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9,549,042 | 25 | 26 |
25. The mobile device of claim 24 , wherein the wireless features comprise Bluetooth® features, the Bluetooth® features include record features that comprise at least one of count features, entropy features and semantic features, and stability features, and the stability features comprise a measure of stability of neighboring information units based on at least one similarity metric.
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25. The mobile device of claim 24 , wherein the wireless features comprise Bluetooth® features, the Bluetooth® features include record features that comprise at least one of count features, entropy features and semantic features, and stability features, and the stability features comprise a measure of stability of neighboring information units based on at least one similarity metric. 26. The mobile device of claim 25 , wherein the processor is configured to execute the application to infer user context by at least one of segmenting the information units and smoothing the information units.
| 0.5 |
8,131,701 | 1 | 7 |
1. A computer-implemented method for probabilistically quantifying a likelihood or probability that a direct citational relationship exists between a first document and a second document based on one or more observed indirect citational relationships occurring between said first document and said second document, the method comprising: determining by a computer system said one or more indirect citational relationships between said first document and said second document; analyzing by the computer system said one or more indirect citational relationships to determine one or more generational citation counts between said first and second documents at generations higher than said direct first generation citational relationship; applying by the computer system a probability transform function to said one or more determined generational citation counts to determine or probabilistically quantify a relative event probability that said first document directly cites said second document or said second document directly cites said first document; calculating by the computer system a probability value based on the relative event probability that said first document directly cites said second document or said second document directly cites said first document, wherein the applying a probability transform function comprises providing said one or more determined generational citation counts as input predictor variables to a multi-variate regression model, said model being selected and adjusted to determine a relative event probability that said first document directly cites said second document or said second document directly cites said first document based on said one or more determined generational citation counts, wherein the computer system comprises at least a processor and a storage device.
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1. A computer-implemented method for probabilistically quantifying a likelihood or probability that a direct citational relationship exists between a first document and a second document based on one or more observed indirect citational relationships occurring between said first document and said second document, the method comprising: determining by a computer system said one or more indirect citational relationships between said first document and said second document; analyzing by the computer system said one or more indirect citational relationships to determine one or more generational citation counts between said first and second documents at generations higher than said direct first generation citational relationship; applying by the computer system a probability transform function to said one or more determined generational citation counts to determine or probabilistically quantify a relative event probability that said first document directly cites said second document or said second document directly cites said first document; calculating by the computer system a probability value based on the relative event probability that said first document directly cites said second document or said second document directly cites said first document, wherein the applying a probability transform function comprises providing said one or more determined generational citation counts as input predictor variables to a multi-variate regression model, said model being selected and adjusted to determine a relative event probability that said first document directly cites said second document or said second document directly cites said first document based on said one or more determined generational citation counts, wherein the computer system comprises at least a processor and a storage device. 7. The computer-implemented method of claim 1 , further comprising generating a search result set comprising two or more documents having one or more indirect citational relationships to said first or second documents and using said determined likelihood or probability of direct citation to rank or order said search result set.
| 0.746533 |
8,204,999 | 1 | 7 |
1. A method for providing access to a resource on a network, comprising the steps of: receiving at an access server an identification of said resource, said identification includes an absolute portion and a query portion; using said query portion to identify a first access rule from a plurality of access rules stored at said access server; determining whether access to said resource is authorized based on said identification of said resource without granting authorization to said resource, said determining including accessing said first access rule for said resource and accessing an identity profile for a first user to determine whether at least a portion of said first access rule is satisfied based on information in said identity profile, wherein said first access rule is not part of said identity profile; and allowing access to said resource, said step of allowing access is not performed if said first access rule is not satisfied.
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1. A method for providing access to a resource on a network, comprising the steps of: receiving at an access server an identification of said resource, said identification includes an absolute portion and a query portion; using said query portion to identify a first access rule from a plurality of access rules stored at said access server; determining whether access to said resource is authorized based on said identification of said resource without granting authorization to said resource, said determining including accessing said first access rule for said resource and accessing an identity profile for a first user to determine whether at least a portion of said first access rule is satisfied based on information in said identity profile, wherein said first access rule is not part of said identity profile; and allowing access to said resource, said step of allowing access is not performed if said first access rule is not satisfied. 7. The method according to claim 1 , wherein: said identification of said resource includes a URL; and said absolute portion includes a file name.
| 0.908291 |
9,251,225 | 36 | 37 |
36. The computing system of claim 30 , wherein the first source entity and the second source entity are related according to a relationship defined in a schema.
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36. The computing system of claim 30 , wherein the first source entity and the second source entity are related according to a relationship defined in a schema. 37. The computing system of claim 36 , wherein the schema includes multiple entities, and relationships between the entities include one or more of: a one-to-one relationship, a one-to-many relationship, or a many-to-many relationship.
| 0.5 |
9,256,676 | 1 | 3 |
1. A method of presenting computer-generated search result information, comprising: receiving a search request from a first user; obtaining a plurality of search results, wherein each search result identifies a respective search result document responsive to the search request; determining an initial ranking of the plurality of search results; determining that one or more particular web notebooks each have a respective title that matches the search request and that a particular search result document corresponding to a particular search result has document content related to respective notebook content in the one or more particular web notebooks of a plurality of web notebooks, wherein each web notebook has an author, the first user is not the author of any of the one or more particular web notebooks, and the related notebook content in each of the plurality of web notebooks includes web clippings taken by the author of the respective web notebook, wherein each of the web clippings is a snippet or a portion of a web document added to the respective web notebook by the author of the respective web notebook; modifying the ranking of the plurality of search results including modifying the ranking of the particular search result based on a web notebook based ranker and using the content from the one or more particular web notebooks; and providing a response to the search request including the plurality of search results for presentation in an order based on the modified ranking.
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1. A method of presenting computer-generated search result information, comprising: receiving a search request from a first user; obtaining a plurality of search results, wherein each search result identifies a respective search result document responsive to the search request; determining an initial ranking of the plurality of search results; determining that one or more particular web notebooks each have a respective title that matches the search request and that a particular search result document corresponding to a particular search result has document content related to respective notebook content in the one or more particular web notebooks of a plurality of web notebooks, wherein each web notebook has an author, the first user is not the author of any of the one or more particular web notebooks, and the related notebook content in each of the plurality of web notebooks includes web clippings taken by the author of the respective web notebook, wherein each of the web clippings is a snippet or a portion of a web document added to the respective web notebook by the author of the respective web notebook; modifying the ranking of the plurality of search results including modifying the ranking of the particular search result based on a web notebook based ranker and using the content from the one or more particular web notebooks; and providing a response to the search request including the plurality of search results for presentation in an order based on the modified ranking. 3. The method of claim 1 , further comprising identifying a first search result that is referenced in the plurality of web notebooks, and identifying a second search result that is not referenced in the plurality of web notebooks, wherein modifying the ranking of the plurality of search results comprises ranking the first search result higher than the second search result.
| 0.524112 |
9,058,331 | 3 | 4 |
3. The method of claim 1 , further comprising responsive to determining that the conversation exists, providing a user associated with the image with access to the conversation.
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3. The method of claim 1 , further comprising responsive to determining that the conversation exists, providing a user associated with the image with access to the conversation. 4. The method of claim 3 , further comprising generating a user interface that provides the user with an option to perform at least one of submitting a new comment to the conversation, viewing all comments associated with the conversation and posting a reply to an existing comment.
| 0.5 |
7,752,204 | 1 | 4 |
1. A method of summarizing a first unit of text data with relation to the contents of multiple documents in an existing document collection, comprising: creating a subspace for the existing document collection before a query and without using a pre-determined set of topics; performing one of a domain driven text summarization, an example type query driven text summarization, and a term type query driven text summarization on a selected document; recomposing a vector using a projection in the subspace representing the contents of multiple documents in the existing document collection when performing the domain driven text summarization or the example type query driven text summarization; computing term relationships representing similarities between query terms and the contents of multiple documents in the existing document collection using a term- term matrix associated with an original term space when performing the term type query driven text summarization; computing a term weight that is representative of the relevance of a term to a second unit of text data with relation to the contents of multiple documents in the document collection from recomposing of the vector using the projection in the subspace or the computing of the term relationships; comparing the computed term weight to a predetermined threshold; returning a relevant term based at least in part on a result of the comparison; summing a plurality of relevant term weights based on a number of occurrences of a plurality of corresponding relevant terms in a segment of the first unit of text data; comparing a plurality of summations based on a plurality of corresponding segments of the first unit of text data to identify a text summarization segment; and returning the text summarization segment.
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1. A method of summarizing a first unit of text data with relation to the contents of multiple documents in an existing document collection, comprising: creating a subspace for the existing document collection before a query and without using a pre-determined set of topics; performing one of a domain driven text summarization, an example type query driven text summarization, and a term type query driven text summarization on a selected document; recomposing a vector using a projection in the subspace representing the contents of multiple documents in the existing document collection when performing the domain driven text summarization or the example type query driven text summarization; computing term relationships representing similarities between query terms and the contents of multiple documents in the existing document collection using a term- term matrix associated with an original term space when performing the term type query driven text summarization; computing a term weight that is representative of the relevance of a term to a second unit of text data with relation to the contents of multiple documents in the document collection from recomposing of the vector using the projection in the subspace or the computing of the term relationships; comparing the computed term weight to a predetermined threshold; returning a relevant term based at least in part on a result of the comparison; summing a plurality of relevant term weights based on a number of occurrences of a plurality of corresponding relevant terms in a segment of the first unit of text data; comparing a plurality of summations based on a plurality of corresponding segments of the first unit of text data to identify a text summarization segment; and returning the text summarization segment. 4. The method of claim 1 , wherein the second unit of text data is a query.
| 0.959807 |
8,965,750 | 13 | 14 |
13. A device for providing language translation, the device comprising: a processor; a memory in electronic communication with the processor, the memory configured with instructions for: receiving a plurality of words in an input language; determining a linguistic structure of the plurality of words in the input language; determining a semantic relation among two or more of the plurality of words in the input language; determining the plurality of words in the input language using linguistic descriptions of the input language; translating the plurality of words in the input language to an expression in an output language; identifying at least one word of the plurality of words in the input language that has a syntactic ambiguity or a semantic ambiguity; displaying on a user interface two or more options corresponding to two or more meanings associated with said syntactic ambiguity or said semantic ambiguity; receiving an indication of selection of one or more of the two or more options corresponding to two or more meanings; displaying an indication of the selection of the one or more of the two or more options corresponding to said two or more meanings; searching a subject area of further options, the subject area corresponding to one or more respective subject areas corresponding to the said one or more of the two or more options; and displaying on the user interface one or more further options.
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13. A device for providing language translation, the device comprising: a processor; a memory in electronic communication with the processor, the memory configured with instructions for: receiving a plurality of words in an input language; determining a linguistic structure of the plurality of words in the input language; determining a semantic relation among two or more of the plurality of words in the input language; determining the plurality of words in the input language using linguistic descriptions of the input language; translating the plurality of words in the input language to an expression in an output language; identifying at least one word of the plurality of words in the input language that has a syntactic ambiguity or a semantic ambiguity; displaying on a user interface two or more options corresponding to two or more meanings associated with said syntactic ambiguity or said semantic ambiguity; receiving an indication of selection of one or more of the two or more options corresponding to two or more meanings; displaying an indication of the selection of the one or more of the two or more options corresponding to said two or more meanings; searching a subject area of further options, the subject area corresponding to one or more respective subject areas corresponding to the said one or more of the two or more options; and displaying on the user interface one or more further options. 14. The device of claim 13 , wherein displaying the indication of the selection of the one or more of the two or more options corresponding to said two or more meanings includes changing, to one or more new words, the identified at least one word of the plurality of words in the output language.
| 0.5 |
8,549,479 | 1 | 3 |
1. A method of testing a registry, the method comprising: obtaining a command phrase specifying an operation, a protocol, and an object, wherein the command phrase is in a natural language based syntax; obtaining one or more parameters associated with the command phrase; determining that one or more additional parameters are associated with the command phrase; obtaining the one or more additional parameters from a database; translating, using one or more processors, the command phrase, the one or more parameters, and the one or more additional parameters into an XML command; transmitting the XML command to the registry; obtaining a response from the registry; and determining that the response corresponds to an anticipated response; wherein the one or mere parameters comprise a variable.
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1. A method of testing a registry, the method comprising: obtaining a command phrase specifying an operation, a protocol, and an object, wherein the command phrase is in a natural language based syntax; obtaining one or more parameters associated with the command phrase; determining that one or more additional parameters are associated with the command phrase; obtaining the one or more additional parameters from a database; translating, using one or more processors, the command phrase, the one or more parameters, and the one or more additional parameters into an XML command; transmitting the XML command to the registry; obtaining a response from the registry; and determining that the response corresponds to an anticipated response; wherein the one or mere parameters comprise a variable. 3. The method of claim 1 , wherein the operation comprises at least one of add, delete, and update domain.
| 0.618705 |
7,542,979 | 7 | 9 |
7. A computer system in accordance with claim 6 , wherein the entity rule is comprised of a condition and an attributelist.
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7. A computer system in accordance with claim 6 , wherein the entity rule is comprised of a condition and an attributelist. 9. A computer system in accordance with claim 7 , wherein the entity rule includes a referenceentity clause.
| 0.5 |
6,014,616 | 17 | 20 |
17. A process for identifying a type of symbols generated by an operating system, comprising the steps of: matching a first set of data corresponding to a first language currently used by said operating system to generate a plurality of symbols with a plurality of signals sendable by an input device, said matching allowing a user to generate a plurality of symbols by manipulating an input device, said operating system running on a computer system comprising a central processing unit, said input device and a display device; displaying a cursor on said display device, said cursor having a color signifying that said first language is currently being used by said operating system to match said symbols with said signals sent by said input device; receiving said signals from said input device; deteimining whether said signals received from said input device include a command to change from using said first language to using a second language to match said symbols with said signals sent by said input device; if said signals include said command to change from said first language to said second language: matching a second set of data corresponding to said second language to said signals sendable by said input device; changing said color of said cursor on said display device to signify that said second language is currently being used to generate said symbols in response to manipulation of said input device; and returning to said step of receiving said signals from said input device; and if said signals do not include a command to change from said first language to said second language: displaying said symbols of said first language corresponding to said signals sent from said input device on said display device; moving said cursor; and returning to said step of displaying a cursor on said display device, said cursor having a color signifying said first language.
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17. A process for identifying a type of symbols generated by an operating system, comprising the steps of: matching a first set of data corresponding to a first language currently used by said operating system to generate a plurality of symbols with a plurality of signals sendable by an input device, said matching allowing a user to generate a plurality of symbols by manipulating an input device, said operating system running on a computer system comprising a central processing unit, said input device and a display device; displaying a cursor on said display device, said cursor having a color signifying that said first language is currently being used by said operating system to match said symbols with said signals sent by said input device; receiving said signals from said input device; deteimining whether said signals received from said input device include a command to change from using said first language to using a second language to match said symbols with said signals sent by said input device; if said signals include said command to change from said first language to said second language: matching a second set of data corresponding to said second language to said signals sendable by said input device; changing said color of said cursor on said display device to signify that said second language is currently being used to generate said symbols in response to manipulation of said input device; and returning to said step of receiving said signals from said input device; and if said signals do not include a command to change from said first language to said second language: displaying said symbols of said first language corresponding to said signals sent from said input device on said display device; moving said cursor; and returning to said step of displaying a cursor on said display device, said cursor having a color signifying said first language. 20. The process of claim 17, said operating system being one of a WINDOWS 3.1 operating system and a WINDOWS 95 operating system and their derivatives.
| 0.709615 |
10,120,865 | 1 | 4 |
1. A system, comprising: at least one hardware processor; and at least one non-transitory computer-readable storage medium storing instructions that, when executed by the at least one hardware processor, cause the at least one hardware processor to perform: accessing information specifying at least one referential expression for a first referent and at least one anaphoric expression for the first referent; accessing a template including human-language text, a first tag that serves as a placeholder for a first text portion including a first reference to the first referent, a second tag that precedes the first tag in the template and serves as a placeholder for a second text portion including a second reference to a second referent different from the first referent, and a third tag that precedes the second tag in the template and serves as a placeholder for a third text portion including a third reference to the first referent; determining whether using the at least one anaphoric expression for the first text portion would result in the insertion of an ambiguous reference in the output text by determining whether the first and second referents are of the same type, whether the first and second referents are mutually ambiguous, or whether the first and second referents are of the same type and whether the first and second referents are mutually ambiguous; identifying text to use for the first text portion based on results of determining whether using the at least one anaphoric expression would result in the insertion of an ambiguous reference in the output text, the identifying comprising: when it is determined that using the at least one anaphoric expression as the text for the first text portion would result in the insertion of an ambiguous reference, making a determination to use the at least one referential expression as the text for the first text portion; and when it is determined that using the at least one anaphoric expression as the text for the first text portion would not result in the insertion of an ambiguous reference, making a determination to use the at least one anaphoric expression as the text for the first text portion; automatically generating output text including the human-language text and the identified text for the first text portion; and presenting, via a device, the automatically generated output text to a user.
