of said at least one candidate plans as a recommended execution plan, wherein said step of using the view statistic to estimate the cost of said candidate plan comprises using at least one transformation rule to transform a first expression comprising all or part of said candidate plan and having an associated first expression estimate quality indicator into an equivalent second expression having an associated second expression estimate quality indicator, where said second expression comprises a reference to one or more views matching the first expression, and wherein said second expression and said first expression have an equivalent cardinality; and wherein the step of selecting the recommended execution plan comprises one of the first expression and the second expression having the higher of the first expression estimate quality indicator and the second expression estimate quality indicator. 15. The computer-implemented method of claim 14 , where said step of collecting a view statistic regarding said view comprises: detecting that said view has been materialized.
| 0.598624 |
10,078,376 | 10 | 12 |
10. The mobile device according to claim 9 , wherein said keyboard mode and said camera mode are both active simultaneously.
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10. The mobile device according to claim 9 , wherein said keyboard mode and said camera mode are both active simultaneously. 12. The mobile device according to claim 10 , wherein said single operation such as said keypress, said control command, and said single gesture include the selection of said recognized character text as input text; the selection of said character key from the A-Z keyboard as input text; and the immediate output of said input text to said processor.
| 0.5 |
9,430,482 | 8 | 11 |
8. An apparatus, comprising: a memory; and a processor coupled to the memory, the processor configured to receive an uncompressed template definition file (TDF) in a template file format and including an uncompressed set of attributes associated with a taxonomy of an online marketplace, the uncompressed set of attributes including a first attribute and a second attribute, the processor configured to compress the uncompressed TDF by defining an association between the first attribute and the second attribute with a third attribute based on each of the first attribute, the second attribute and the third attribute having a common characteristic to define a compressed TDF in the template file format and including a set of compressed attributes including the third attribute and excluding the first attribute and the second attribute, the processor configured to send the compressed TDF to a taxonomy platform such that a set of inventory data associated with a merchant is mapped, based on at least the compressed TDF, to the compressed set of attributes to define mapped inventory data, the processor configured to associate a value of the third attribute within the mapped inventory data with a value of the first attribute and a value of the second attribute, the processor configured to define, based on the mapped inventory data, a file including values for the uncompressed set of attributes, the values for the uncompressed set of attributes excluding the value for the third attribute and including the value for the first attribute and the value for the second attribute, the processor configured to send the file to the online marketplace such that the value for the first attribute and the value for the second attribute are mapped, based on at least the file, to the taxonomy of the online marketplace.
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8. An apparatus, comprising: a memory; and a processor coupled to the memory, the processor configured to receive an uncompressed template definition file (TDF) in a template file format and including an uncompressed set of attributes associated with a taxonomy of an online marketplace, the uncompressed set of attributes including a first attribute and a second attribute, the processor configured to compress the uncompressed TDF by defining an association between the first attribute and the second attribute with a third attribute based on each of the first attribute, the second attribute and the third attribute having a common characteristic to define a compressed TDF in the template file format and including a set of compressed attributes including the third attribute and excluding the first attribute and the second attribute, the processor configured to send the compressed TDF to a taxonomy platform such that a set of inventory data associated with a merchant is mapped, based on at least the compressed TDF, to the compressed set of attributes to define mapped inventory data, the processor configured to associate a value of the third attribute within the mapped inventory data with a value of the first attribute and a value of the second attribute, the processor configured to define, based on the mapped inventory data, a file including values for the uncompressed set of attributes, the values for the uncompressed set of attributes excluding the value for the third attribute and including the value for the first attribute and the value for the second attribute, the processor configured to send the file to the online marketplace such that the value for the first attribute and the value for the second attribute are mapped, based on at least the file, to the taxonomy of the online marketplace. 11. The apparatus of claim 8 , wherein the first attribute is at a first level of a taxonomy tree of the online marketplace, the second attribute is at a second level of the taxonomy tree different from the first level.
| 0.912888 |
7,689,404 | 4 | 5 |
4. The method of claim 3 wherein the multilingual dispatcher engine includes score scaling functions, which convert a hypothesis score from its language-specific scale to a multilingual dispatcher engine scale and from the multilingual dispatcher engine scale to a language-specific scale, these functions enabled as best-fit functions to generate equal multilingual dispatcher engine scores for silence and, optionally, noise hypotheses, in different languages on qualified application-dependent noise utterances.