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1. A system, comprising: at least one hardware processor; and at least one non-transitory computer-readable storage medium storing instructions that, when executed by the at least one hardware processor, cause the at least one hardware processor to perform: accessing information specifying at least one referential expression for a first referent and at least one anaphoric expression for the first referent; accessing a template including human-language text, a first tag that serves as a placeholder for a first text portion including a first reference to the first referent, a second tag that precedes the first tag in the template and serves as a placeholder for a second text portion including a second reference to a second referent different from the first referent, and a third tag that precedes the second tag in the template and serves as a placeholder for a third text portion including a third reference to the first referent; determining whether using the at least one anaphoric expression for the first text portion would result in the insertion of an ambiguous reference in the output text by determining whether the first and second referents are of the same type, whether the first and second referents are mutually ambiguous, or whether the first and second referents are of the same type and whether the first and second referents are mutually ambiguous; identifying text to use for the first text portion based on results of determining whether using the at least one anaphoric expression would result in the insertion of an ambiguous reference in the output text, the identifying comprising: when it is determined that using the at least one anaphoric expression as the text for the first text portion would result in the insertion of an ambiguous reference, making a determination to use the at least one referential expression as the text for the first text portion; and when it is determined that using the at least one anaphoric expression as the text for the first text portion would not result in the insertion of an ambiguous reference, making a determination to use the at least one anaphoric expression as the text for the first text portion; automatically generating output text including the human-language text and the identified text for the first text portion; and presenting, via a device, the automatically generated output text to a user. 4. The system of claim 1 , wherein the at least one anaphoric expression includes an anaphoric expression that is not a pronoun.
| 0.875728 |
9,355,636 | 15 | 16 |
15. One or more non-transitory computer readable media comprising executable code that, when executed, cause one or more computing devices to perform a process comprising: obtaining audio data regarding an utterance; determining one or more articulatory features associated with a portion of the audio data; identifying a subset of a plurality of acoustic model components based at least on the one or more articulatory features; computing a score, using the subset, for a feature vector for the portion of audio data; and generating automatic speech recognition results based at least on the score computed for the feature vector, wherein the automatic speech recognition results comprise one or more transcriptions corresponding to the utterance.
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15. One or more non-transitory computer readable media comprising executable code that, when executed, cause one or more computing devices to perform a process comprising: obtaining audio data regarding an utterance; determining one or more articulatory features associated with a portion of the audio data; identifying a subset of a plurality of acoustic model components based at least on the one or more articulatory features; computing a score, using the subset, for a feature vector for the portion of audio data; and generating automatic speech recognition results based at least on the score computed for the feature vector, wherein the automatic speech recognition results comprise one or more transcriptions corresponding to the utterance. 16. The one or more non-transitory computer readable media of claim 15 , wherein at least one of the one or more articulatory features indicates one or more of whether a sound is a consonant or vowel, is voiced, is a fricative, is bilabial, or is labiodental.
| 0.599071 |
6,166,739 | 1 | 3 |
1. A method for navigating a group of information in a computer system, comprising the steps of: identifying said group of information associated with a selected data item; converting certain pieces of said group information into generated data items; associating unilateral and multilateral display relationships with said generated data items, each unilateral display relationship representing a direct relationship between two of said generated data items, and said multilateral relationships also representing an indirect relationship with other generated data items; associating unilateral and multilateral display relationships with said generated data items and said selected data item, each unilateral display relationship representing a direct relationship between said selected data item and one of said generated data items, and said multilateral relationships also representing an indirect relationship between said selected data item and said generated data items; and forming a display associated with said selected data item, including said generated data items, wherein said display distinguishes between said selected data item and said generated data items.
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1. A method for navigating a group of information in a computer system, comprising the steps of: identifying said group of information associated with a selected data item; converting certain pieces of said group information into generated data items; associating unilateral and multilateral display relationships with said generated data items, each unilateral display relationship representing a direct relationship between two of said generated data items, and said multilateral relationships also representing an indirect relationship with other generated data items; associating unilateral and multilateral display relationships with said generated data items and said selected data item, each unilateral display relationship representing a direct relationship between said selected data item and one of said generated data items, and said multilateral relationships also representing an indirect relationship between said selected data item and said generated data items; and forming a display associated with said selected data item, including said generated data items, wherein said display distinguishes between said selected data item and said generated data items. 3. The method claimed in claim 1, wherein said group of information comprises a hierarchical group of information.
| 0.801394 |
8,965,776 | 10 | 13 |
10. A method comprising: receiving, from an optical receiver and by a device, a word, of a block of words within traffic, on which to perform forward error correction, the word including encoded bits and sets of reliability bits for identifying a respective level of reliability of each of the encoded bits; updating, by the device, a first segment of the word and a second segment of the word based on first extrinsic information, the first segment including a first subset of the encoded bits, the second segment including a second subset of the encoded bits, and the first extrinsic information modifying reliability bits associated with the first subset and the second subset; identifying, by the device, first least reliable positions (LRPs) within the updated first segment and second LRPs within the updated second segment, the first LRPs corresponding to first bits within the first subset with a lowest level of reliability, the second LRPs corresponding to second bits within the second subset with another lowest level of reliability; identifying, by the device, LRPs, within the word, based on a subset of a combination of the first bits and the second bits associated with one or more lowest levels of reliability; generating, by the device, candidate words based on different combinations of inverted encoded bits, within the word, that corresponds to the identified LRPs; identifying, by the device, errors within the word or the candidate words; updating, by the device and based on the first extrinsic information, a first encoded bit, within the first segment, in which an error is identified; determining, by the device, first partial distances between the updated first segment and first segments of the candidate words and second partial distances between the second segment and second segments of the candidate words; generating, by the device, second extrinsic information based on two or more best words of the candidate words, the two or more best words corresponding to two or more shortest first partial distances and two or more shortest second partial distances; and processing, by the device, a next word, of the block of words, using the second extrinsic information.
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10. A method comprising: receiving, from an optical receiver and by a device, a word, of a block of words within traffic, on which to perform forward error correction, the word including encoded bits and sets of reliability bits for identifying a respective level of reliability of each of the encoded bits; updating, by the device, a first segment of the word and a second segment of the word based on first extrinsic information, the first segment including a first subset of the encoded bits, the second segment including a second subset of the encoded bits, and the first extrinsic information modifying reliability bits associated with the first subset and the second subset; identifying, by the device, first least reliable positions (LRPs) within the updated first segment and second LRPs within the updated second segment, the first LRPs corresponding to first bits within the first subset with a lowest level of reliability, the second LRPs corresponding to second bits within the second subset with another lowest level of reliability; identifying, by the device, LRPs, within the word, based on a subset of a combination of the first bits and the second bits associated with one or more lowest levels of reliability; generating, by the device, candidate words based on different combinations of inverted encoded bits, within the word, that corresponds to the identified LRPs; identifying, by the device, errors within the word or the candidate words; updating, by the device and based on the first extrinsic information, a first encoded bit, within the first segment, in which an error is identified; determining, by the device, first partial distances between the updated first segment and first segments of the candidate words and second partial distances between the second segment and second segments of the candidate words; generating, by the device, second extrinsic information based on two or more best words of the candidate words, the two or more best words corresponding to two or more shortest first partial distances and two or more shortest second partial distances; and processing, by the device, a next word, of the block of words, using the second extrinsic information. 13. The method of claim 10 , further comprising: identifying one of the first partial distances based on one of: identifying a quantity of bits, associated with a first segment of a first candidate word, that do not match encoded bits associated with the updated first segment; or identifying a difference between a first sum of reliability values that correspond to encoded bits associated with the first segment of the first candidate word and a second sum of reliability values that correspond to encoded bits associated with the first updated segment; and selecting the first segment of the first candidate word on which to base one of the two or more best words when the one of the first partial distances is less than other ones of the first partial distances.
| 0.5 |
9,436,951 | 1 | 11 |
1. A computer-implemented method of presenting additional content for a term presented by a first mobile communication device, the computer-implemented method comprising: receiving, by the first mobile communication device, a first utterance; transmitting, by the first mobile communication device, a first identifier of the first mobile communication device and the first utterance to a server; receiving, by the first mobile communication device from the server, text representing a transcription of the first utterance; receiving, by the first mobile communication device from the server, an indicator that first additional content is available for a term identified within the text by the indicator, wherein the term is associated at the server with the first identifier of the first mobile communication device and a second identifier of a second mobile communication device, and wherein the first additional content for the term is associated with the first identifier and the second identifier; presenting, on the first mobile communication device, the text with an emphasis on the term identified by the indicator; after presenting the text on the first mobile communication device, receiving, by the first mobile communication device, a second utterance that includes the term; transmitting, by the first mobile communication device, the first identifier and the second utterance to the server; receiving, by the first mobile communication device from the server, in response to transmitting the second utterance and the first identifier, the first additional content; presenting, on the first mobile communication device the first additional content for the term; transmitting, by the first mobile communication device, the second identifier to the server, the server configured to send the text as well as the indicator that first additional content is available for the term to the second mobile communication device using the second identifier of the second mobile communication device; and receiving, by the first mobile communication device from the server, a message including a transcribed third utterance received by the second communication device in response to the text.
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1. A computer-implemented method of presenting additional content for a term presented by a first mobile communication device, the computer-implemented method comprising: receiving, by the first mobile communication device, a first utterance; transmitting, by the first mobile communication device, a first identifier of the first mobile communication device and the first utterance to a server; receiving, by the first mobile communication device from the server, text representing a transcription of the first utterance; receiving, by the first mobile communication device from the server, an indicator that first additional content is available for a term identified within the text by the indicator, wherein the term is associated at the server with the first identifier of the first mobile communication device and a second identifier of a second mobile communication device, and wherein the first additional content for the term is associated with the first identifier and the second identifier; presenting, on the first mobile communication device, the text with an emphasis on the term identified by the indicator; after presenting the text on the first mobile communication device, receiving, by the first mobile communication device, a second utterance that includes the term; transmitting, by the first mobile communication device, the first identifier and the second utterance to the server; receiving, by the first mobile communication device from the server, in response to transmitting the second utterance and the first identifier, the first additional content; presenting, on the first mobile communication device the first additional content for the term; transmitting, by the first mobile communication device, the second identifier to the server, the server configured to send the text as well as the indicator that first additional content is available for the term to the second mobile communication device using the second identifier of the second mobile communication device; and receiving, by the first mobile communication device from the server, a message including a transcribed third utterance received by the second communication device in response to the text. 11. The method of claim 1 , wherein the first additional content is presented in a popup window of the first mobile communication device.
| 0.849119 |
9,778,973 | 8 | 9 |
8. A computer program product for providing early diagnosis of hardware, software or configuration problems in a data warehouse system, the computer program product comprising a computer readable storage medium having program code embodied therewith, the program code comprising the programming instructions for: receiving a query from a client device; parsing said query to determine properties of said query, wherein said properties comprise the following: user defined functions, and tables scanned during execution of said query and user which executes said query; joining said query to one or more groups of queries with shared properties of said query; executing said query according to an execution plan; receiving results from said execution of said query in all groups associated with said query, wherein said results indicate a problem during said execution of said query; ranking said problem in each of said one or more groups of queries joined by said query based on severity of said problem, and priority of said query and number of other queries that are impacted; and reporting said problem in one or more of said one or more groups of queries to a user in response to said problem reaching a pre-defined threshold of becoming a group problem in said one or more of said one or more groups of queries based on said ranking of said problem.
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8. A computer program product for providing early diagnosis of hardware, software or configuration problems in a data warehouse system, the computer program product comprising a computer readable storage medium having program code embodied therewith, the program code comprising the programming instructions for: receiving a query from a client device; parsing said query to determine properties of said query, wherein said properties comprise the following: user defined functions, and tables scanned during execution of said query and user which executes said query; joining said query to one or more groups of queries with shared properties of said query; executing said query according to an execution plan; receiving results from said execution of said query in all groups associated with said query, wherein said results indicate a problem during said execution of said query; ranking said problem in each of said one or more groups of queries joined by said query based on severity of said problem, and priority of said query and number of other queries that are impacted; and reporting said problem in one or more of said one or more groups of queries to a user in response to said problem reaching a pre-defined threshold of becoming a group problem in said one or more of said one or more groups of queries based on said ranking of said problem. 9. The computer program product as recited in claim 8 , wherein the program code further comprises the programming instructions for: creating a new group of queries associated with said properties of said query in response to said properties not being associated with any existing groups of queries.
| 0.657895 |
5,539,427 | 1 | 13 |
1. A method for indexing at least one segment of at least one stored document, said method comprising the steps of: displaying a first document on a display; selecting a first segment from said displayed first document using a selecting device, said first segment having first coordinates and bounds; selecting a first index from storage; storing said first coordinates and bounds of said first segment in said first index; retrieving said first index, said first index containing said coordinates and bounds of said first segment; and displaying said first segment as an indexing entry within said first index by using said coordinates and bounds of said first segment to retrieve said first segment from said first document.
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1. A method for indexing at least one segment of at least one stored document, said method comprising the steps of: displaying a first document on a display; selecting a first segment from said displayed first document using a selecting device, said first segment having first coordinates and bounds; selecting a first index from storage; storing said first coordinates and bounds of said first segment in said first index; retrieving said first index, said first index containing said coordinates and bounds of said first segment; and displaying said first segment as an indexing entry within said first index by using said coordinates and bounds of said first segment to retrieve said first segment from said first document. 13. The method of claim 1 wherein said coordinates and bounds are the (x,y) coordinates of two opposite vertices of said first segment.
| 0.917482 |
8,301,356 | 1 | 3 |
1. Method for estimating NOx creation in a combustion process of a four-stroke internal combustion engine including a variable volume combustion chamber defined by a piston reciprocating within a cylinder between top-dead center and bottom-dead center points, intake and exhaust passages, and intake and exhaust valves controlled during repetitive, sequential exhaust, intake, compression and expansion strokes of said piston, comprising: monitoring engine sensor inputs comprising a cylinder pressure within the combustion chamber; modeling a mass fraction burn value for combustion within the combustion chamber based upon said engine sensor inputs, wherein said mass fraction burn value indexes a crank angle at which a selected percentage of injected fuel is burned in a combustion cycle; estimating a state of combustion within the combustion chamber based upon the mass fraction burn value, the state of combustion comprising a combustion phasing and a combustion strength; and estimating NOx creation within the combustion chamber with an artificial neural network based upon said state of combustion.
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1. Method for estimating NOx creation in a combustion process of a four-stroke internal combustion engine including a variable volume combustion chamber defined by a piston reciprocating within a cylinder between top-dead center and bottom-dead center points, intake and exhaust passages, and intake and exhaust valves controlled during repetitive, sequential exhaust, intake, compression and expansion strokes of said piston, comprising: monitoring engine sensor inputs comprising a cylinder pressure within the combustion chamber; modeling a mass fraction burn value for combustion within the combustion chamber based upon said engine sensor inputs, wherein said mass fraction burn value indexes a crank angle at which a selected percentage of injected fuel is burned in a combustion cycle; estimating a state of combustion within the combustion chamber based upon the mass fraction burn value, the state of combustion comprising a combustion phasing and a combustion strength; and estimating NOx creation within the combustion chamber with an artificial neural network based upon said state of combustion. 3. The method of claim 1 , wherein said modeling said mass fraction burn value comprises calculating a total heat released for a given crank angle based upon said cylinder pressure.
| 0.784524 |
9,183,499 | 10 | 12 |
10. A system comprising: a data processing apparatus; and a data store storing instructions that, when executed by the data processing apparatus, cause the data processing apparatus to perform operations comprising: obtaining a first quality model that was trained using a first set of training entities; identifying a set of candidate entities, where each candidate entity is different from each of the training entities; for each candidate entity in the set of candidate entities: obtaining a first quality score for the candidate entity; obtaining one or more neighbor features for neighbor entities of the candidate entity, where each neighbor entity of the candidate entity is an entity that is linked to the candidate entity; obtaining one or more entity specific feature values for the candidate entity, where each entity specific feature value is determined independent of the neighbor entities of the candidate entity; and determining a second quality score for the candidate entity using the first quality model, the second quality score being computed based on the first quality score, the neighbor features, and the entity specific feature values.
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10. A system comprising: a data processing apparatus; and a data store storing instructions that, when executed by the data processing apparatus, cause the data processing apparatus to perform operations comprising: obtaining a first quality model that was trained using a first set of training entities; identifying a set of candidate entities, where each candidate entity is different from each of the training entities; for each candidate entity in the set of candidate entities: obtaining a first quality score for the candidate entity; obtaining one or more neighbor features for neighbor entities of the candidate entity, where each neighbor entity of the candidate entity is an entity that is linked to the candidate entity; obtaining one or more entity specific feature values for the candidate entity, where each entity specific feature value is determined independent of the neighbor entities of the candidate entity; and determining a second quality score for the candidate entity using the first quality model, the second quality score being computed based on the first quality score, the neighbor features, and the entity specific feature values. 12. The system of claim 10 , wherein the first quality model was trained based on training features associated with the training entities, where the training features for each training entity include: a given quality score for the training entity; at least one neighbor quality score for at least one neighbor entity of the training entity; and at least one entity specific feature value of the training entity.
| 0.669082 |
8,731,934 | 1 | 12 |
1. A system for data-mining a monitored telephone conversation, the system comprising: one or more microprocessors; a memory; a speech recognition processor configured to transcribe the monitored telephone conversation and associate a plurality of characteristics of the monitored telephone conversation with a transcript of the monitored telephone conversation; an event detection processor configured to analyze the transcript of the monitored telephone conversation to detect one or more events within the monitored telephone conversation following the transcription of the monitored telephone conversation; a topic database configured to include a slang term associated with both a major topic and a minor topic; a topic detection processor configured to determine a topic of the monitored telephone conversation based upon querying the topic database with one or more extracted phrases from the transcript; a tagging processor configured to associate metadata information indicative of the detected one or more events with the transcript of the monitored telephone conversation; and a multimedia data warehouse configured to store at least the transcript of the monitored telephone conversation and the metadata information and characteristics associated therewith as a data record for the monitored telephone conversation.