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4. The method of claim 3 wherein the multilingual dispatcher engine includes score scaling functions, which convert a hypothesis score from its language-specific scale to a multilingual dispatcher engine scale and from the multilingual dispatcher engine scale to a language-specific scale, these functions enabled as best-fit functions to generate equal multilingual dispatcher engine scores for silence and, optionally, noise hypotheses, in different languages on qualified application-dependent noise utterances. 5. The method of claim 4 wherein the language-specific recognizers include a component computing a score adjustment for switching to another language after a given pronunciation and a component computing a score adjustment for switching from another language before a given pronunciation.
| 0.5 |
8,959,083 | 1 | 10 |
1. A computer-implemented method executed by one or more processors of a social video search system, the method comprising: receiving a query from a user; obtaining, by the one or more processors, social context information about interactions of the user from a social graph; obtaining, by the one or more processors, a search result using the query; modifying, by the one or more processors, a ranking of the search result based on an association with a particular source and a relation to the user to produce a modified search result; generating, by the one or more processors, an annotation for at least one search result where the annotation indicates the association with the particular source and the relation to the user; and providing, for display to the user, the modified search result and the annotation for presentation.
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1. A computer-implemented method executed by one or more processors of a social video search system, the method comprising: receiving a query from a user; obtaining, by the one or more processors, social context information about interactions of the user from a social graph; obtaining, by the one or more processors, a search result using the query; modifying, by the one or more processors, a ranking of the search result based on an association with a particular source and a relation to the user to produce a modified search result; generating, by the one or more processors, an annotation for at least one search result where the annotation indicates the association with the particular source and the relation to the user; and providing, for display to the user, the modified search result and the annotation for presentation. 10. The method of claim 1 further comprising sending a section divider that is a selectable link for retrieving additional results modified in rank by the social context information.
| 0.5 |
9,684,741 | 7 | 9 |
7. The method of claim 2 : at least one input component of the device comprising an audio input component; the query comprising an audio query received through an audio input component; at least one search engine comprising a textual search engine configured to handle textual queries; and the instructions configured to, after receiving the query through the audio input component and before executing the query on the textual search engine, transitioning the audio query to a textual query.
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7. The method of claim 2 : at least one input component of the device comprising an audio input component; the query comprising an audio query received through an audio input component; at least one search engine comprising a textual search engine configured to handle textual queries; and the instructions configured to, after receiving the query through the audio input component and before executing the query on the textual search engine, transitioning the audio query to a textual query. 9. The method of claim 7 , the instructions configured to, after recognizing the audio query to a textual query and before executing the query on the textual search engine, confirm the textual query with the user.
| 0.718254 |
9,600,231 | 14 | 15 |
14. The computing system of claim 13 , the set of actions further comprising: determining a first value of a first feature dimension, the first value associated with first audio data including a first keyword; determining a second value of the first feature dimension, the second value associated with second audio data not including a keyword; and determining a first utility metric for the first feature dimension using the first value and the second value.
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14. The computing system of claim 13 , the set of actions further comprising: determining a first value of a first feature dimension, the first value associated with first audio data including a first keyword; determining a second value of the first feature dimension, the second value associated with second audio data not including a keyword; and determining a first utility metric for the first feature dimension using the first value and the second value. 15. The computing system of claim 14 , the set of actions further comprising: processing a plurality of training audio data samples to determine a plurality of feature vectors, wherein each feature vector includes a value for each of a second plurality of feature dimensions and wherein the first plurality of feature dimensions is a subset of the second plurality of feature dimensions and the second plurality of feature dimensions includes the first feature dimension; determining a threshold utility; determining the first utility metric is above the threshold utility; and including the first feature dimension in the first plurality of feature dimensions.