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1. A system for data-mining a monitored telephone conversation, the system comprising: one or more microprocessors; a memory; a speech recognition processor configured to transcribe the monitored telephone conversation and associate a plurality of characteristics of the monitored telephone conversation with a transcript of the monitored telephone conversation; an event detection processor configured to analyze the transcript of the monitored telephone conversation to detect one or more events within the monitored telephone conversation following the transcription of the monitored telephone conversation; a topic database configured to include a slang term associated with both a major topic and a minor topic; a topic detection processor configured to determine a topic of the monitored telephone conversation based upon querying the topic database with one or more extracted phrases from the transcript; a tagging processor configured to associate metadata information indicative of the detected one or more events with the transcript of the monitored telephone conversation; and a multimedia data warehouse configured to store at least the transcript of the monitored telephone conversation and the metadata information and characteristics associated therewith as a data record for the monitored telephone conversation. 12. The system according to claim 1 , wherein the topic database includes at least one domain-specific Hidden Markov Model and at least one domain-specific topic language model, and wherein the topic detection processor is configured to perform automatic topic annotation of the monitored telephone conversation.
| 0.711645 |
9,135,912 | 1 | 8 |
1. A computer-implemented method comprising: identifying, from among a set of query terms, a particular query term that (i) does not occur in a lexicon of terms, and (ii) has no designated, canonical phonetic representation in a pronunciation phonetic dictionary, wherein a canonical phonetic representation comprises a sequence of phonemes; generating a phonetic representation estimate for the particular query term that (i) does not occur in the lexicon of terms, and (ii) has no designated, canonical phonetic representation in the pronunciation phonetic dictionary; transmitting data identifying at least a portion of a term that does occur in the lexicon of terms and the particular query term to a spelling correction server; receiving, from the spelling correction server, data that specifies a spelling correction confidence score, wherein the spelling correction confidence score reflects a probability that the term that does occur in the lexicon of terms is a correct spelling of the particular query term; determining that the spelling correction confidence score satisfies a predetermined threshold; and in response to determining that the spelling correction confidence score satisfies a predetermined threshold, designating, by one or more computing devices, the phonetic representation estimate for the particular query term as a canonical phonetic representation, in the phonetic dictionary, of the term that does occur in the lexicon of terms.
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1. A computer-implemented method comprising: identifying, from among a set of query terms, a particular query term that (i) does not occur in a lexicon of terms, and (ii) has no designated, canonical phonetic representation in a pronunciation phonetic dictionary, wherein a canonical phonetic representation comprises a sequence of phonemes; generating a phonetic representation estimate for the particular query term that (i) does not occur in the lexicon of terms, and (ii) has no designated, canonical phonetic representation in the pronunciation phonetic dictionary; transmitting data identifying at least a portion of a term that does occur in the lexicon of terms and the particular query term to a spelling correction server; receiving, from the spelling correction server, data that specifies a spelling correction confidence score, wherein the spelling correction confidence score reflects a probability that the term that does occur in the lexicon of terms is a correct spelling of the particular query term; determining that the spelling correction confidence score satisfies a predetermined threshold; and in response to determining that the spelling correction confidence score satisfies a predetermined threshold, designating, by one or more computing devices, the phonetic representation estimate for the particular query term as a canonical phonetic representation, in the phonetic dictionary, of the term that does occur in the lexicon of terms. 8. The method of claim 1 , comprising: receiving a data representation of an utterance in which a user has spoken the term that does occur in the lexicon of terms; and using the phonetic representation estimate for the particular query term, in outputting the term that does occur in the lexicon of terms as part of a transcription of the utterance.
| 0.733994 |
8,788,525 | 23 | 30 |
23. A computer-program product, tangibly embodied in one or more non-transitory machine-readable media, including instructions configured to cause one or more data processing apparatuses to: access time stamped events in a data store on a computing device including one or more processors, wherein the set of events are searchable; maintain a data model that is associated with a set of the time stamped events, wherein the data model defines a schema to apply to the set of the time stamped events, wherein the data model includes one or more sub-models, and wherein each sub-model of the one or more sub-models is associated with a subset of events in the set of the time stamped events, the subset of events being smaller than the set of the time stamped events; cause display of a graphical interface that lists the one or more sub-models of the data model; receive first input corresponding to a selection of a particular sub-model of the one or more sub-models through the graphical interface; responsive to the first input, narrow the set of the time stamped events that are searchable to a particular subset of events that is associated with the selected particular sub-model; subsequent to receiving the first input, receive second input corresponding to criteria for a search query; after receiving the second input, initiating a search that uses the received criteria to evaluate values extracted using an extraction rule or a regular expression from events in the particular subset of events, wherein the extraction rule or the regular expression corresponds to a field in the schema.
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23. A computer-program product, tangibly embodied in one or more non-transitory machine-readable media, including instructions configured to cause one or more data processing apparatuses to: access time stamped events in a data store on a computing device including one or more processors, wherein the set of events are searchable; maintain a data model that is associated with a set of the time stamped events, wherein the data model defines a schema to apply to the set of the time stamped events, wherein the data model includes one or more sub-models, and wherein each sub-model of the one or more sub-models is associated with a subset of events in the set of the time stamped events, the subset of events being smaller than the set of the time stamped events; cause display of a graphical interface that lists the one or more sub-models of the data model; receive first input corresponding to a selection of a particular sub-model of the one or more sub-models through the graphical interface; responsive to the first input, narrow the set of the time stamped events that are searchable to a particular subset of events that is associated with the selected particular sub-model; subsequent to receiving the first input, receive second input corresponding to criteria for a search query; after receiving the second input, initiating a search that uses the received criteria to evaluate values extracted using an extraction rule or a regular expression from events in the particular subset of events, wherein the extraction rule or the regular expression corresponds to a field in the schema. 30. The computer-program product of claim 23 , wherein the one or more sub-models include at least a first sub-model associated with a first sub-schema of the schema and a second sub-model associated with a second sub-schema of the schema, the first sub-schema having at least one different element than the second sub-schema, wherein the criteria is relative to a particular sub-schema associated with the particular sub-model that was selected by the first input.
| 0.839766 |
8,239,820 | 16 | 19 |
16. A method executed by a processor for encoding conformance requirements in an executable form for use in assessing compliance for web services, method comprising: generating a conformance profile, said conformance profile comprising a description section and at least one rule category, wherein there is at least one rule in said at least one rule category, and wherein said rule has descriptive meta data, functional code fragments including a query and a match script, and a designation of a level of severity for non-conformance of said rule; asserting said conformance profile against a set of artifacts by executing said query for each of said set of artifacts to identify a portion of an artifact to be processed by said match script and invoking said match script to determine whether said artifact includes at least one of efficiency problems and fatal errors; and if said artifact causes said at least one of efficiency problems and fatal errors, generating a results report that includes at least one result message containing (1) a result pointer that links to a location of a cause of said at least one of efficiency problems and fatal errors in said artifact and (2) a result meta data that includes (i) a text description of said at least one of efficiency problems and fatal errors, (ii) date and time of a run resulting in said at least one of efficiency problems and fatal errors, and (iii) input provided for said run.
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16. A method executed by a processor for encoding conformance requirements in an executable form for use in assessing compliance for web services, method comprising: generating a conformance profile, said conformance profile comprising a description section and at least one rule category, wherein there is at least one rule in said at least one rule category, and wherein said rule has descriptive meta data, functional code fragments including a query and a match script, and a designation of a level of severity for non-conformance of said rule; asserting said conformance profile against a set of artifacts by executing said query for each of said set of artifacts to identify a portion of an artifact to be processed by said match script and invoking said match script to determine whether said artifact includes at least one of efficiency problems and fatal errors; and if said artifact causes said at least one of efficiency problems and fatal errors, generating a results report that includes at least one result message containing (1) a result pointer that links to a location of a cause of said at least one of efficiency problems and fatal errors in said artifact and (2) a result meta data that includes (i) a text description of said at least one of efficiency problems and fatal errors, (ii) date and time of a run resulting in said at least one of efficiency problems and fatal errors, and (iii) input provided for said run. 19. The method according to claim 16 , further comprising interactively analyzing said results report to locate an error source.
| 0.721739 |
9,832,646 | 35 | 36 |
35. The system of claim 1 , wherein the processor further implements the first processing node and the second processing node for continuing to monitor and verify the user identity and the user activity for the dynamic time period by: monitoring further user activity at the first processing node within the mirrored live data flow for a configurable period after allowing the relevant network access and activity; updating the verification criteria at the second processing node based on the monitored further user activity in the mirrored live-data flow; and verifying the monitored further user activity using the verification criteria to ensure continued user identity fidelity.
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35. The system of claim 1 , wherein the processor further implements the first processing node and the second processing node for continuing to monitor and verify the user identity and the user activity for the dynamic time period by: monitoring further user activity at the first processing node within the mirrored live data flow for a configurable period after allowing the relevant network access and activity; updating the verification criteria at the second processing node based on the monitored further user activity in the mirrored live-data flow; and verifying the monitored further user activity using the verification criteria to ensure continued user identity fidelity. 36. The system of claim 35 , wherein the time period for continuing to monitor and verify identity is dynamic.
| 0.834337 |
7,620,494 | 17 | 23 |
17. An apparatus for displaying driving directions, the apparatus being configured to: access data that describes at least one maneuver to be executed to traverse a route from an origin to a destination; select a portion of the accessed data that describes a particular maneuver, the maneuver including an action and a road; determine a road symbol to associate with the particular maneuver, the road symbol having substantially the same appearance as a road sign used to mark the road involved in the particular maneuver; determine an action symbol to associate with the particular maneuver, the action symbol having substantially the same appearance as a road sign used to identify the action to be performed to execute the particular maneuver; and present both the road symbol and the action symbol to describe the particular maneuver, such that the road symbol and the action symbol describe the particular maneuver.
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17. An apparatus for displaying driving directions, the apparatus being configured to: access data that describes at least one maneuver to be executed to traverse a route from an origin to a destination; select a portion of the accessed data that describes a particular maneuver, the maneuver including an action and a road; determine a road symbol to associate with the particular maneuver, the road symbol having substantially the same appearance as a road sign used to mark the road involved in the particular maneuver; determine an action symbol to associate with the particular maneuver, the action symbol having substantially the same appearance as a road sign used to identify the action to be performed to execute the particular maneuver; and present both the road symbol and the action symbol to describe the particular maneuver, such that the road symbol and the action symbol describe the particular maneuver. 23. The apparatus of claim 17 wherein the action symbol indicates a turn direction involved in the particular maneuver.
| 0.735556 |
8,726,032 | 12 | 14 |
12. The non-transitory computer readable medium of claim 11 , wherein the secrets directory comprises a plurality of decoy files.
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12. The non-transitory computer readable medium of claim 11 , wherein the secrets directory comprises a plurality of decoy files. 14. The non-transitory computer readable medium of claim 12 , wherein each of the plurality of decoy files is associated with a modification time within a range of modification times and a modification time of the secrets file is within the range of modification times.
| 0.5 |
8,510,646 | 5 | 6 |
5. The method according to claim 1 , further comprising the step of generating an alert in response to an input of one of the plurality of successive annotations.
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5. The method according to claim 1 , further comprising the step of generating an alert in response to an input of one of the plurality of successive annotations. 6. The method according to claim 5 , wherein the alert is generated in response to a user request.
| 0.5 |
9,678,619 | 8 | 10 |
8. A system comprising: a computing device that includes hardware memory; and at least one program module stored at least in part in the hardware memory that, based on execution by the computing device, configures the system to perform actions comprising: generating, by the computing device, a graph representing switches between various windows, the graph comprising nodes, edges, and weights, where each node in the graph represents one of the various windows, and where each edge of the graph represents a switch between two of the various windows, and where the each edge is weighted based on a number of switches between the two of the various windows; discarding directionality of the edges of the generated graph; eliminating the edges of the generated graph that are weighted less than a particular threshold; grouping, based on the generated graph subsequent to the discarding and the eliminating, some of the various windows.
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8. A system comprising: a computing device that includes hardware memory; and at least one program module stored at least in part in the hardware memory that, based on execution by the computing device, configures the system to perform actions comprising: generating, by the computing device, a graph representing switches between various windows, the graph comprising nodes, edges, and weights, where each node in the graph represents one of the various windows, and where each edge of the graph represents a switch between two of the various windows, and where the each edge is weighted based on a number of switches between the two of the various windows; discarding directionality of the edges of the generated graph; eliminating the edges of the generated graph that are weighted less than a particular threshold; grouping, based on the generated graph subsequent to the discarding and the eliminating, some of the various windows. 10. The system of claim 8 where the grouping results in a group that contains the some of the various windows.
| 0.513274 |
8,996,439 | 1 | 3 |
1. A method to perform a reasoning task, the method comprising: receiving a reasoning task, wherein the reasoning task includes one or more symbols; extracting, based on an interpretation of the one or more symbols, polarity-based modules from a knowledge base, wherein the polarity-based modules includes axioms relevant to at least one of a positive polarity and a negative polarity of each of the one or more symbols; intersecting the polarity-based modules to identify an intersection polarity-based module that includes axioms from each of the polarity-based modules; and performing the reasoning task with the at least one intersection polarity-based module to obtain a result.
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1. A method to perform a reasoning task, the method comprising: receiving a reasoning task, wherein the reasoning task includes one or more symbols; extracting, based on an interpretation of the one or more symbols, polarity-based modules from a knowledge base, wherein the polarity-based modules includes axioms relevant to at least one of a positive polarity and a negative polarity of each of the one or more symbols; intersecting the polarity-based modules to identify an intersection polarity-based module that includes axioms from each of the polarity-based modules; and performing the reasoning task with the at least one intersection polarity-based module to obtain a result. 3. The method of claim 1 , wherein the intersection polarity-based module includes axioms relevant to the reasoning task, wherein the axioms relevant to the reasoning task are identified by interpretation of the one or more symbols in the reasoning task.
| 0.861656 |
10,002,132 | 1 | 4 |
1. A computer-implemented method, comprising: displaying a graphical user interface for language translation on a user device that includes (i) one or more automated speech recognizers that recognize speech in a source language only when in a first mode and to recognize speech in a target language only when in a second mode, (ii) a microphone that receives audio, and (iii) a speaker that outputs audio, the graphical user interface comprising a source language visual indicator and a target language visual indicator; in the first mode in which the one or more automated speech recognizers recognize speech in the source language only, highlighting the source language visual indicator and displaying an input visual indicator on the graphical user interface to provide a visual indication that the user device receives audio input in the source language only; and in response to an endpointer on the user device automatically determining that the input in the source language only has completed, and without requiring the user to manually switch between the first mode and the second mode, automatically activating the second mode in which the one or more automated speech recognizers recognize speech in the target language only, removing highlighting from the source language visual indicator, highlighting the target language visual indicator, and replacing the input visual indicator with an output visual indicator on the graphical user interface to provide a visual indication that the user device provides audio output in the target language.
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1. A computer-implemented method, comprising: displaying a graphical user interface for language translation on a user device that includes (i) one or more automated speech recognizers that recognize speech in a source language only when in a first mode and to recognize speech in a target language only when in a second mode, (ii) a microphone that receives audio, and (iii) a speaker that outputs audio, the graphical user interface comprising a source language visual indicator and a target language visual indicator; in the first mode in which the one or more automated speech recognizers recognize speech in the source language only, highlighting the source language visual indicator and displaying an input visual indicator on the graphical user interface to provide a visual indication that the user device receives audio input in the source language only; and in response to an endpointer on the user device automatically determining that the input in the source language only has completed, and without requiring the user to manually switch between the first mode and the second mode, automatically activating the second mode in which the one or more automated speech recognizers recognize speech in the target language only, removing highlighting from the source language visual indicator, highlighting the target language visual indicator, and replacing the input visual indicator with an output visual indicator on the graphical user interface to provide a visual indication that the user device provides audio output in the target language. 4. The method of claim 1 , wherein the visual indication that the user device provides audio output in the target language is provided by highlighting the output visual indicator on the graphical user interface while highlighting the target language visual indicator.
| 0.697964 |
8,073,869 | 1 | 21 |
1. A method for searching a structured data table T with m attributes and n records, where A={a 1 ; a 2 , : : : ; a m } denotes an attribute set, R={r 1 ; r 2 , : : : , r n } denotes the record set, and W={w 1 ; w 2 , : : : ; w p } denotes a distinct word set in T, where given two words, w i and w i , “w i ≦w j †denotes that w i is a prefix string of w j , where a query consists of a set of prefixes Q={p 1 , p 2 , . . . , p l }, where a predicted-word set is W k l ={w|w is a member of W and k l ≦w}, the method comprising for each prefix p i finding the set of prefixes from the data set that are similar to p i , by: determining the predicted-record set R Q ={r|r is a member of R, for every i; 1≦i≦·l−1, p i appears in r, and there exists a w included in W k l , w appears in r}; and for a keystroke that invokes query Q, returning the top-t records in R Q for a given value t, ranked by their relevancy to the query, treating every keyword as a partial keyword, namely given an input Q={k 1 ; k 2 ; : : : ; k l for each predicted record r, for each 1≦i≦·l, there exists at least one predicted word w i for k i in r, since k i must be a prefix of w i ,quantifying their similarity as:
sim =( k i ;w i )=| k i |/|w i | if there are multiple predicted words in r for a partial keyword k j , selecting the predicted word w i with the maximal similarity to k i and quantifying a weight of a predicted word to capture the importance of a predicted word, and taking into account the number of attributes that the l predicted words appear in, denoted as n a , to combine similarity, weight and number of attributes to generate a ranking function to score r for the query Q as follows: SCORE ⡠( r , Q ) = α * ∑ l = 1 1 ⢠⢠idf w i * sim ⡠( k i , w i ) + ( 1 - α ) * 1 n a , where α is a tuning parameter between 0 and 1.
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1. A method for searching a structured data table T with m attributes and n records, where A={a 1 ; a 2 , : : : ; a m } denotes an attribute set, R={r 1 ; r 2 , : : : , r n } denotes the record set, and W={w 1 ; w 2 , : : : ; w p } denotes a distinct word set in T, where given two words, w i and w i , “w i ≦w j †denotes that w i is a prefix string of w j , where a query consists of a set of prefixes Q={p 1 , p 2 , . . . , p l }, where a predicted-word set is W k l ={w|w is a member of W and k l ≦w}, the method comprising for each prefix p i finding the set of prefixes from the data set that are similar to p i , by: determining the predicted-record set R Q ={r|r is a member of R, for every i; 1≦i≦·l−1, p i appears in r, and there exists a w included in W k l , w appears in r}; and for a keystroke that invokes query Q, returning the top-t records in R Q for a given value t, ranked by their relevancy to the query, treating every keyword as a partial keyword, namely given an input Q={k 1 ; k 2 ; : : : ; k l for each predicted record r, for each 1≦i≦·l, there exists at least one predicted word w i for k i in r, since k i must be a prefix of w i ,quantifying their similarity as:
sim =( k i ;w i )=| k i |/|w i | if there are multiple predicted words in r for a partial keyword k j , selecting the predicted word w i with the maximal similarity to k i and quantifying a weight of a predicted word to capture the importance of a predicted word, and taking into account the number of attributes that the l predicted words appear in, denoted as n a , to combine similarity, weight and number of attributes to generate a ranking function to score r for the query Q as follows: SCORE ⡠( r , Q ) = α * ∑ l = 1 1 ⢠⢠idf w i * sim ⡠( k i , w i ) + ( 1 - α ) * 1 n a , where α is a tuning parameter between 0 and 1. 21. The method of claim 1 where the data table T is structured into a trie and further comprising linking each node on the trie corresponding to a word, w i , to each node corresponding to the synonyms of the word, w i , in the trie and vise versa to return both w i and its synonyms using the link when the word, w i is retrieved.
| 0.7559 |
7,519,528 | 9 | 19 |
9. A method for building concept knowledge in a computer system from a machine-readable dictionary for use by a search engine to facilitate quick and accurate information retrieval, the machine-readable dictionary including a plurality of words in a first language and a plurality of corresponding translated words in a second language, and a plurality of words in the second language and a plurality of corresponding translated words in the first language, said method comprising steps of: providing a seed word in the first language; forward-translating said seed word to obtain a plurality of translated words corresponding to said seed word by looking up said machine-readable dictionary; backward-translating said translated words to obtain a plurality of translated words in the first language corresponding to each of said plurality of translated words obtained by said step of forward-translating respectively, as words of the concept knowledge, by looking up said machine-readable dictionary; storing synonymous words of each translated word in said first language and in said second language; calculating frequency of each translated word in said first language and in said second language; ranking said translated words based on said stored synonymous words and said calculated frequency; presenting said ranked words to a user for allowing the user to select desired words among said ranked words and to delete irrelevant words among said ranked words; and providing the selected desired words to said search engine to perform information retrieval on a knowledge-based level instead of a keyword-based level.
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9. A method for building concept knowledge in a computer system from a machine-readable dictionary for use by a search engine to facilitate quick and accurate information retrieval, the machine-readable dictionary including a plurality of words in a first language and a plurality of corresponding translated words in a second language, and a plurality of words in the second language and a plurality of corresponding translated words in the first language, said method comprising steps of: providing a seed word in the first language; forward-translating said seed word to obtain a plurality of translated words corresponding to said seed word by looking up said machine-readable dictionary; backward-translating said translated words to obtain a plurality of translated words in the first language corresponding to each of said plurality of translated words obtained by said step of forward-translating respectively, as words of the concept knowledge, by looking up said machine-readable dictionary; storing synonymous words of each translated word in said first language and in said second language; calculating frequency of each translated word in said first language and in said second language; ranking said translated words based on said stored synonymous words and said calculated frequency; presenting said ranked words to a user for allowing the user to select desired words among said ranked words and to delete irrelevant words among said ranked words; and providing the selected desired words to said search engine to perform information retrieval on a knowledge-based level instead of a keyword-based level. 19. The method for building concept knowledge in a computer system from a machine-readable dictionary for use by a search engine in accordance with claim 9 , wherein said seed word is provided by being automatically selected from a file.
| 0.853342 |
7,973,959 | 5 | 6 |
5. The system according to claim 1 , wherein the document destruction apparatus further includes a destruction operator authentication section that identifies a destruction operator who is to destruct the document.
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5. The system according to claim 1 , wherein the document destruction apparatus further includes a destruction operator authentication section that identifies a destruction operator who is to destruct the document. 6. The system according to claim 5 , wherein the document destruction apparatus further includes: a document administration information storage section that stores the administration information containing a destruction authority for the document, a first identification section that identifies a destruction authority of the destruction operator authenticated by the destruction operator authentication section, a second identification section that identifies the destruction authority for the document based on the document identification information and the administration information, a third determination section that determines as to whether or not the destruction operator has the destruction authority for the document, based on the destruction authority of the destruction operator identified by the first identification section and the destruction authority for the document determined by the second identification section, a document destruction member, and a controller that controls the document destruction member to destruct the document if the destruction operator has the destruction authority for the document.
| 0.5 |
8,676,567 | 1 | 10 |
1. A method for filtering adjectives from a lexical chain, the method comprising: receiving, at a processor, the lexical chain comprising an adjective, the lexical chain being a component of an input document; calculating a non-characteristic-ness score based on the adjective's usage within the input document according to linguistic tests, the non-characteristic-ness score being a function of at least a frequency of the adjective's usage in the input document and a gradability non-characteristic-ness score for the adjective; testing, by the processor, the adjective to determine if the adjective is at least one of the following: a characteristic adjective and a non-characteristic adjective, wherein testing the adjective comprises comparing the non-characteristic-ness score to a threshold score; removing, by the processor, the adjective from the lexical chain when the non-characteristic-ness score is above the threshold score; and leaving, by the processor, the adjective in the lexical chain when the non-characteristic-ness score is below the threshold score.
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1. A method for filtering adjectives from a lexical chain, the method comprising: receiving, at a processor, the lexical chain comprising an adjective, the lexical chain being a component of an input document; calculating a non-characteristic-ness score based on the adjective's usage within the input document according to linguistic tests, the non-characteristic-ness score being a function of at least a frequency of the adjective's usage in the input document and a gradability non-characteristic-ness score for the adjective; testing, by the processor, the adjective to determine if the adjective is at least one of the following: a characteristic adjective and a non-characteristic adjective, wherein testing the adjective comprises comparing the non-characteristic-ness score to a threshold score; removing, by the processor, the adjective from the lexical chain when the non-characteristic-ness score is above the threshold score; and leaving, by the processor, the adjective in the lexical chain when the non-characteristic-ness score is below the threshold score. 10. The method of claim 1 , wherein calculating the non-characteristic-ness score according to linguistic tests comprises: assigning a point value based on each of the linguistic tests; and summing the point values for each of the linguistic tests.
| 0.507937 |
9,602,950 | 6 | 10 |
6. A computer program product stored on a non-transitory computer readable storage medium, which when executed by a computer system, provides context-based data storage management between a network provisioning platform and a mobile device, comprising: program instructions for assigning context to user files stored in the network provisioning platform; program instructions for periodically analyzing schedule information and location information associated with a user to determine a current user context; program instructions for matching the current user context with user files stored in the network provisioning platform to identify a set of context matching files; program instructions for periodically downloading context matching files from the network provisioning platform to the mobile device associated with the user; program instructions for determining if the mobile device has enough free space to download the context matching files; and program instructions for automatically deleting files from the mobile device to create more free space if the program instructions for determining determines that the mobile device does not have enough space to download the context matching files and another instance of the deleted files is stored in the network provisioning platform.
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6. A computer program product stored on a non-transitory computer readable storage medium, which when executed by a computer system, provides context-based data storage management between a network provisioning platform and a mobile device, comprising: program instructions for assigning context to user files stored in the network provisioning platform; program instructions for periodically analyzing schedule information and location information associated with a user to determine a current user context; program instructions for matching the current user context with user files stored in the network provisioning platform to identify a set of context matching files; program instructions for periodically downloading context matching files from the network provisioning platform to the mobile device associated with the user; program instructions for determining if the mobile device has enough free space to download the context matching files; and program instructions for automatically deleting files from the mobile device to create more free space if the program instructions for determining determines that the mobile device does not have enough space to download the context matching files and another instance of the deleted files is stored in the network provisioning platform. 10. The program product of claim 6 , further comprising a system for deploying agents to run on at least one of the mobile device and network provisioning platform.
| 0.5 |
9,753,546 | 1 | 13 |
1. A computer-implemented method comprising: processing a plurality of data frames, each data frame of the plurality of data frames comprising one or more body point locations of each of a plurality of collaborating users that are interfacing with an application at each of a plurality of time intervals; defining a spatial volume for each of the plurality of collaborating users based on the plurality of processed data frames; detecting a gesture performed by a first collaborating user of the plurality of collaborating users based on the plurality of processed data frames; determining the gesture to be an input gesture based on the gesture being performed by the first collaborating user in a first spatial volume; interpreting, by a machine having a memory and at least one processor, the input gesture based on a context of the first spatial volume, the context of the first spatial volume comprising an intersection volume between the first spatial volume and a second spatial volume for a second collaborating user; and providing an input command to the application based on the interpreted input gesture, the input command being different for the gesture being within the intersection volume than for the gesture being outside of the intersection volume.
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1. A computer-implemented method comprising: processing a plurality of data frames, each data frame of the plurality of data frames comprising one or more body point locations of each of a plurality of collaborating users that are interfacing with an application at each of a plurality of time intervals; defining a spatial volume for each of the plurality of collaborating users based on the plurality of processed data frames; detecting a gesture performed by a first collaborating user of the plurality of collaborating users based on the plurality of processed data frames; determining the gesture to be an input gesture based on the gesture being performed by the first collaborating user in a first spatial volume; interpreting, by a machine having a memory and at least one processor, the input gesture based on a context of the first spatial volume, the context of the first spatial volume comprising an intersection volume between the first spatial volume and a second spatial volume for a second collaborating user; and providing an input command to the application based on the interpreted input gesture, the input command being different for the gesture being within the intersection volume than for the gesture being outside of the intersection volume. 13. The computer-implemented method of claim 1 , wherein the input command comprises translating a display object of the application, and the input gesture comprises translating a first body point location.
| 0.677116 |
8,380,712 | 12 | 13 |
12. The system of claim 1 , wherein some of the identified words comprise a number of words.
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12. The system of claim 1 , wherein some of the identified words comprise a number of words. 13. The system of claim 12 , the number of words is a signature block.
| 0.5625 |
9,135,653 | 24 | 30 |
24. A method comprising: receiving first activity information for a sender of a first link to at least one recipient collected by a collection resource at a Web site, wherein no personally identifiable information of the sender is collected in the first activity information; storing the first activity information at a storage server; receiving second activity information when a recipient accesses the first link sent by the sender corresponding to the first activity information stored at the storage server, wherein no personally identifiable information of the recipient is collected in the second activity information; using at least one processor, using the first activity information to identify a first node in a social graph as being representative of the sender comprising: extracting a user identifier from the first activity data and if a match for the user identifier is not found in the social graph, performing a probabilistic fingerprinting approach using attributes comprising at least one of device identifiers; IP addresses; operating systems; browsers types; browser versions; or user navigational, geo-temporal, and behavioral patterns; using the second activity information to identify a second node in the social graph as being representative of the recipient; determining a category for the first link as a first category type; in the social graph, identifying a first edge between the first and second nodes as being representative of the first category type; and updating a first value associated with the first edge to a second value based on a time elapsed from at least one of the first activity information or second activity information.
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24. A method comprising: receiving first activity information for a sender of a first link to at least one recipient collected by a collection resource at a Web site, wherein no personally identifiable information of the sender is collected in the first activity information; storing the first activity information at a storage server; receiving second activity information when a recipient accesses the first link sent by the sender corresponding to the first activity information stored at the storage server, wherein no personally identifiable information of the recipient is collected in the second activity information; using at least one processor, using the first activity information to identify a first node in a social graph as being representative of the sender comprising: extracting a user identifier from the first activity data and if a match for the user identifier is not found in the social graph, performing a probabilistic fingerprinting approach using attributes comprising at least one of device identifiers; IP addresses; operating systems; browsers types; browser versions; or user navigational, geo-temporal, and behavioral patterns; using the second activity information to identify a second node in the social graph as being representative of the recipient; determining a category for the first link as a first category type; in the social graph, identifying a first edge between the first and second nodes as being representative of the first category type; and updating a first value associated with the first edge to a second value based on a time elapsed from at least one of the first activity information or second activity information. 30. The method of claim 24 wherein the first value is dependent upon a type of sharing activity by the sender to the at least one recipient, wherein the type of sharing activity comprises at least one of posting onto a social networking service, an e-mail, an instant message, or messaging in a chat to the at least one recipient.
| 0.5 |
8,620,718 | 1 | 2 |
1. A computer implemented method for benchmarking a brand based on social media strength of said brand, comprising: providing a brand monitoring platform comprising at least one processor configured to monitor said brand in a virtual social media environment; acquiring input information on said brand by said brand monitoring platform; identifying industries related to said brand and competing brands in said identified industries using said acquired input information on said brand by said brand monitoring platform; acquiring social media information related to said brand and said competing brands in said identified industries from a plurality of social media sources in said virtual social media environment by said brand monitoring platform via a network; dynamically generating categories in one or more hierarchical levels in each of said identified industries by said brand monitoring platform based on an independent analysis of said acquired social media information related to said brand and said competing brands from each of said social media sources; sorting said acquired social media information related to said brand and said competing brands in said each of said identified industries into one or more of said dynamically generated categories in said one or more hierarchical levels by said brand monitoring platform using a sorting interface provided by said brand monitoring platform; determining an audience score for said brand and each of said competing brands by measuring an aggregate reach of said brand and said each of said competing brands in said virtual social media environment by said brand monitoring platform based on one or more of a plurality of weighted audience score metric parameters using said sorted social media information; determining an engagement score for said brand and said each of said competing brands by measuring interaction between said brand and said each of said competing brands and their followers by said brand monitoring platform based on one or more of a plurality of weighted engagement score metric parameters using said sorted social media information; generating an aggregate score for said brand and said each of said competing brands by said brand monitoring platform using said determined audience score and said determined engagement score; and determining social media strength of said brand in comparison with said competing brands in said virtual social media environment by assigning a rank to said brand and said each of said competing brands by said brand monitoring platform based on said aggregate score; whereby said brand is benchmarked in comparison with said competing brands in said virtual social media environment based on said social media strength of said brand in comparison with said competing brands in said virtual social media environment.
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1. A computer implemented method for benchmarking a brand based on social media strength of said brand, comprising: providing a brand monitoring platform comprising at least one processor configured to monitor said brand in a virtual social media environment; acquiring input information on said brand by said brand monitoring platform; identifying industries related to said brand and competing brands in said identified industries using said acquired input information on said brand by said brand monitoring platform; acquiring social media information related to said brand and said competing brands in said identified industries from a plurality of social media sources in said virtual social media environment by said brand monitoring platform via a network; dynamically generating categories in one or more hierarchical levels in each of said identified industries by said brand monitoring platform based on an independent analysis of said acquired social media information related to said brand and said competing brands from each of said social media sources; sorting said acquired social media information related to said brand and said competing brands in said each of said identified industries into one or more of said dynamically generated categories in said one or more hierarchical levels by said brand monitoring platform using a sorting interface provided by said brand monitoring platform; determining an audience score for said brand and each of said competing brands by measuring an aggregate reach of said brand and said each of said competing brands in said virtual social media environment by said brand monitoring platform based on one or more of a plurality of weighted audience score metric parameters using said sorted social media information; determining an engagement score for said brand and said each of said competing brands by measuring interaction between said brand and said each of said competing brands and their followers by said brand monitoring platform based on one or more of a plurality of weighted engagement score metric parameters using said sorted social media information; generating an aggregate score for said brand and said each of said competing brands by said brand monitoring platform using said determined audience score and said determined engagement score; and determining social media strength of said brand in comparison with said competing brands in said virtual social media environment by assigning a rank to said brand and said each of said competing brands by said brand monitoring platform based on said aggregate score; whereby said brand is benchmarked in comparison with said competing brands in said virtual social media environment based on said social media strength of said brand in comparison with said competing brands in said virtual social media environment. 2. The computer implemented method of claim 1 , wherein said dynamically generated categories comprise a location of each of said identified industries related to said brand and said each of said competing brands, a location of each of a plurality of authors of said social media information, types of said social media sources utilized by said brand and said each of said competing brands, and marketing elements.
| 0.732212 |
9,742,836 | 1 | 2 |
1. A computer-implemented method comprising: generating, by a computer system, a plurality of articles, wherein each of the plurality of articles includes one or more content items, wherein generating each article in the plurality of articles includes ranking a plurality of content items on a topic, selecting a subset of content items from the plurality of content items based on the respective ranking of each content item, and including the subset of content items in the article, and wherein selecting the subset of content items includes selecting a first content item in a first format and a second content item in a second format, the first content item having the highest ranking of any of the plurality of content items in the first format, and the second content item having the highest ranking of any of the plurality of content items in the second format; generating, by the computer system, a content digest that includes the plurality of articles; transmitting, to a computing device of a user, a notification indicating the content digest is ready to be downloaded; receiving, from the user's computing device, a first request for a first portion of the content digest; in response to the first request and to a determination that a quality level of a connection between the computer system and the user's computing device meets or exceeds a first predetermined threshold, transmitting the first portion of the content digest to the user's computing device at a first time, the first portion comprising lead images from the content digest; receiving, from the user's computing device, a second request for a second portion of the content digest; in response to the second request and to a determination that the quality level of the connection is beneath a second predetermined threshold, transmitting the second portion of the content digest to the user's computing device at a second time, the second portion comprising thumbnails of gallery images, wherein the first and second portions of the content digest are transmitted to the user's computing device without providing any notification of the transmissions to the user.