| 0.5 |
9,916,350 | 17 | 18 |
17. A non-transitory computer-readable medium storing instruction code that when executed by a general purpose computer system causes the general purpose computer system to perform a method comprising: for each table of a plurality of database tables and for each column of a plurality of columns within the each table: determining a data type for the each column by accessing and analyzing a subset of values stored in the each column, determining a cardinality of the each column, and storing a profile for the each column, wherein the profile for the each column includes the determined data type and the determined cardinality; establishing a join graph comprising a plurality of nodes, wherein each node of the plurality of nodes represents one of the plurality of database tables; for each pair of a plurality of pairs of a first table and a second table from the plurality of database tables: for each pair of at least one pair of a first column from the first table and a second column from the second table: comparing the data type included in the profile for the first column with the data type included in the profile for the second column, determining that the data type included in the profile for the first column matches the data type included in the profile for the second column, calculating a joinability score based upon the profile for the first column and the profile for the second column, wherein calculating the joinability score comprises: retrieving the determined cardinality of the first column from the stored profile for the first column, retrieving the determined cardinality of the second column from the stored profile for the second column, determining a cardinality of a union between the first column and the second column, determining a cardinality of an intersection between the first column and the second column by subtracting the determined cardinality of the union from the sum of the determined cardinality of the first column and the determined cardinality of the second column, and dividing the determined cardinality of the intersection by the determined cardinality of the first column, and adding a directed weighted edge to the join graph from a node representing the first table to a node representing the second table, wherein the weight of the added edge is based on the joinability score; receiving a selection of a subset of the plurality of database tables; creating a join tree comprising a maximum weight subset of edges in the join graph that spans a subset of nodes in the join graph corresponding to the selected subset of the plurality of database tables; extracting a set of joins represented by the subset of edges; creating a database query based on the extracted set of joins; and executing the created database query to produce a query result.
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17. A non-transitory computer-readable medium storing instruction code that when executed by a general purpose computer system causes the general purpose computer system to perform a method comprising: for each table of a plurality of database tables and for each column of a plurality of columns within the each table: determining a data type for the each column by accessing and analyzing a subset of values stored in the each column, determining a cardinality of the each column, and storing a profile for the each column, wherein the profile for the each column includes the determined data type and the determined cardinality; establishing a join graph comprising a plurality of nodes, wherein each node of the plurality of nodes represents one of the plurality of database tables; for each pair of a plurality of pairs of a first table and a second table from the plurality of database tables: for each pair of at least one pair of a first column from the first table and a second column from the second table: comparing the data type included in the profile for the first column with the data type included in the profile for the second column, determining that the data type included in the profile for the first column matches the data type included in the profile for the second column, calculating a joinability score based upon the profile for the first column and the profile for the second column, wherein calculating the joinability score comprises: retrieving the determined cardinality of the first column from the stored profile for the first column, retrieving the determined cardinality of the second column from the stored profile for the second column, determining a cardinality of a union between the first column and the second column, determining a cardinality of an intersection between the first column and the second column by subtracting the determined cardinality of the union from the sum of the determined cardinality of the first column and the determined cardinality of the second column, and dividing the determined cardinality of the intersection by the determined cardinality of the first column, and adding a directed weighted edge to the join graph from a node representing the first table to a node representing the second table, wherein the weight of the added edge is based on the joinability score; receiving a selection of a subset of the plurality of database tables; creating a join tree comprising a maximum weight subset of edges in the join graph that spans a subset of nodes in the join graph corresponding to the selected subset of the plurality of database tables; extracting a set of joins represented by the subset of edges; creating a database query based on the extracted set of joins; and executing the created database query to produce a query result. 18. The computer-readable medium of claim 17 , wherein for each instance of determining a cardinality, the determining is performed by at least one of calculating an estimated cardinality and calculating an exact cardinality.