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1. A computer-implemented method comprising: generating, by a computer system, a plurality of articles, wherein each of the plurality of articles includes one or more content items, wherein generating each article in the plurality of articles includes ranking a plurality of content items on a topic, selecting a subset of content items from the plurality of content items based on the respective ranking of each content item, and including the subset of content items in the article, and wherein selecting the subset of content items includes selecting a first content item in a first format and a second content item in a second format, the first content item having the highest ranking of any of the plurality of content items in the first format, and the second content item having the highest ranking of any of the plurality of content items in the second format; generating, by the computer system, a content digest that includes the plurality of articles; transmitting, to a computing device of a user, a notification indicating the content digest is ready to be downloaded; receiving, from the user's computing device, a first request for a first portion of the content digest; in response to the first request and to a determination that a quality level of a connection between the computer system and the user's computing device meets or exceeds a first predetermined threshold, transmitting the first portion of the content digest to the user's computing device at a first time, the first portion comprising lead images from the content digest; receiving, from the user's computing device, a second request for a second portion of the content digest; in response to the second request and to a determination that the quality level of the connection is beneath a second predetermined threshold, transmitting the second portion of the content digest to the user's computing device at a second time, the second portion comprising thumbnails of gallery images, wherein the first and second portions of the content digest are transmitted to the user's computing device without providing any notification of the transmissions to the user. 2. The method of claim 1 , wherein the entire content digest is transmitted to the user's computing device prior to a predetermined time when the content digest is to be presented to the user via the user's computing device.
| 0.715013 |
7,865,364 | 1 | 2 |
1. A method for performing recognition of speech in reply to a prompt, the method comprising: storing a data set, in a dialog manager module, the data set including a plurality of information items and a corresponding set of one or more improbable values for each of the plurality of information items, the one or more improbable values for each of the plurality of information items comprising one or more values that are not valid for the information item based on a context of the prompt; receiving a plurality of recognized ordered interpretations from an automatic speech recognition (ASR) engine, the plurality of recognized ordered interpretations each including a plurality of received information items; and comparing a value of one of the plurality of received information items for a first recognized ordered interpretation to the data set to determine if the value of the one of the received information items matches any of the set of one or more improbable values for the information item.
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1. A method for performing recognition of speech in reply to a prompt, the method comprising: storing a data set, in a dialog manager module, the data set including a plurality of information items and a corresponding set of one or more improbable values for each of the plurality of information items, the one or more improbable values for each of the plurality of information items comprising one or more values that are not valid for the information item based on a context of the prompt; receiving a plurality of recognized ordered interpretations from an automatic speech recognition (ASR) engine, the plurality of recognized ordered interpretations each including a plurality of received information items; and comparing a value of one of the plurality of received information items for a first recognized ordered interpretation to the data set to determine if the value of the one of the received information items matches any of the set of one or more improbable values for the information item. 2. The method of claim 1 , further including replacing the value of the one of the received information items for the first recognized ordered interpretation with a value of a corresponding information item from one of other recognized ordered interpretations if the value of the one of the received information items matches a value of any of the set of one or more improbable values for the corresponding information item.
| 0.5 |
8,117,203 | 15 | 16 |
15. A non-transitory computer-readable storage medium having embodied thereon a program, the program being executable by a computer processor to perform a method for automatically extracting and structuring data from a semi-structured web site, the method comprising: developing a set of experts; analyzing links and pages on the web site by the set of experts; identifying predetermined types of generic structures by the set of experts; clustering pages and textual segments within the pages based on the identified types of generic structures, the clustering being represented as a Bayesian belief network and including: adding a layer of nodes to the Bayesian belief network, the added layer of nodes including a node for every pair of samples being analyzed, at least one node in the added layer of nodes being an in-same-cluster node which represents whether or not a respective pair of samples being analyzed is in a same cluster, and the set of experts providing their output probabilistic suggestions represented as virtual-evidence nodes; collecting virtual-evidence from the set of experts; calculating a belief in a corresponding in-same-cluster node by propagating beliefs from all the virtual evidence nodes; and calculating a belief in a root clustering node by propagating beliefs from all corresponding in-same-cluster nodes; identifying, based on the clustering, semi-structured data that can be extracted; and extracting the identified semi-structured data from the web site.
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15. A non-transitory computer-readable storage medium having embodied thereon a program, the program being executable by a computer processor to perform a method for automatically extracting and structuring data from a semi-structured web site, the method comprising: developing a set of experts; analyzing links and pages on the web site by the set of experts; identifying predetermined types of generic structures by the set of experts; clustering pages and textual segments within the pages based on the identified types of generic structures, the clustering being represented as a Bayesian belief network and including: adding a layer of nodes to the Bayesian belief network, the added layer of nodes including a node for every pair of samples being analyzed, at least one node in the added layer of nodes being an in-same-cluster node which represents whether or not a respective pair of samples being analyzed is in a same cluster, and the set of experts providing their output probabilistic suggestions represented as virtual-evidence nodes; collecting virtual-evidence from the set of experts; calculating a belief in a corresponding in-same-cluster node by propagating beliefs from all the virtual evidence nodes; and calculating a belief in a root clustering node by propagating beliefs from all corresponding in-same-cluster nodes; identifying, based on the clustering, semi-structured data that can be extracted; and extracting the identified semi-structured data from the web site. 16. The non-transitory computer-readable storage medium of claim 15 , wherein each expert in the set of experts is assigned a particular type of structure independently from all other experts to focus on.
| 0.824742 |
9,159,318 | 15 | 20 |
15. A computer-readable storage device having instructions stored which, when executed by a computing device, cause the computing device to perform operations comprising: performing automatic speech recognition using a bootstrap model on utterance data not having a corresponding manual transcription, to produce automatically transcribed utterances, wherein the bootstrap model is based on text data mined from a website relevant to a specific domain; selecting a predetermined number of utterances not having a corresponding manual transcription based on a geometrically computed n-tuple confidence score; receiving transcriptions of the predetermined number of utterances, wherein the transcriptions are made by a human being; and generating a language model based on the automatically transcribed utterances, the predetermined number of utterances, and the transcriptions.
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15. A computer-readable storage device having instructions stored which, when executed by a computing device, cause the computing device to perform operations comprising: performing automatic speech recognition using a bootstrap model on utterance data not having a corresponding manual transcription, to produce automatically transcribed utterances, wherein the bootstrap model is based on text data mined from a website relevant to a specific domain; selecting a predetermined number of utterances not having a corresponding manual transcription based on a geometrically computed n-tuple confidence score; receiving transcriptions of the predetermined number of utterances, wherein the transcriptions are made by a human being; and generating a language model based on the automatically transcribed utterances, the predetermined number of utterances, and the transcriptions. 20. The computer-readable storage device of claim 15 , wherein the predetermined number of utterances are randomly selected.
| 0.730435 |
8,380,705 | 1 | 4 |
1. A computer-implemented method comprising: determining, by a computer system, a score for a document as a result for a search query, the score based, at least in part, on one or more related search queries that have at least a threshold relationship to the search query, wherein determining the score comprises: identifying click data associated with the document and the one or more related search queries, wherein the click data indicates how frequently the document was selected when the document was presented in search results for the one or more related search queries over a period of time; weighting the click data based on weights for the one or more related search queries, wherein the weights indicate how strongly the one or more related queries are related to the search query; and combining the weighted click data to generate at least a component of the score; and providing, by the computer system, the score for the document as a result for the search query.
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1. A computer-implemented method comprising: determining, by a computer system, a score for a document as a result for a search query, the score based, at least in part, on one or more related search queries that have at least a threshold relationship to the search query, wherein determining the score comprises: identifying click data associated with the document and the one or more related search queries, wherein the click data indicates how frequently the document was selected when the document was presented in search results for the one or more related search queries over a period of time; weighting the click data based on weights for the one or more related search queries, wherein the weights indicate how strongly the one or more related queries are related to the search query; and combining the weighted click data to generate at least a component of the score; and providing, by the computer system, the score for the document as a result for the search query. 4. The method of claim 1 , further comprising combining additional click data with the combined weighted click data to generate the score, wherein the additional click data indicates how frequently the document was selected when the document was presented in search results for the search query.
| 0.733273 |
8,886,519 | 9 | 10 |
9. The text processing apparatus according to claim 8 , wherein the descriptive content determination unit derives, based on the result of the determination by the segment determination unit, an extent to which the content of the homogeneous segment is included in the second text corresponding to the other first text which includes the homogeneous segment, further derives, based on the derived extent, a degree to which each segment constituting the first text which is set as the analysis target should be described in the second text corresponding to the first text which is set as the analysis target, and performs the determination using the degree.
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9. The text processing apparatus according to claim 8 , wherein the descriptive content determination unit derives, based on the result of the determination by the segment determination unit, an extent to which the content of the homogeneous segment is included in the second text corresponding to the other first text which includes the homogeneous segment, further derives, based on the derived extent, a degree to which each segment constituting the first text which is set as the analysis target should be described in the second text corresponding to the first text which is set as the analysis target, and performs the determination using the degree. 10. The text processing apparatus according to claim 9 , wherein the inclusion determination unit, in addition to the determination regarding the content of each segment, computes, for the plurality of segments respectively constituting all of the first texts, an inclusion score representing a possibility of the content of each segment being included in the second text corresponding to the first text which includes the segment, and the descriptive content determination unit further derives the degree using the inclusion score computed by the inclusion determination unit, such that the degree increase the higher the inclusion score.
| 0.5 |
9,715,702 | 12 | 13 |
12. A manufacture comprising: a non-transitory computer-readable storage medium; and a computer executable instruction stored on the non-transitory computer-readable storage medium which, when executed by a computer device, causes the computing device to perform a method comprising: calculating target prices based at least in part on existing price information of a plurality of products, generating price bounds for a set of bounded business rules based at least in part on the calculated target prices, generating rule parameters for a set of comparative business rules based at least in part on the existing price information, and generating at least one recommended price for at least one of the plurality of products by applying the price bounds and the rule parameters to the existing price information.
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12. A manufacture comprising: a non-transitory computer-readable storage medium; and a computer executable instruction stored on the non-transitory computer-readable storage medium which, when executed by a computer device, causes the computing device to perform a method comprising: calculating target prices based at least in part on existing price information of a plurality of products, generating price bounds for a set of bounded business rules based at least in part on the calculated target prices, generating rule parameters for a set of comparative business rules based at least in part on the existing price information, and generating at least one recommended price for at least one of the plurality of products by applying the price bounds and the rule parameters to the existing price information. 13. The manufacture of claim 12 , wherein generating price bounds comprises: for each bounded rule in the set of bounded business rules generating an upper bound value R max , a lower bound value R min , and a target value R target .
| 0.800855 |
9,390,086 | 3 | 4 |
3. The method of claim 1 , wherein determining, using the trained classifier, the candidate label for the document includes at least: receiving, from the trained classifier, a particular respective score for each different label of the plurality of labels, wherein the particular respective score represents a confidence of the trained classifier with respect to the label being correct for the document.
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3. The method of claim 1 , wherein determining, using the trained classifier, the candidate label for the document includes at least: receiving, from the trained classifier, a particular respective score for each different label of the plurality of labels, wherein the particular respective score represents a confidence of the trained classifier with respect to the label being correct for the document. 4. The method of claim 3 , wherein the trained classifier determines the respective score for each classification by at least: determining, for each document portion of a plurality of document portions of the document, a respective sub-score for the document portion; determining the particular respective score of the document based on aggregating the respective sub-score for each document portion of the plurality of document portions.
| 0.5 |
6,023,528 | 1 | 3 |
1. A method of inputting and preparing data from source documents for storage and subsequent retrieval comprising the steps of scanning each source document and forming signals representative of digitized patterns derived from images of characters and graphics thereon, storing the signals representative of the digitized patterns, selecting segments of the stored signals for further processing, converting signals representative of digitized patterns of characters into a machine code, storing as ambiguous characters the digitized patterns of each character not successfully converted into machine code including storing as ambiguous words each group of characters which includes converted characters and at least one ambiguous character, and storing the digitized patterns of the selected segments correlated with the machine code for subsequent use.
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1. A method of inputting and preparing data from source documents for storage and subsequent retrieval comprising the steps of scanning each source document and forming signals representative of digitized patterns derived from images of characters and graphics thereon, storing the signals representative of the digitized patterns, selecting segments of the stored signals for further processing, converting signals representative of digitized patterns of characters into a machine code, storing as ambiguous characters the digitized patterns of each character not successfully converted into machine code including storing as ambiguous words each group of characters which includes converted characters and at least one ambiguous character, and storing the digitized patterns of the selected segments correlated with the machine code for subsequent use. 3. A method according to claim 1 and further including preparing for a search by displaying a selected group of converted characters to be used as a search word, concurrently displaying the image of the group from which the characters were converted, and conducting the search with the image.
| 0.5 |
9,626,438 | 1 | 12 |
1. A computer-implemented method comprising: receiving search data by a computer system from a server hosting a first website, the search data including first information regarding content accessed by a user subsequent to a search, the first information indicative of a first content format accessed by the user, the first content format corresponding to a first search result of a plurality of search results provided to the user from the search, and the plurality of search results including content in a second content format that is different from the first content format, wherein the user does not access the second content format; identifying a category related to at least a portion of the search data, the identifying based at least on formats of content on target websites; determining, by the computer system and based on the search data, a topic for first content associated with the identified category; determining, by the computer system, a score indicative of a level of popularity for the topic, wherein determining the score is based on the search data, information regarding different formats of content offered on the first website, the format of content accessed by the user on the first website, and the user not accessing the second content format; and presenting the topic and the score to a content provider for producing the first content for the target websites, the content provider to select a format for the first content based on the score and on user interaction with the plurality of search results.
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1. A computer-implemented method comprising: receiving search data by a computer system from a server hosting a first website, the search data including first information regarding content accessed by a user subsequent to a search, the first information indicative of a first content format accessed by the user, the first content format corresponding to a first search result of a plurality of search results provided to the user from the search, and the plurality of search results including content in a second content format that is different from the first content format, wherein the user does not access the second content format; identifying a category related to at least a portion of the search data, the identifying based at least on formats of content on target websites; determining, by the computer system and based on the search data, a topic for first content associated with the identified category; determining, by the computer system, a score indicative of a level of popularity for the topic, wherein determining the score is based on the search data, information regarding different formats of content offered on the first website, the format of content accessed by the user on the first website, and the user not accessing the second content format; and presenting the topic and the score to a content provider for producing the first content for the target websites, the content provider to select a format for the first content based on the score and on user interaction with the plurality of search results. 12. The method of claim 1 , wherein the search data further includes one or more of: search terms entered by a user; information regarding a website hosting content accessed by a user; information regarding content returned in response to search terms entered by a user; or combinations thereof.
| 0.625635 |
8,909,579 | 3 | 4 |
3. The computer-implemented method of claim 1 , wherein said obtaining comprises: utilizing a Boolean satisfiability problem solver to determine that there is no scenario in which the bounded model does not hold the property; wherein said utilizing the solver comprises: providing the solver with a formula based on the bounded model and the property; constructing, by the solver, a proof of unsatisfiability, wherein the proof of unsatisfiability comprises a leaf node corresponding to a negation of the property; and transforming the proof of unsatisfiability to the proof of the property.
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3. The computer-implemented method of claim 1 , wherein said obtaining comprises: utilizing a Boolean satisfiability problem solver to determine that there is no scenario in which the bounded model does not hold the property; wherein said utilizing the solver comprises: providing the solver with a formula based on the bounded model and the property; constructing, by the solver, a proof of unsatisfiability, wherein the proof of unsatisfiability comprises a leaf node corresponding to a negation of the property; and transforming the proof of unsatisfiability to the proof of the property. 4. The computer-implemented method of claim 3 , wherein said transforming comprises performing linear-time transformations on the proof of unsatisfiability to determine the proof of the property.
| 0.5 |
7,962,328 | 26 | 30 |
26. An apparatus comprising: discriminant representation means for providing, to a user, representations of a plurality of discriminants of meanings of a plurality of symbols in a natural language, wherein each of the plurality of discriminants is associated with a corresponding finite set of mutually exclusive answers to the discriminant, and wherein the plurality of discriminants are orthogonal to each other; means for receiving, from the user, input representing a plurality of answers to the plurality of discriminants, wherein each of the plurality of answers from the user is selected from the finite set of mutually exclusive answers to the corresponding discriminant, wherein the means for receiving comprises: means for receiving first input from the user representing a first answer to a first one of the plurality of discriminants; and means for receiving second input, independent of the first input, from the user representing a second answer to a second one of the plurality of discriminants; and means for generating, in response to the input, a data structure tangibly stored in a computer-readable memory, the data structure comprising data representing the plurality of answers from the user, including the first answer and the second answer, and thereby representing a meaning of one of the plurality of symbols in the natural language; wherein the plurality of discriminants includes at least one Nature-related discriminant and one Realm-related discriminant, wherein Nature-related discriminants include a discriminant for distinguishing between individual and collective meanings in the natural language and a discriminant for distinguishing between specific and indefinite meanings in the natural language, and wherein Realm-related discriminants include a discriminant for distinguishing between natural and artificial meanings in the natural language and a discriminant for distinguishing between concrete and information meanings in the natural language.