| 0.71875 |
8,898,151 | 1 | 8 |
1. A method of separating a set of related documents, the method comprising: determining, on a document selection system, quality scores for a plurality of the documents in the set of related documents based on comparisons with a predetermined value; obtaining a similarity score for a plurality of pairs of documents in the set of related document; and on the document selection system, obtaining a first subset of related documents which solves an optimization problem, the first subset of related documents being a subset of the set of related documents, the optimization problem being a function of one or more quality scores of the documents assigned to the first subset of related documents and one or more similarity scores of pairs of documents assigned to the first subset of related documents, wherein the optimization problem maximizes an evaluation function and wherein the evaluation function is: f ( A ) = ∑ v ∈ V u v ( v , A ( v ) ) + ∑ v 1 , v 2 ∈ E u E ( v 1 , v 2 , A ( v 1 ) , A ( v 2 ) ) where v is a document, A(v) is a labelling function which assigns a document, v, to either the first subset of related documents or a second subset of related documents, V is the set of related documents, u v (v,A(v)) is a function of the quality score of a document v, E is a set of all pairs of documents and u E (v 1 ,v 2 ,A(v 1 ),A(v 2 )) is a function of the similarly score between document v 1 and v 2 .
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1. A method of separating a set of related documents, the method comprising: determining, on a document selection system, quality scores for a plurality of the documents in the set of related documents based on comparisons with a predetermined value; obtaining a similarity score for a plurality of pairs of documents in the set of related document; and on the document selection system, obtaining a first subset of related documents which solves an optimization problem, the first subset of related documents being a subset of the set of related documents, the optimization problem being a function of one or more quality scores of the documents assigned to the first subset of related documents and one or more similarity scores of pairs of documents assigned to the first subset of related documents, wherein the optimization problem maximizes an evaluation function and wherein the evaluation function is: f ( A ) = ∑ v ∈ V u v ( v , A ( v ) ) + ∑ v 1 , v 2 ∈ E u E ( v 1 , v 2 , A ( v 1 ) , A ( v 2 ) ) where v is a document, A(v) is a labelling function which assigns a document, v, to either the first subset of related documents or a second subset of related documents, V is the set of related documents, u v (v,A(v)) is a function of the quality score of a document v, E is a set of all pairs of documents and u E (v 1 ,v 2 ,A(v 1 ),A(v 2 )) is a function of the similarly score between document v 1 and v 2 . 8. The method of claim 1 , wherein the similarity score for a pair of documents is determined based on the number of terms which are common to both documents in the pair.
| 0.93699 |
9,940,411 | 14 | 18 |
14. A computer implemented system of handling events generated by user interaction with a display that includes a first window and a second iframe that is not contained within the first window, the computer implemented system including: a processor, memory coupled to the processor, and program instructions stored in the memory that implement a method comprising: handling events generated by user interaction with a display that includes a first window and a second iframe that is not contained within the first window, including: generating a popup control of the first window responsive to a first user event representing user interaction with the first window; in circumstances when the user relocates a cursor from the first window to the second iframe and a focus event generated from a mouse or touch event within the second iframe is suppressed from propagation to the first window, receiving a blur event and a location of the blur event propagated from the second iframe, and responsive to the blur event in the second iframe, triggering a dismiss class within the first window, wherein the dismiss class dismisses at least a target portion of the generated popup control of the first window.
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14. A computer implemented system of handling events generated by user interaction with a display that includes a first window and a second iframe that is not contained within the first window, the computer implemented system including: a processor, memory coupled to the processor, and program instructions stored in the memory that implement a method comprising: handling events generated by user interaction with a display that includes a first window and a second iframe that is not contained within the first window, including: generating a popup control of the first window responsive to a first user event representing user interaction with the first window; in circumstances when the user relocates a cursor from the first window to the second iframe and a focus event generated from a mouse or touch event within the second iframe is suppressed from propagation to the first window, receiving a blur event and a location of the blur event propagated from the second iframe, and responsive to the blur event in the second iframe, triggering a dismiss class within the first window, wherein the dismiss class dismisses at least a target portion of the generated popup control of the first window. 18. The system of claim 14 , further including: displaying a trigger component within the popup control; and receiving an event that selects the trigger component and displaying a target component that displays a list of choices responsive to selection of the trigger; wherein the portion of the popup control that is dismissed by the dismiss class is the target component, leaving the trigger component active after shift of focus to the second iframe.