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26. An apparatus comprising: discriminant representation means for providing, to a user, representations of a plurality of discriminants of meanings of a plurality of symbols in a natural language, wherein each of the plurality of discriminants is associated with a corresponding finite set of mutually exclusive answers to the discriminant, and wherein the plurality of discriminants are orthogonal to each other; means for receiving, from the user, input representing a plurality of answers to the plurality of discriminants, wherein each of the plurality of answers from the user is selected from the finite set of mutually exclusive answers to the corresponding discriminant, wherein the means for receiving comprises: means for receiving first input from the user representing a first answer to a first one of the plurality of discriminants; and means for receiving second input, independent of the first input, from the user representing a second answer to a second one of the plurality of discriminants; and means for generating, in response to the input, a data structure tangibly stored in a computer-readable memory, the data structure comprising data representing the plurality of answers from the user, including the first answer and the second answer, and thereby representing a meaning of one of the plurality of symbols in the natural language; wherein the plurality of discriminants includes at least one Nature-related discriminant and one Realm-related discriminant, wherein Nature-related discriminants include a discriminant for distinguishing between individual and collective meanings in the natural language and a discriminant for distinguishing between specific and indefinite meanings in the natural language, and wherein Realm-related discriminants include a discriminant for distinguishing between natural and artificial meanings in the natural language and a discriminant for distinguishing between concrete and information meanings in the natural language. 30. The apparatus of claim 26 , wherein the plurality of discriminants further includes a discriminant for distinguishing between composite and characteristic meanings in the natural language.
| 0.827648 |
10,027,778 | 7 | 8 |
7. A non-transitory machine-readable medium comprising instructions, which when executed by a machine, cause the machine to perform operations comprising: selecting from a plurality of ranked skills corresponding to a first member profile of a social networking service at most a predetermined number of the highest ranked skills as a first set of skills, the plurality of ranked skills quantifying a proficiency of a first member described by the first member profile in a given skill relative to other members of the social networking service; selecting from a second plurality of ranked skills corresponding to a second member profile of a second member of the social networking service at most the predetermined number of highest ranked skills as a second set of skills, the second plurality of ranked skills quantifying a proficiency of the second member in a given skill relative to the other members of the social networking service; selecting a particular skill that appears in both the first and second sets of skills; and presenting a graphical user interface to the first member, the graphical user interface preconfigured for the first member to include an indication requesting, from the first member, an endorsement of the second member for the particular skill, the graphical user interface providing at least one selectable indication to the first member, the at least one selectable indication including a first selectable indication to endorse the second member for the particular skill, the endorsement indicating that the first member thinks that the second member possesses the particular skill; and updating the second member profile based upon a selection by the first member of the at least one selectable indication.
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7. A non-transitory machine-readable medium comprising instructions, which when executed by a machine, cause the machine to perform operations comprising: selecting from a plurality of ranked skills corresponding to a first member profile of a social networking service at most a predetermined number of the highest ranked skills as a first set of skills, the plurality of ranked skills quantifying a proficiency of a first member described by the first member profile in a given skill relative to other members of the social networking service; selecting from a second plurality of ranked skills corresponding to a second member profile of a second member of the social networking service at most the predetermined number of highest ranked skills as a second set of skills, the second plurality of ranked skills quantifying a proficiency of the second member in a given skill relative to the other members of the social networking service; selecting a particular skill that appears in both the first and second sets of skills; and presenting a graphical user interface to the first member, the graphical user interface preconfigured for the first member to include an indication requesting, from the first member, an endorsement of the second member for the particular skill, the graphical user interface providing at least one selectable indication to the first member, the at least one selectable indication including a first selectable indication to endorse the second member for the particular skill, the endorsement indicating that the first member thinks that the second member possesses the particular skill; and updating the second member profile based upon a selection by the first member of the at least one selectable indication. 8. The non-transitory machine-readable medium of claim 7 , wherein the operations of presenting the graphical user interface comprises the operations of asking the first member if they would rate the second member's proficiency on the particular skill.
| 0.648045 |
9,646,604 | 10 | 11 |
10. A system of a contact center to adjust an automatic speech recognition (ASR) engine, comprising: a first processor; a first storage device in communication with the first processor, storing first executable instructions, wherein the executable instructions, when executed by the processor, cause the processor to: receive social network information from a social network; and modify the social network information, wherein the modifying comprises filtering the social network information and redacting the filtered social network information based on a relevancy of the social network information to the ASR engine; a second processor, in communication with the first processor; and a second storage device in communication with the second processor, wherein the second executable instructions, when executed by the second processor, cause the second processor to: data mine the modified social network information to extract one or more characteristics; infer a trend from the extracted one or more characteristics; add one or more words or phrases related to the trend to a recognition grammar of the ASR engine; calculate a magnitude of an adjustment to weights of the added one or more words or phrases in the recognition grammar of the ASR engine based upon a shaped sliding window; and adjust the ASR engine by adjusting a speech recognition weighting of the ASR engine based upon the calculated magnitude of adjustment, wherein the adjustment to the speech recognition weighting of the ASR engine has a limited duration.
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10. A system of a contact center to adjust an automatic speech recognition (ASR) engine, comprising: a first processor; a first storage device in communication with the first processor, storing first executable instructions, wherein the executable instructions, when executed by the processor, cause the processor to: receive social network information from a social network; and modify the social network information, wherein the modifying comprises filtering the social network information and redacting the filtered social network information based on a relevancy of the social network information to the ASR engine; a second processor, in communication with the first processor; and a second storage device in communication with the second processor, wherein the second executable instructions, when executed by the second processor, cause the second processor to: data mine the modified social network information to extract one or more characteristics; infer a trend from the extracted one or more characteristics; add one or more words or phrases related to the trend to a recognition grammar of the ASR engine; calculate a magnitude of an adjustment to weights of the added one or more words or phrases in the recognition grammar of the ASR engine based upon a shaped sliding window; and adjust the ASR engine by adjusting a speech recognition weighting of the ASR engine based upon the calculated magnitude of adjustment, wherein the adjustment to the speech recognition weighting of the ASR engine has a limited duration. 11. The system of claim 10 , wherein the second executable instructions, when executed by the second processor, cause the second processor to: receive a speech signal from a user, wherein the speech signal is recognized by use of the adjusted ASR engine.
| 0.539855 |
7,752,194 | 1 | 3 |
1. A computer-implemented method comprising: receiving, by a directory server, a query for an attribute associated with a Lightweight Directory Access Protocol (LDAP) entry, wherein the attribute is stored in a repository and contains a plurality of pairs of values, with each pair containing an attribute value and a corresponding version number stored as a subtype of the attribute; determining, by the directory server, whether the query includes a version number of the attribute; in response to a determination that the query includes a version number, returning a first attribute value that corresponds to the version number in the query; in response to a determination that the query includes a predetermined symbol, returning all versions of the attribute; and in response to a determination that the query does not include a version number or the predetermined symbol, returning a second attribute value that corresponds to a current version of the attribute.
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1. A computer-implemented method comprising: receiving, by a directory server, a query for an attribute associated with a Lightweight Directory Access Protocol (LDAP) entry, wherein the attribute is stored in a repository and contains a plurality of pairs of values, with each pair containing an attribute value and a corresponding version number stored as a subtype of the attribute; determining, by the directory server, whether the query includes a version number of the attribute; in response to a determination that the query includes a version number, returning a first attribute value that corresponds to the version number in the query; in response to a determination that the query includes a predetermined symbol, returning all versions of the attribute; and in response to a determination that the query does not include a version number or the predetermined symbol, returning a second attribute value that corresponds to a current version of the attribute. 3. The method of claim 1 wherein the query specifies the version in a subtype field of the attribute.
| 0.767281 |
8,296,130 | 16 | 29 |
16. A computer-implemented system, comprising: a data processor; a computer-readable memory encoded with instructions for commanding the data processors to execute steps including: receiving a plurality of offensive words, wherein each respective offensive word in the plurality of offensive words is associated with a severity score identifying the offensiveness of the respective word; receiving a string of words, wherein a candidate word is selected from the string of words; calculating, for each respective offensive word in the plurality of offensive words, a distance between the candidate word and the respective offensive word; calculating a plurality of offensiveness scores for the candidate word, each offensiveness score in the plurality of offensiveness scores based upon (i) the calculated distance between the candidate word and an offensive word in the plurality of offensive words and (ii) the severity score of the offensive word, wherein the plurality of offensiveness scores are calculated according to one or more of:
offensiveness score= A *(( B−C )/ B );
offensiveness score= A *(( B −(1/ C ))/ B );
offensiveness score=Max((( A−C )/ A ),0); and
offensiveness score=((( B−C )/ B )> T ); wherein A is the severity score for an offensive word in the plurality of offensive words; B is a function of a length of the offensive word; C is the calculated distance between the candidate word and the offensive word; and T is a threshold value; and determining whether the candidate word is an offender word based on whether the highest offensiveness score in the plurality of offensiveness scores for the candidate word exceeds an offensiveness threshold value.
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16. A computer-implemented system, comprising: a data processor; a computer-readable memory encoded with instructions for commanding the data processors to execute steps including: receiving a plurality of offensive words, wherein each respective offensive word in the plurality of offensive words is associated with a severity score identifying the offensiveness of the respective word; receiving a string of words, wherein a candidate word is selected from the string of words; calculating, for each respective offensive word in the plurality of offensive words, a distance between the candidate word and the respective offensive word; calculating a plurality of offensiveness scores for the candidate word, each offensiveness score in the plurality of offensiveness scores based upon (i) the calculated distance between the candidate word and an offensive word in the plurality of offensive words and (ii) the severity score of the offensive word, wherein the plurality of offensiveness scores are calculated according to one or more of:
offensiveness score= A *(( B−C )/ B );
offensiveness score= A *(( B −(1/ C ))/ B );
offensiveness score=Max((( A−C )/ A ),0); and
offensiveness score=((( B−C )/ B )> T ); wherein A is the severity score for an offensive word in the plurality of offensive words; B is a function of a length of the offensive word; C is the calculated distance between the candidate word and the offensive word; and T is a threshold value; and determining whether the candidate word is an offender word based on whether the highest offensiveness score in the plurality of offensiveness scores for the candidate word exceeds an offensiveness threshold value. 29. The system of claim 16 , wherein the plurality of offensive words and severity score identifying each of the plurality of offensive words are identified by a user, a service administrator, a third-party, or any combination thereof.
| 0.721564 |
8,583,576 | 2 | 3 |
2. The method of claim 1 , further comprising: associating each of one or more of the selected model implementations with a node in a directed graph, wherein for one or more ordered pairs of nodes in the graph the prediction output of a model implementation associated with a tail node in the pair serves as input to a model implementation associated with a head node in the pair.
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2. The method of claim 1 , further comprising: associating each of one or more of the selected model implementations with a node in a directed graph, wherein for one or more ordered pairs of nodes in the graph the prediction output of a model implementation associated with a tail node in the pair serves as input to a model implementation associated with a head node in the pair. 3. The method of claim 2 , further comprising executing each model implementation in an order prescribed by the directed graph.
| 0.5 |
8,521,723 | 1 | 6 |
1. A method comprising: receiving a first query; transforming the first query, comprising an outer query that includes a selection of a subquery that (a) returns at most one row and (b) includes a first predicate that references a database object that is from the outer query and outside the subquery, to a second query that includes: a second predicate that corresponds to the first predicate, wherein the second predicate references an outer join, wherein the second query does not select any subquery having the first predicate or the second predicate, and wherein the second query is semantically equivalent to the first query; causing execution of the second query instead of the first query; wherein the method is performed by one or more computing devices.
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1. A method comprising: receiving a first query; transforming the first query, comprising an outer query that includes a selection of a subquery that (a) returns at most one row and (b) includes a first predicate that references a database object that is from the outer query and outside the subquery, to a second query that includes: a second predicate that corresponds to the first predicate, wherein the second predicate references an outer join, wherein the second query does not select any subquery having the first predicate or the second predicate, and wherein the second query is semantically equivalent to the first query; causing execution of the second query instead of the first query; wherein the method is performed by one or more computing devices. 6. The method of claim 1 , wherein the subquery includes a first set of two or more predicates that reference one or more database objects that are not in the subquery, wherein the second query includes a second set of two or more predicates that correspond to the first set of two or more predicates, wherein the second set of two or more predicates are not in the subquery.
| 0.5 |
8,000,964 | 1 | 4 |
1. A method of constructing a model of recognizing English pronunciation variations, applying to a computer connected to a non-transitory recording medium, for recognizing English pronunciations with intonations influenced by different non-English native languages, the method at least comprising: 1) providing a plurality of English expressions and at least one phonetic alphabet corresponding to each of the English expressions by the non-transitory recording medium, and collecting a plurality of corresponding sound information according to the phonetic alphabet of each of the English expression by the computer; 2) corresponding phonetic alphabets of the non-English native language and English to a plurality of international phonetic alphabets (IPAs) by the computer, so as to form a plurality of pronunciation models, wherein the computer forms each pronunciation models; 2-1) collecting a plurality of phonetic alphabet pronunciations directed to one of the IPAs, and converts each of the phonetic alphabet pronunciations into a corresponding characteristic value; 2-2) forming the characteristic values into a value group and calculates a grouping threshold value corresponding to the characteristic values; 2-3) calculating the computer calculates a mean value of the value group; 2-4) obtaining a first characteristic value from the value group which is away from the mean value by a maximum numerical distance; 2-5) calculating a second characteristic value in the value group which is away from the first characteristic value by a maximum numerical distance; 2-6) calculating numerical distances, wherein a first distance is calculated between each characteristic value and the first characteristic value and a second distance is calculated between each characteristic value and the second characteristic value, and forming value groups by the first distances and the second distances, one of the two value groups containing the characteristic values close to the first characteristic value and the other one of the two value groups containing the characteristic values close to the second characteristic value, respectively; 2-7) obtaining a within-group distance and a between-group distance of the two value groups, so as to calculate a grouping standard; and 2-8) determining whether the grouping standard is higher than the grouping threshold value through comparison, if yes, calculating each mean value of the two value groups and then, the step 2-4) to the step 2-8) are repeated for each one of the two value groups respectively, and if no, obtaining each value group of the pronunciation model that the computer want to form; 3) converting the sound information of each of the English expressions by using the pronunciation models, and constructing a pronunciation variation network corresponding to the English expression with reference to the phonetic alphabet of the English expression by the computer, so as to detect whether each of the English expressions has a pronunciation variation path; and 4) summarizing each of the pronunciation variation paths to form a plurality of pronunciation variation rules by the computer.
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1. A method of constructing a model of recognizing English pronunciation variations, applying to a computer connected to a non-transitory recording medium, for recognizing English pronunciations with intonations influenced by different non-English native languages, the method at least comprising: 1) providing a plurality of English expressions and at least one phonetic alphabet corresponding to each of the English expressions by the non-transitory recording medium, and collecting a plurality of corresponding sound information according to the phonetic alphabet of each of the English expression by the computer; 2) corresponding phonetic alphabets of the non-English native language and English to a plurality of international phonetic alphabets (IPAs) by the computer, so as to form a plurality of pronunciation models, wherein the computer forms each pronunciation models; 2-1) collecting a plurality of phonetic alphabet pronunciations directed to one of the IPAs, and converts each of the phonetic alphabet pronunciations into a corresponding characteristic value; 2-2) forming the characteristic values into a value group and calculates a grouping threshold value corresponding to the characteristic values; 2-3) calculating the computer calculates a mean value of the value group; 2-4) obtaining a first characteristic value from the value group which is away from the mean value by a maximum numerical distance; 2-5) calculating a second characteristic value in the value group which is away from the first characteristic value by a maximum numerical distance; 2-6) calculating numerical distances, wherein a first distance is calculated between each characteristic value and the first characteristic value and a second distance is calculated between each characteristic value and the second characteristic value, and forming value groups by the first distances and the second distances, one of the two value groups containing the characteristic values close to the first characteristic value and the other one of the two value groups containing the characteristic values close to the second characteristic value, respectively; 2-7) obtaining a within-group distance and a between-group distance of the two value groups, so as to calculate a grouping standard; and 2-8) determining whether the grouping standard is higher than the grouping threshold value through comparison, if yes, calculating each mean value of the two value groups and then, the step 2-4) to the step 2-8) are repeated for each one of the two value groups respectively, and if no, obtaining each value group of the pronunciation model that the computer want to form; 3) converting the sound information of each of the English expressions by using the pronunciation models, and constructing a pronunciation variation network corresponding to the English expression with reference to the phonetic alphabet of the English expression by the computer, so as to detect whether each of the English expressions has a pronunciation variation path; and 4) summarizing each of the pronunciation variation paths to form a plurality of pronunciation variation rules by the computer. 4. The method of constructing a model of recognizing English pronunciation variations as claimed in claim 1 , wherein the phonetic alphabet pronunciation is transformed into the characteristic value by using Fourier Transform equation.
| 0.829215 |
9,058,393 | 1 | 18 |
1. A system, comprising: A) a database running on one or more server computers communicatively coupled to a network, said database comprising one or more data records, each of said one or more data records comprising: i) a text string; and ii) a monetary value associated with said text string; B) said one or more server computers running a domain name appraisal module configured to: i) receive an appraisal request for a domain name; ii) set an appraisal of said domain name to 0; iii) identify a keyword within said domain name; iv) determine the existence, within said database, of one or more matching data records wherein said text string matches said keyword; v) responsive to a determination that said one or more matching data records do not exist within said database, generate a keyword appraisal value of 0; vi) responsive to a determination that said one or more matching data records exist within said database: a) identify a keyword frequency count comprising a quantity of said one or more matching data records; b) identify a keyword monetary value comprising a sum of said monetary value, associated with said text string, for all of said one or more matching data records; c) generate said keyword appraisal value comprising a quotient calculated by dividing said keyword monetary value by said keyword frequency count; d) add said keyword appraisal value to said appraisal value of said domain name; and vii) transmit said appraisal value to one or more client computers communicatively coupled to said network.