| 0.5 |
8,275,615 | 12 | 13 |
12. A translation method, comprising: recognizing an utterance using a plurality of models each being employed to decode the utterance to provide an output; assigning probabilities to the outputs based on an exponentiated and normalized loss accumulated over time; weighting the models based upon the assigned probabilities; using a processor to predict a best performing model based on the weighting of the outputs; and determining a combination hypothesis for the best performing model and applying at least one of past performance and user input as feedback to adjust the weights for translating a next utterance.
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12. A translation method, comprising: recognizing an utterance using a plurality of models each being employed to decode the utterance to provide an output; assigning probabilities to the outputs based on an exponentiated and normalized loss accumulated over time; weighting the models based upon the assigned probabilities; using a processor to predict a best performing model based on the weighting of the outputs; and determining a combination hypothesis for the best performing model and applying at least one of past performance and user input as feedback to adjust the weights for translating a next utterance. 13. The method as recited in claim 12 , wherein the user input includes at least one of selecting an output associated with one of the models and editing information provided by the user.
| 0.612033 |
9,418,218 | 8 | 13 |
8. An apparatus comprising: one or more processors; and a memory including instructions executable by the one or more processor to configure a web browser to: access a web page from a web server; parse the web page into a document object model (DOM); cause rendering of the DOM on a display as a rendered web page; use a browser extension to listen for one or more user action DOM events derived from interaction between the user and the rendered web page without causing a corresponding call made to the web server; in response to the browser extension detecting a user action DOM event derived from interaction between the user and the rendered web page without causing a corresponding call made to the web server, add information about the user action DOM event to a configurable element within the DOM; receive a request for an audit trail from a server; and return the information about the user action DOM event in the configurable element within the DOM to the server in response to the receiving of the request for an audit trail.
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8. An apparatus comprising: one or more processors; and a memory including instructions executable by the one or more processor to configure a web browser to: access a web page from a web server; parse the web page into a document object model (DOM); cause rendering of the DOM on a display as a rendered web page; use a browser extension to listen for one or more user action DOM events derived from interaction between the user and the rendered web page without causing a corresponding call made to the web server; in response to the browser extension detecting a user action DOM event derived from interaction between the user and the rendered web page without causing a corresponding call made to the web server, add information about the user action DOM event to a configurable element within the DOM; receive a request for an audit trail from a server; and return the information about the user action DOM event in the configurable element within the DOM to the server in response to the receiving of the request for an audit trail. 13. The apparatus of claim 8 , wherein the browser extension utilizes a list of one or more user action DOM events to listen for, the list corresponding to a security group in which the user is a member.
| 0.509662 |
8,831,403 | 1 | 3 |
1. A method, comprising: receiving a search query that includes one or more attributes; evaluating a plurality of video files, wherein when a specific search attribute is recognized in a particular video file, the particular video file may be divided into a plurality of video clips with at least one video clip having the recognized specific search attribute; identifying video clips within the video files that have one or more of the recognized specific search attributes; and creating a video report comprising a contiguous sequence of the identified video clips, wherein the video clips are stitched together according to a stitch criterion, wherein each video clip in the video report comprises an embedded link, which can be selected to access a corresponding video file that includes the particular video clip.