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1. A system, comprising: A) a database running on one or more server computers communicatively coupled to a network, said database comprising one or more data records, each of said one or more data records comprising: i) a text string; and ii) a monetary value associated with said text string; B) said one or more server computers running a domain name appraisal module configured to: i) receive an appraisal request for a domain name; ii) set an appraisal of said domain name to 0; iii) identify a keyword within said domain name; iv) determine the existence, within said database, of one or more matching data records wherein said text string matches said keyword; v) responsive to a determination that said one or more matching data records do not exist within said database, generate a keyword appraisal value of 0; vi) responsive to a determination that said one or more matching data records exist within said database: a) identify a keyword frequency count comprising a quantity of said one or more matching data records; b) identify a keyword monetary value comprising a sum of said monetary value, associated with said text string, for all of said one or more matching data records; c) generate said keyword appraisal value comprising a quotient calculated by dividing said keyword monetary value by said keyword frequency count; d) add said keyword appraisal value to said appraisal value of said domain name; and vii) transmit said appraisal value to one or more client computers communicatively coupled to said network. 18. The system of claim 1 , wherein said domain name appraisal module is further configured to write said keyword frequency count and said keyword monetary value to said database.
| 0.924916 |
9,122,958 | 1 | 4 |
1. An object recognition system comprising: an object detection module configured to apply a cascade classifier to a source image, wherein application of the cascade classifier results in identification of candidate objects for a predetermined object type; a plurality of verification tests, each of the verification tests configured to generate a plurality of difference values for a candidate object identified by the object detection module and a corresponding reference image, wherein the corresponding reference image depicts an object of the predetermined object type, and wherein each one of the difference values represents an indication of a difference between a characteristic of the candidate object and a characteristic of the corresponding reference image; a scoring module configured to determine, for each of the candidate objects, a belief score for the candidate object based on the difference values for the candidate object, wherein the belief score indicates a likelihood that the candidate object is of the predetermined object type; and a verification module configured to identify a set of detected objects based on the candidate objects and the belief scores for the candidate objects.
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1. An object recognition system comprising: an object detection module configured to apply a cascade classifier to a source image, wherein application of the cascade classifier results in identification of candidate objects for a predetermined object type; a plurality of verification tests, each of the verification tests configured to generate a plurality of difference values for a candidate object identified by the object detection module and a corresponding reference image, wherein the corresponding reference image depicts an object of the predetermined object type, and wherein each one of the difference values represents an indication of a difference between a characteristic of the candidate object and a characteristic of the corresponding reference image; a scoring module configured to determine, for each of the candidate objects, a belief score for the candidate object based on the difference values for the candidate object, wherein the belief score indicates a likelihood that the candidate object is of the predetermined object type; and a verification module configured to identify a set of detected objects based on the candidate objects and the belief scores for the candidate objects. 4. The system of claim 1 , wherein the characteristic of the candidate object includes color in a hue, saturation, and value color space.
| 0.5 |
8,280,448 | 1 | 5 |
1. A method of controlling a mobile terminal, the method comprising: displaying a character input window for inputting a character string via a touch input on a touch screen; receiving an input character string through the character input window, the input character having a configuration; and outputting a control signal for controlling a haptic module to generate a haptic effect corresponding to the configuration of the input character string.
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1. A method of controlling a mobile terminal, the method comprising: displaying a character input window for inputting a character string via a touch input on a touch screen; receiving an input character string through the character input window, the input character having a configuration; and outputting a control signal for controlling a haptic module to generate a haptic effect corresponding to the configuration of the input character string. 5. The method of claim 1 , further comprising: outputting a control signal for controlling the haptic module to generate a haptic effect corresponding to a predefined word when the input character string comprises the predefined word.
| 0.5 |
10,163,439 | 1 | 5 |
1. A computer-implemented method comprising: during a trigger phrase enrollment process: prompting, by a speech recognition-enabled electronic device, a user of the speech recognition-enabled electronic device to speak a trigger phrase; receiving, at the speech recognition-enabled device, a first audio signal corresponding to the user speaking the trigger phrase; selecting, by the speech recognition-enabled device, a shortest segment among a plurality of segments in the first audio signal that have voice activity, each segment comprising a corresponding sequence of contiguous frames in the first audio signal that have voice activity; determining, by the data processing hardware, a length of the shortest segment by counting a number of frames in the sequence of contiguous frames corresponding to the shortest segment; and when the length of the shortest segment in the first audio signal satisfies a threshold value, training, by the speech recognition-enabled electronic device, a trigger phrase model with the first audio signal corresponding to the user speaking the trigger phrase, the trigger phrase model configured to detect the trigger phrase in a spoken utterance; and after the trigger phrase enrollment process: receiving, at the speech recognition-enabled device and while the speech recognition-enabled electronic device is in a sleep mode, a second audio signal including an utterance of the trigger phrase spoken by the user; and detecting, by the speech recognition-enabled electronic device and using the trigger phrase model trained during the trigger phrase enrollment process, the utterance of the trigger phrase in the second audio signal, the trigger phrase when detected in the second audio signal causing the speech recognition-enabled electronic device to wake from the sleep mode, the sleep mode comprising a power-saving mode of operation in which one or more parts of the speech recognition-enabled electronic device are in a low-power state or powered off.
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1. A computer-implemented method comprising: during a trigger phrase enrollment process: prompting, by a speech recognition-enabled electronic device, a user of the speech recognition-enabled electronic device to speak a trigger phrase; receiving, at the speech recognition-enabled device, a first audio signal corresponding to the user speaking the trigger phrase; selecting, by the speech recognition-enabled device, a shortest segment among a plurality of segments in the first audio signal that have voice activity, each segment comprising a corresponding sequence of contiguous frames in the first audio signal that have voice activity; determining, by the data processing hardware, a length of the shortest segment by counting a number of frames in the sequence of contiguous frames corresponding to the shortest segment; and when the length of the shortest segment in the first audio signal satisfies a threshold value, training, by the speech recognition-enabled electronic device, a trigger phrase model with the first audio signal corresponding to the user speaking the trigger phrase, the trigger phrase model configured to detect the trigger phrase in a spoken utterance; and after the trigger phrase enrollment process: receiving, at the speech recognition-enabled device and while the speech recognition-enabled electronic device is in a sleep mode, a second audio signal including an utterance of the trigger phrase spoken by the user; and detecting, by the speech recognition-enabled electronic device and using the trigger phrase model trained during the trigger phrase enrollment process, the utterance of the trigger phrase in the second audio signal, the trigger phrase when detected in the second audio signal causing the speech recognition-enabled electronic device to wake from the sleep mode, the sleep mode comprising a power-saving mode of operation in which one or more parts of the speech recognition-enabled electronic device are in a low-power state or powered off. 5. The computer-implemented method of claim 1 , wherein selecting the shortest segment among the plurality of segments in the first audio signal that have voice activity comprises determining a lowest number of contiguous frames in the received audio signal that comprise the accept enrollment flag.
| 0.834441 |
10,095,786 | 31 | 33 |
31. The computer readable non-transitory storage medium of claim 25 , scoring further comprising: determining, using the media content item feature space and the auxiliary data feature space, a shared dictionary comprising a plurality of canonical patterns shared by the first media content item and the auxiliary data; and scoring the plurality of segments of the first media content item, for each segment of the plurality of segments, the scoring comprising determining a measure of similarity of the segment to the descriptive information using the shared dictionary.
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31. The computer readable non-transitory storage medium of claim 25 , scoring further comprising: determining, using the media content item feature space and the auxiliary data feature space, a shared dictionary comprising a plurality of canonical patterns shared by the first media content item and the auxiliary data; and scoring the plurality of segments of the first media content item, for each segment of the plurality of segments, the scoring comprising determining a measure of similarity of the segment to the descriptive information using the shared dictionary. 33. The computer readable non-transitory storage medium of claim 31 , determining a shared library further comprising: determining a first set of coefficients for use with the shared dictionary in approximating the plurality of feature descriptor values of each unit of the plurality of units of the first media content item; determining a second set of coefficients for use with the shared dictionary in approximating the plurality of feature descriptor values of each second media content item of the plurality of second media content items of the auxiliary data; and determining third and fourth sets of coefficients, the third set of coefficients for use with the plurality of feature descriptor values of each unit of the plurality of units and the fourth set of coefficients for use with the plurality of feature descriptor values of each second media content item of the plurality of second media content items of auxiliary data in approximating the shared dictionary.
| 0.5 |
7,865,955 | 1 | 2 |
1. An apparatus for extracting attacking packet signature candidates comprising: a processor and a memory; a packet separator for separating a network packet into a header and a payload; a header information parser for parsing the header information; a traffic information generator for generating traffic information based on the parsed value; a substring extractor, having a processor and a memory, for measuring a frequency of appearing of a substring with a predetermined length in the separated payload for a constant observation period, and extracting a substring having a frequency higher than a predetermined setup value by updating the measured frequency information to a substring frequency table, wherein the updating includes increasing said frequency information of the substring that is less frequently shown previously by a larger increment amount; and a signature candidate extractor for generating a signature by collecting the extracted substring information of the substring frequency table and the generated traffic information, updating a signature frequency table, and extracting a signature candidate with reference to the signature frequency table.
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1. An apparatus for extracting attacking packet signature candidates comprising: a processor and a memory; a packet separator for separating a network packet into a header and a payload; a header information parser for parsing the header information; a traffic information generator for generating traffic information based on the parsed value; a substring extractor, having a processor and a memory, for measuring a frequency of appearing of a substring with a predetermined length in the separated payload for a constant observation period, and extracting a substring having a frequency higher than a predetermined setup value by updating the measured frequency information to a substring frequency table, wherein the updating includes increasing said frequency information of the substring that is less frequently shown previously by a larger increment amount; and a signature candidate extractor for generating a signature by collecting the extracted substring information of the substring frequency table and the generated traffic information, updating a signature frequency table, and extracting a signature candidate with reference to the signature frequency table. 2. The apparatus according to claim 1 , further comprising an allowable list information storing unit for interrupting a related process before measuring the frequency of the substring in the substring extractor if the extracted substring information is identical to a pre-stored substring information by storing information about an allowable substring.
| 0.694828 |
7,774,746 | 1 | 23 |
1. A method of generating code, comprising: receiving a specification of one or more translation patterns; using at least one of the one or more translation patterns to generate using a processor at least a portion of a first code associated with a first translator, wherein the first translator is configured to create a target object model, including by populating one or more elements of the target object model in a processing order at least in part associated with an order of elements in at least one of the one or more translation patterns; using at least one of the one or more translation patterns to generate at least a portion of a second code associated with a second translator; and connecting together the first translator and the second translator to form at least a portion of a converter.
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1. A method of generating code, comprising: receiving a specification of one or more translation patterns; using at least one of the one or more translation patterns to generate using a processor at least a portion of a first code associated with a first translator, wherein the first translator is configured to create a target object model, including by populating one or more elements of the target object model in a processing order at least in part associated with an order of elements in at least one of the one or more translation patterns; using at least one of the one or more translation patterns to generate at least a portion of a second code associated with a second translator; and connecting together the first translator and the second translator to form at least a portion of a converter. 23. A method as recited in claim 1 , wherein the target object model includes data accessed by a translator associated with another target object model.
| 0.790055 |
8,818,932 | 4 | 5 |
4. The method of claim 3 , wherein the method further comprises the steps of: converting extracted entities and attributes into sets of entity-attribute-value and entity-entity-relationship; automatically determining the number of kinds of objects contained in the entity-attribute-value set; assigning attributes to the kind of object described; and, automatically determining rules which govern entities in the entity-entity-relationship set.
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4. The method of claim 3 , wherein the method further comprises the steps of: converting extracted entities and attributes into sets of entity-attribute-value and entity-entity-relationship; automatically determining the number of kinds of objects contained in the entity-attribute-value set; assigning attributes to the kind of object described; and, automatically determining rules which govern entities in the entity-entity-relationship set. 5. The method of claim 4 , wherein the method further comprises the step of: converting segments into object-oriented Bayesian Networks.
| 0.5 |
7,680,783 | 10 | 14 |
10. A search system comprising a computer-readable storage medium storing instructions to be executed by a processor, the instructions, when executed, implementing: an identification strategy associating a parsing grammar in a data entry field with a search algorithm, the parsing grammar comprising parsing grammar rules which split data into a plurality of token names, each token name identifying data in a section of a grammatical statement; a plurality of expression formats, at least one expression format preceding each of the plurality of token names, each expression format representing a number of alphanumeric characters of the identified data of the token name that the expression format precedes; and a punctuation, the punctuation separating each of the plurality of token names; the search algorithm comprising search rules, a search rule producing a query identifying a field in a database to be searched using the data identified by at least one token name in the parsing grammar; a configuration interface displayed on a computer display, the configuration interface receiving a change to the identification strategy; a configuration engine which updates the identification strategy to reflect the change; an application interface displayed on one of the computer display and a second computer display, the application interface receiving search data in the data entry field; a parsing engine to apply the parsing grammar to split the search data by: checking whether the entered data contains the punctuation; checking whether the entered data before the punctuation contains the number of alphanumeric characters required by the expression format for the token name preceding the punctuation; and checking whether the entered data after the punctuation contains the number of alphanumeric characters required by the expression format for the token name following the punctuation; a query generating engine to apply the search algorithm to produce a search query using at least a portion of the search data corresponding to at least one token name; and a search engine to execute the search query in a database to produce and return a results set.
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10. A search system comprising a computer-readable storage medium storing instructions to be executed by a processor, the instructions, when executed, implementing: an identification strategy associating a parsing grammar in a data entry field with a search algorithm, the parsing grammar comprising parsing grammar rules which split data into a plurality of token names, each token name identifying data in a section of a grammatical statement; a plurality of expression formats, at least one expression format preceding each of the plurality of token names, each expression format representing a number of alphanumeric characters of the identified data of the token name that the expression format precedes; and a punctuation, the punctuation separating each of the plurality of token names; the search algorithm comprising search rules, a search rule producing a query identifying a field in a database to be searched using the data identified by at least one token name in the parsing grammar; a configuration interface displayed on a computer display, the configuration interface receiving a change to the identification strategy; a configuration engine which updates the identification strategy to reflect the change; an application interface displayed on one of the computer display and a second computer display, the application interface receiving search data in the data entry field; a parsing engine to apply the parsing grammar to split the search data by: checking whether the entered data contains the punctuation; checking whether the entered data before the punctuation contains the number of alphanumeric characters required by the expression format for the token name preceding the punctuation; and checking whether the entered data after the punctuation contains the number of alphanumeric characters required by the expression format for the token name following the punctuation; a query generating engine to apply the search algorithm to produce a search query using at least a portion of the search data corresponding to at least one token name; and a search engine to execute the search query in a database to produce and return a results set. 14. The system of claim 10 , wherein the database resides in volatile memory.
| 0.827354 |
8,392,353 | 4 | 6 |
4. A frame-based Knowledge Representation System (KRS) on a non-transitory computer readable storage medium, populated with facts, the facts having been entered into the system by the step of: completing at least one fact-type specific fact template, each fact-type specific fact template accepting one predefined fact type found in predetermined information sources, wherein the at least one fact-type specific fact template comprises at least one user interface that constrains user data entry to one of a predetermined set of valid user entries that may be inserted into the fact template at the user interface, and wherein the user interface includes a user entry field that permits entry of new information not included in the predetermined set of valid user entries; transferring a plurality of structured facts from completed fact templates into the KRS to form a knowledge base, the structured facts being derived from natural language information sources; wherein the KRS is an ontology having varying levels of abstraction of biological concepts and the structured facts correspond to one or more of the varying levels of abstraction of biological concepts and wherein information entered into the user entry field automatically flags the completed fact template for review.
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4. A frame-based Knowledge Representation System (KRS) on a non-transitory computer readable storage medium, populated with facts, the facts having been entered into the system by the step of: completing at least one fact-type specific fact template, each fact-type specific fact template accepting one predefined fact type found in predetermined information sources, wherein the at least one fact-type specific fact template comprises at least one user interface that constrains user data entry to one of a predetermined set of valid user entries that may be inserted into the fact template at the user interface, and wherein the user interface includes a user entry field that permits entry of new information not included in the predetermined set of valid user entries; transferring a plurality of structured facts from completed fact templates into the KRS to form a knowledge base, the structured facts being derived from natural language information sources; wherein the KRS is an ontology having varying levels of abstraction of biological concepts and the structured facts correspond to one or more of the varying levels of abstraction of biological concepts and wherein information entered into the user entry field automatically flags the completed fact template for review. 6. The KRS of claim 4 , wherein the fact templates structure and constrain a fact extracted from the information sources according to a user interface that constrains user data entry based upon a set of valid entries consistent with the structure and content of the ontology.
| 0.5 |
7,801,906 | 5 | 6 |
5. The computer implemented method of claim 1 wherein converting of another file retrieval request is unsuccessful and subsequently: determining if Roman text encoding is a default text encoding; and if Roman text encoding is not a default text encoding, then using Roman text encoding to convert the retrieval request to Unicode text encoding.