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1. A method, comprising: receiving a search query that includes one or more attributes; evaluating a plurality of video files, wherein when a specific search attribute is recognized in a particular video file, the particular video file may be divided into a plurality of video clips with at least one video clip having the recognized specific search attribute; identifying video clips within the video files that have one or more of the recognized specific search attributes; and creating a video report comprising a contiguous sequence of the identified video clips, wherein the video clips are stitched together according to a stitch criterion, wherein each video clip in the video report comprises an embedded link, which can be selected to access a corresponding video file that includes the particular video clip. 3. The method of claim 1 , further comprising: tagging the video files with tags corresponding to predefined attributes; and identifying the predefined attributes in response to the search query.
| 0.83777 |
6,122,658 | 22 | 23 |
22. The client computer of claim 19 wherein said global content includes a video stream.
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22. The client computer of claim 19 wherein said global content includes a video stream. 23. The client computer of claim 22 wherein said local content includes annotations associated with said video stream.
| 0.528 |
10,162,605 | 16 | 20 |
16. A method of completing a code snippet to define an object literal, the method comprising: providing a proxy object to a function that is included in code; performing global dynamic analysis by using a getter trap that is included in the proxy object to extract information regarding one or more properties of the object literal from the code, which includes the function; and generating a recommendation that recommends content for completion of the code snippet to define the object literal based at least in part on the information, the content identifying the one or more properties of the object literal.
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16. A method of completing a code snippet to define an object literal, the method comprising: providing a proxy object to a function that is included in code; performing global dynamic analysis by using a getter trap that is included in the proxy object to extract information regarding one or more properties of the object literal from the code, which includes the function; and generating a recommendation that recommends content for completion of the code snippet to define the object literal based at least in part on the information, the content identifying the one or more properties of the object literal. 20. The method of claim 16 , further comprising: determining that the object literal is to be a designated type selected from a plurality of types based at least in part on the information; wherein generating the recommendation that recommends the content for the completion of the code snippet comprises: indicating that the object literal is to be the designated type.
| 0.644914 |
8,655,650 | 9 | 10 |
9. A method for decoding data streams in a full-duplex voice communication system, comprising: receiving multiple sets of speech coding parameters, where each set of speech coding parameters was received over a different channel in the system; determining a weighting metric for each channel over which speech coding parameters were received; weighting the speech coding parameters using the weighting metric for the channel over which the parameters were received; summing weighted speech coding parameters to form a set of combined speech coding parameters; and outputting the set of combined speech coding parameters to a speech synthesizer.
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9. A method for decoding data streams in a full-duplex voice communication system, comprising: receiving multiple sets of speech coding parameters, where each set of speech coding parameters was received over a different channel in the system; determining a weighting metric for each channel over which speech coding parameters were received; weighting the speech coding parameters using the weighting metric for the channel over which the parameters were received; summing weighted speech coding parameters to form a set of combined speech coding parameters; and outputting the set of combined speech coding parameters to a speech synthesizer. 10. The method of claim 9 further comprises receiving two or more data streams having voice data encoded therein at a receiver, where each data stream corresponds to a channel in the system, and decoding each data stream into a set of speech coding parameters.
| 0.706546 |
8,484,022 | 16 | 17 |
16. An article of manufacture including a non-transitory computer-readable storage medium, having stored thereon program instructions that, upon execution by one or more processors of a system, cause the system to perform operations comprising: transforming an input audio training signal into a sequence of feature vectors, wherein each feature vector of the sequence bears quantitative measures of acoustic properties of the input audio training signal; processing the sequence of feature vectors with an auto-encoder to generate (i) an encoded form of the quantitative measures, and (ii) a recovered form of the quantitative measures based on an inverse operation by the auto-encoder on the encoded form of the quantitative measures; processing a duplicate copy of the sequence of feature vectors with a normalizer to generate a normalized form of the quantitative measures in which supra-phonetic acoustic properties of the input audio training signal are reduced in comparison with phonetic acoustic properties of the input audio training signal; determining an error signal based on a difference between the normalized form of the quantitative measures and the recovered form of the quantitative measures; providing the error signal to the auto-encoder; and by adjusting parameters of the auto-encoder to reduce the magnitude of the error signal, training the auto-encoder to compensate for supra-phonetic acoustic properties of input audio signals.
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16. An article of manufacture including a non-transitory computer-readable storage medium, having stored thereon program instructions that, upon execution by one or more processors of a system, cause the system to perform operations comprising: transforming an input audio training signal into a sequence of feature vectors, wherein each feature vector of the sequence bears quantitative measures of acoustic properties of the input audio training signal; processing the sequence of feature vectors with an auto-encoder to generate (i) an encoded form of the quantitative measures, and (ii) a recovered form of the quantitative measures based on an inverse operation by the auto-encoder on the encoded form of the quantitative measures; processing a duplicate copy of the sequence of feature vectors with a normalizer to generate a normalized form of the quantitative measures in which supra-phonetic acoustic properties of the input audio training signal are reduced in comparison with phonetic acoustic properties of the input audio training signal; determining an error signal based on a difference between the normalized form of the quantitative measures and the recovered form of the quantitative measures; providing the error signal to the auto-encoder; and by adjusting parameters of the auto-encoder to reduce the magnitude of the error signal, training the auto-encoder to compensate for supra-phonetic acoustic properties of input audio signals. 17. The article of manufacture of claim 16 , wherein the system comprises the auto-encoder and the normalizer, wherein the auto-encoder comprises a first sequential set of neural network layers adapted for forward processing of input, and a second sequential set of neural network layers adapted for inverse processing of input, and wherein processing the sequence of feature vectors with the auto-encoder comprises: forward processing the sequence of feature vectors with the first sequential set to generate the encoded form of the quantitative measures; and inverse processing the encoded form of the quantitative measures with the second sequential set to generate the recovered form of the quantitative measures.
| 0.5 |
7,606,785 | 24 | 25 |
24. The apparatus of claim 22 , wherein the instructions for generating from the first set of input information and the set of rules a t-box comprising categories and relationships about categories and an a-box comprising assertions of individual instances of the categories of the t-box comprise instructions for carrying out the steps of: reading the set of rules; generating a portion of t-box information from a portion of the set of rules; reading and parsing the input information to form an internal representation; analyzing the internal representation against at least one rule of the set of rules to generate a result comprising at least one of an instance, a property instance and a t-box statement; and outputting the result.
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24. The apparatus of claim 22 , wherein the instructions for generating from the first set of input information and the set of rules a t-box comprising categories and relationships about categories and an a-box comprising assertions of individual instances of the categories of the t-box comprise instructions for carrying out the steps of: reading the set of rules; generating a portion of t-box information from a portion of the set of rules; reading and parsing the input information to form an internal representation; analyzing the internal representation against at least one rule of the set of rules to generate a result comprising at least one of an instance, a property instance and a t-box statement; and outputting the result. 25. The apparatus of claim 24 , wherein the instructions for analyzing the internal representation comprise instructions for carrying out at least one of the steps of: walking a Document Object Model (“DOM”) tree and processing a Simple API for XML (“SAX”) event.
| 0.5 |
9,875,299 | 10 | 16 |
10. A method for identifying relevant search results via an index, comprising steps of: receiving a search query and generating a semantic representation for the query comprising a plurality of substructures as a semantic analysis of the search query and a list of key terms, wherein the key terms each comprise a term in the search query or a term related to one of the terms in the search query; accessing an inverted index comprising a set of key terms each associated with a semantic representation for each of a plurality of passages comprising a plurality of substructures and further associated with a link to a reference document for the passage; identifying one or more of the passages as retrieval candidates by comparing the semantic representations associated with the passages with the semantic representation of the search query, comprising: selecting a subset of the key terms from the search query and querying the inverted index with the key terms in the subset; identifying within the inverted index a result set for each of the key terms in the subset; and scoring each of the results in the set based on a distance of relationships of the substructures in the semantic representation of the search query and the semantic representation of the passages and identifying a subset of the result sets as the retrieval candidates based on the scoring; and selecting one or more of the retrieval candidates based on a comparison of the semantic representation of the search query with the semantic representations for each of the retrieval candidates.
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10. A method for identifying relevant search results via an index, comprising steps of: receiving a search query and generating a semantic representation for the query comprising a plurality of substructures as a semantic analysis of the search query and a list of key terms, wherein the key terms each comprise a term in the search query or a term related to one of the terms in the search query; accessing an inverted index comprising a set of key terms each associated with a semantic representation for each of a plurality of passages comprising a plurality of substructures and further associated with a link to a reference document for the passage; identifying one or more of the passages as retrieval candidates by comparing the semantic representations associated with the passages with the semantic representation of the search query, comprising: selecting a subset of the key terms from the search query and querying the inverted index with the key terms in the subset; identifying within the inverted index a result set for each of the key terms in the subset; and scoring each of the results in the set based on a distance of relationships of the substructures in the semantic representation of the search query and the semantic representation of the passages and identifying a subset of the result sets as the retrieval candidates based on the scoring; and selecting one or more of the retrieval candidates based on a comparison of the semantic representation of the search query with the semantic representations for each of the retrieval candidates. 16. A method according to claim 10 , comprising: defining search retrieval goals for the retrieval candidates comprising one or more of retrieving certain search terms, minimum relevance scores, inclusion of one or more search terms identified as necessary or significant, and identifying one of a minimum and maximum number of retrieval candidates.
| 0.5 |
7,993,372 | 1 | 2 |
1. A spinal implant system that is adapted to be mounted to a spine comprising: first and second anchors that are adapted to be secured to a spine; a first horizontal rod that is secured to the first and second anchors; a deflection rod system; a mount to mount the deflection rod system to the first horizontal rod; said deflection rod system including an inner rod and an outer shell; wherein said inner rod is elongated and said outer shell is elongated along the elongated inner rod; said deflection rod system includes at least one first end that is moveable relative to a second end in order to accommodate movement of a spine; at least one joint connection located at the first end of the deflection rod system; and a first vertical rod that is secured by said joint connection to said first horizontal rod.
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1. A spinal implant system that is adapted to be mounted to a spine comprising: first and second anchors that are adapted to be secured to a spine; a first horizontal rod that is secured to the first and second anchors; a deflection rod system; a mount to mount the deflection rod system to the first horizontal rod; said deflection rod system including an inner rod and an outer shell; wherein said inner rod is elongated and said outer shell is elongated along the elongated inner rod; said deflection rod system includes at least one first end that is moveable relative to a second end in order to accommodate movement of a spine; at least one joint connection located at the first end of the deflection rod system; and a first vertical rod that is secured by said joint connection to said first horizontal rod. 2. The system of claim 1 wherein said deflection rod system is about parallel to the first horizontal rod.
| 0.871046 |
8,768,913 | 13 | 15 |
13. A computer program product for multi-source searching for a data driven application, the computer program product comprising: a computer readable storage medium having computer readable program code embodied therewith, the computer readable program code comprising: computer readable program code for defining a form for a data driven application, the form comprising a plurality of user interface controls corresponding to respectively to different fields in different data sources; computer readable program code for generating a single search index for the form, the single search index including the different fields referenced in the form and also at least one data operation for the different fields referenced in the form; computer readable program code for coupling the form with a search user interface comprising a search control configured to accept at least one query term, the search control comprising a text control configured to link to one or more of the different fields of the single search index; and, computer readable program code for directing during execution of the data driven application a search engine query of the selection of the different fields in the single search index and not the different data sources according to the at least one query term provided in the search user interface without first requiring a join operation joining the different data sources.
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13. A computer program product for multi-source searching for a data driven application, the computer program product comprising: a computer readable storage medium having computer readable program code embodied therewith, the computer readable program code comprising: computer readable program code for defining a form for a data driven application, the form comprising a plurality of user interface controls corresponding to respectively to different fields in different data sources; computer readable program code for generating a single search index for the form, the single search index including the different fields referenced in the form and also at least one data operation for the different fields referenced in the form; computer readable program code for coupling the form with a search user interface comprising a search control configured to accept at least one query term, the search control comprising a text control configured to link to one or more of the different fields of the single search index; and, computer readable program code for directing during execution of the data driven application a search engine query of the selection of the different fields in the single search index and not the different data sources according to the at least one query term provided in the search user interface without first requiring a join operation joining the different data sources. 15. The computer program product of claim 13 , wherein at least one of the different data sources is separate and independent of the data driven application.
| 0.716606 |
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