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5. The computer implemented method of claim 1 wherein converting of another file retrieval request is unsuccessful and subsequently: determining if Roman text encoding is a default text encoding; and if Roman text encoding is not a default text encoding, then using Roman text encoding to convert the retrieval request to Unicode text encoding. 6. The computer implemented method of claim 5 wherein the encoding bitmap is located in the memory device.
| 0.5 |
9,480,908 | 1 | 7 |
1. A networked computer system for generating marketing data, comprising: a player database comprising records related to players of a plurality of players, a record including, for a player, a player ID of the player and demographic data of the player; a game data database comprising records related to game rounds, wherein a game round record includes an answer and a list of clues that can be offered from a first clue-giving player to a guessing player for the guessing player to attempt to guess the answer; a weighting generator for generating cost weights for a plurality of clues in the list of clues for a game round record, wherein the weighting generator generates cost weights such that, over a distributed plurality of game rounds played a plurality of times, selections of clues by the clue-giving player from the list of clues is more reflective of revealed sentiment than of stated opinion, wherein cost weights for more obvious clues are greater than cost weights for less obvious clues and clues more reflective of revealed sentiment than of stated opinion; a game results database that records clue selections by a plurality of clue-giving players, wherein the plurality of clue-giving players comprises a large enough number of clue-giving players to result in the selections of clues by the plurality of clue-giving players being more reflective of revealed sentiment distributed over the large enough number of clue-giving players than of individual stated opinions or preferences; and logic for converting game results from the game results database into the marketing data.
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1. A networked computer system for generating marketing data, comprising: a player database comprising records related to players of a plurality of players, a record including, for a player, a player ID of the player and demographic data of the player; a game data database comprising records related to game rounds, wherein a game round record includes an answer and a list of clues that can be offered from a first clue-giving player to a guessing player for the guessing player to attempt to guess the answer; a weighting generator for generating cost weights for a plurality of clues in the list of clues for a game round record, wherein the weighting generator generates cost weights such that, over a distributed plurality of game rounds played a plurality of times, selections of clues by the clue-giving player from the list of clues is more reflective of revealed sentiment than of stated opinion, wherein cost weights for more obvious clues are greater than cost weights for less obvious clues and clues more reflective of revealed sentiment than of stated opinion; a game results database that records clue selections by a plurality of clue-giving players, wherein the plurality of clue-giving players comprises a large enough number of clue-giving players to result in the selections of clues by the plurality of clue-giving players being more reflective of revealed sentiment distributed over the large enough number of clue-giving players than of individual stated opinions or preferences; and logic for converting game results from the game results database into the marketing data. 7. The networked computer system of claim 1 , further comprising: a plurality of clue-selection game displays, presented to at least some of the plurality of players including the clue-giving player, wherein a clue-selection game display includes a representation of the list of clues and their respective cost weights, thereby allowing the clue-giving player to select clues from among the list of clues; and a plurality of guessing game displays, presented to at least some of the plurality of players including the guessing player, wherein a guessing game display includes a representation of clues selected by the clue-giving player and a countdown timer, wherein an initial value of the countdown timer is a function of cost weights assigned to the clues selected by the clue-giving player and shown to the guessing player, with more obvious clues causing the countdown timer to be more decremented than for less obvious clues, for sentiment-oriented words, and/or for words more useful for market research purposes.
| 0.5 |
8,996,353 | 21 | 27 |
21. A computer program product stored in one or more non-transitory storage media for controlling a processing mode of a data processing apparatus, the computer program product being executable by the data processing apparatus to cause the data processing apparatus to perform operations comprising: obtaining, from a client device of a user, a text message in a first language, the text message comprising at least one word; providing the text message to a machine translation system; obtaining a translation of the text message from the machine translation system; determining that the text message and the translation both comprise the at least one word in the first language and that the at least one word is correctly spelled; and performing one or more of the following: (a) determining Bayesian probabilities for neighboring words that appear before and after the at least one word and, when the Bayesian probabilities exceed a threshold value, adding the at least one word to a lexicon in a data store; and (b) performing k-means clustering to identify a cluster of words comprising synonyms and, when the cluster comprises the at least one word, adding the at least one word to the lexicon in the data store.
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21. A computer program product stored in one or more non-transitory storage media for controlling a processing mode of a data processing apparatus, the computer program product being executable by the data processing apparatus to cause the data processing apparatus to perform operations comprising: obtaining, from a client device of a user, a text message in a first language, the text message comprising at least one word; providing the text message to a machine translation system; obtaining a translation of the text message from the machine translation system; determining that the text message and the translation both comprise the at least one word in the first language and that the at least one word is correctly spelled; and performing one or more of the following: (a) determining Bayesian probabilities for neighboring words that appear before and after the at least one word and, when the Bayesian probabilities exceed a threshold value, adding the at least one word to a lexicon in a data store; and (b) performing k-means clustering to identify a cluster of words comprising synonyms and, when the cluster comprises the at least one word, adding the at least one word to the lexicon in the data store. 27. The computer program product of claim 21 , wherein determining Bayesian probabilities comprises (i) reviewing previous uses of the at least one word in prior text messages and (ii) identifying words, if any, that appear before and after the at least one word in the prior text messages.
| 0.5 |
7,844,464 | 1 | 15 |
1. A method performed by a computer processor executing computer program instructions tangibly stored on a first computer-readable medium to perform a method comprising steps of: (A) deriving, from a region of a document and a corresponding region of a spoken audio stream, a likelihood that the region of the document correctly represents content in the corresponding region of the spoken audio stream; (B) selecting a measure of relevance of the region of the spoken audio stream, the measure of relevance representing a measure of importance that the region of the spoken audio stream be brought to the attention of a human proofreader; and (C) deriving, using the processor, from the stored representation of the likelihood and the stored representation of the measure of relevance, an emphasis factor that modifies emphasis placed on the region of the spoken audio stream when played back, wherein (C) comprises: (C)(1) identifying a rule that identifies the emphasis factor based on the identified likelihood and the identified measure of relevance; and (C)(2) applying the rule to the identified likelihood and the identified measure of relevance to derive the emphasis factor; where (C)(2) comprises: (C)(2)(a) identifying a first weight associated with the identified likelihood; (C)(2)(b) identifying a second weight associated with the measure of relevance; and (C)(2)(c) deriving the emphasis factor from a combination of the identified likelihood and the measure of relevance weighted by the first and second weights, respectively.
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1. A method performed by a computer processor executing computer program instructions tangibly stored on a first computer-readable medium to perform a method comprising steps of: (A) deriving, from a region of a document and a corresponding region of a spoken audio stream, a likelihood that the region of the document correctly represents content in the corresponding region of the spoken audio stream; (B) selecting a measure of relevance of the region of the spoken audio stream, the measure of relevance representing a measure of importance that the region of the spoken audio stream be brought to the attention of a human proofreader; and (C) deriving, using the processor, from the stored representation of the likelihood and the stored representation of the measure of relevance, an emphasis factor that modifies emphasis placed on the region of the spoken audio stream when played back, wherein (C) comprises: (C)(1) identifying a rule that identifies the emphasis factor based on the identified likelihood and the identified measure of relevance; and (C)(2) applying the rule to the identified likelihood and the identified measure of relevance to derive the emphasis factor; where (C)(2) comprises: (C)(2)(a) identifying a first weight associated with the identified likelihood; (C)(2)(b) identifying a second weight associated with the measure of relevance; and (C)(2)(c) deriving the emphasis factor from a combination of the identified likelihood and the measure of relevance weighted by the first and second weights, respectively. 15. The method of claim 1 , wherein the step (A) comprises a step of: (A)(1) deriving the likelihood from a feature of the spoken audio stream.
| 0.87 |
9,336,269 | 15 | 16 |
15. A computer system comprising: one or more computing devices; an interface at the one or more computing devices that is programmed to receive a query; a knowledge repository that is accessible to the one or more computing devices and that includes a plurality of topics, each topic including one or more attributes and associated values for the attributes; a mapping module that is installed on the one or more computing devices and that identifies one or more candidate topics from the topics in the knowledge repository, wherein the identified candidate topics are determined to relate to a possible subject of the query; an answer generator that is installed on the one or more computing devices and that generates, for each candidate topic, a candidate topic-answer pair that includes (i) the candidate topic, and (ii) an answer to the query for the candidate topic, wherein the answer for each candidate topic is identified from information in the knowledge repository; a search engine that is installed on the one or more computing devices and that returns search results based on the query, wherein one or more of the search results references an annotated resource, wherein an annotated resource is a resource that, based on an automated evaluation of the content of the resource, is associated with an annotation that identifies one or more likely topics associated with the resource; a scoring module that is installed on the one or more computing devices and that determines a score for each candidate topic-answer pair based on (i) an occurrence of the candidate topic in the annotations of the resources referenced by one or more of the search results, and (ii) an occurrence of the answer in annotations of the resources referenced by the one or more search results, or in the resources referenced by the one or more search results; and a front end system at the one or more computing devices that determines whether to respond to the query with one or more answers from the candidate topic-answer pairs, based on the scores.
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15. A computer system comprising: one or more computing devices; an interface at the one or more computing devices that is programmed to receive a query; a knowledge repository that is accessible to the one or more computing devices and that includes a plurality of topics, each topic including one or more attributes and associated values for the attributes; a mapping module that is installed on the one or more computing devices and that identifies one or more candidate topics from the topics in the knowledge repository, wherein the identified candidate topics are determined to relate to a possible subject of the query; an answer generator that is installed on the one or more computing devices and that generates, for each candidate topic, a candidate topic-answer pair that includes (i) the candidate topic, and (ii) an answer to the query for the candidate topic, wherein the answer for each candidate topic is identified from information in the knowledge repository; a search engine that is installed on the one or more computing devices and that returns search results based on the query, wherein one or more of the search results references an annotated resource, wherein an annotated resource is a resource that, based on an automated evaluation of the content of the resource, is associated with an annotation that identifies one or more likely topics associated with the resource; a scoring module that is installed on the one or more computing devices and that determines a score for each candidate topic-answer pair based on (i) an occurrence of the candidate topic in the annotations of the resources referenced by one or more of the search results, and (ii) an occurrence of the answer in annotations of the resources referenced by the one or more search results, or in the resources referenced by the one or more search results; and a front end system at the one or more computing devices that determines whether to respond to the query with one or more answers from the candidate topic-answer pairs, based on the scores. 16. The computer system of claim 15 , wherein the front end system determines whether to respond to the query based on a comparison of one or more of the scores to a predetermined threshold.
| 0.759494 |
8,676,800 | 5 | 7 |
5. The method defined by claim 1 wherein the derived attribute is one or more of the following: (a) an aggregate data item; (b) a text condenser attribute; (c) any other result of preprocessing data that extracts one or more high level concept data items from a plurality of data items thereby reducing data complexity.
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5. The method defined by claim 1 wherein the derived attribute is one or more of the following: (a) an aggregate data item; (b) a text condenser attribute; (c) any other result of preprocessing data that extracts one or more high level concept data items from a plurality of data items thereby reducing data complexity. 7. A method defined by claim 5 wherein the text condenser attribute maps one or more sequences of key terms to a key concept, and wherein a key term is a regular expression referring to a fragment of freeform text such that information can be extracted from freeform text.
| 0.5 |
8,020,187 | 1 | 2 |
1. A method for associating an electronic document work with an action, the document work comprising text, the method comprising: a) electronically extracting within a client device features from the electronic document work; b) transmitting the extracted features from the client device to one or more servers; c) receiving at the client device from the one or more servers an identification of the electronic document work based on the extracted features, wherein the identification is based on a sub-linear search to identify at least a neighbor; d) electronically determining an action” based on the identification of the electronic document work; and e) electronically performing the action on the client device.
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1. A method for associating an electronic document work with an action, the document work comprising text, the method comprising: a) electronically extracting within a client device features from the electronic document work; b) transmitting the extracted features from the client device to one or more servers; c) receiving at the client device from the one or more servers an identification of the electronic document work based on the extracted features, wherein the identification is based on a sub-linear search to identify at least a neighbor; d) electronically determining an action” based on the identification of the electronic document work; and e) electronically performing the action on the client device. 2. The method of claim 1 , wherein the electronic document work comprises an image.
| 0.814732 |
7,877,349 | 15 | 16 |
15. The computer readable storage medium of claim 13 , wherein the expression of the semantic proximity between the variation of the string of natural language text contained in the reference rephrased object and the string of natural language text expressed in the reference free-form text entry is expressed as a percentage.
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15. The computer readable storage medium of claim 13 , wherein the expression of the semantic proximity between the variation of the string of natural language text contained in the reference rephrased object and the string of natural language text expressed in the reference free-form text entry is expressed as a percentage. 16. The computer readable storage medium of claim 15 , wherein the step of calculating the combined semantic proximity comprises multiplying the percentages of the matched reference rephrased object and user rephrased object.
| 0.5 |
8,954,314 | 1 | 7 |
1. A method comprising: receiving an input in a first language; obtaining a plurality of probable translation alternatives for a translation result, each probable translation alternative being a translation of the input into a second language; presenting a first of the plurality of probable translation alternatives on a display; determining that the device is being shaken; responsive to a determination that the device is being shaken, presenting a second of the plurality of probable translation alternatives in the alternate translation result dialog screen on the display; and responsive to subsequent determinations that the device is being shaken, presenting the alternative translation results in a loop through all of the alternative translation results until the user selects a translation result from the presented list.
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1. A method comprising: receiving an input in a first language; obtaining a plurality of probable translation alternatives for a translation result, each probable translation alternative being a translation of the input into a second language; presenting a first of the plurality of probable translation alternatives on a display; determining that the device is being shaken; responsive to a determination that the device is being shaken, presenting a second of the plurality of probable translation alternatives in the alternate translation result dialog screen on the display; and responsive to subsequent determinations that the device is being shaken, presenting the alternative translation results in a loop through all of the alternative translation results until the user selects a translation result from the presented list. 7. The method of claim 1 , wherein the input information is textual data received from a keyboard.
| 0.855457 |
9,152,730 | 9 | 12 |
9. A method, according to claim 1 , further comprising: following selecting the top score candidate, determining if the top score candidate meets predetermined criteria; and if the top score candidate does not meet predetermined criteria, determining if a different top score candidate should be selected.
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9. A method, according to claim 1 , further comprising: following selecting the top score candidate, determining if the top score candidate meets predetermined criteria; and if the top score candidate does not meet predetermined criteria, determining if a different top score candidate should be selected. 12. A method, according to claim 9 , wherein the predetermined criteria is selected from the group consisting of: whether the top score candidate has less than 25 embedded containers, wherein a container is a Web page element that is associated with tags ‘body’, div′, ‘td’, ‘article/section’, whether the top score candidate has no embedded other candidates and whether the top score candidate has no more than three embedded candidates that have other embedded candidates.
| 0.5 |
9,239,888 | 1 | 6 |
1. A method performed by a data processing apparatus, the method comprising: receiving, for a query sequence, a word boundary likelihood that represents a likelihood that the query sequence terminates at a word boundary; determining, based on the word boundary likelihood, a time delay for delaying providing search results for the query sequence; determining that an amount of time since receipt of the query sequence exceeds the time delay, and in response: identifying search results responsive to the query sequence; and providing the identified search results.
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1. A method performed by a data processing apparatus, the method comprising: receiving, for a query sequence, a word boundary likelihood that represents a likelihood that the query sequence terminates at a word boundary; determining, based on the word boundary likelihood, a time delay for delaying providing search results for the query sequence; determining that an amount of time since receipt of the query sequence exceeds the time delay, and in response: identifying search results responsive to the query sequence; and providing the identified search results. 6. The method of claim 1 , wherein determining the time delay comprises determining that the word boundary likelihood exceeds a threshold and, in response, setting the time delay to a particular value.
| 0.708696 |
8,185,425 | 23 | 32 |
23. A computer readable storage medium as recited in claim 18 , wherein the contextual date information includes a first date followed by a range indicator that is followed by a second date to thereby specify a date range that begins with the first date and ends with the second date.
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23. A computer readable storage medium as recited in claim 18 , wherein the contextual date information includes a first date followed by a range indicator that is followed by a second date to thereby specify a date range that begins with the first date and ends with the second date. 32. A computer readable storage medium as recited in claim 23 , wherein the range indicator is a “-” or a “<-->” set of characters.
| 0.5 |
9,105,268 | 6 | 8 |
6. The method of claim 2 , further comprising: a control module querying said customer with regard to an action or actions that said customer wishes to perform when said customer connects to said IVR system via a user interface; upon receiving a response from said customer, said control module forwarding said response to a speech recognition module; and said speech recognition module converting said response into text using techniques based on a special grammar that is trained to identify certain keywords more accurately than others.
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6. The method of claim 2 , further comprising: a control module querying said customer with regard to an action or actions that said customer wishes to perform when said customer connects to said IVR system via a user interface; upon receiving a response from said customer, said control module forwarding said response to a speech recognition module; and said speech recognition module converting said response into text using techniques based on a special grammar that is trained to identify certain keywords more accurately than others. 8. The method of claim 6 , further comprising: said control module identifying gender and age group of said customer based on analysis of a response received from said customer.
| 0.5 |
7,818,275 | 9 | 10 |
9. An apparatus that creates a presentation to a user, comprising: a processor that executes a computer program to support the presentation; a memory that stores information under the control of the processor; logic that presents information indicative of a goal, the goal being associated with a skill required for the user in a business endeavor; logic that integrates information that motivates accomplishment of the goal; logic that monitors progress toward the goal and provides feedback that further motivates accomplishment of the goal, wherein: the feedback is characterized by a set of profiles and topics; the profiles trigger the topics in a concept tree to obtain a plurality of activated topics; and the feedback is selected from the plurality of activated topics in the concept tree by: identifying a top-most target group with an activated topic; if the top-most target group is a first type of feedback, selecting that feedback for display to the user without examining any other activated topics; and if the top-most target group is a second type of feedback different from the first type of feedback, grouping the activated feedback in the children target groups and assembling it into a feedback paragraph for display to the user; and logic that performs regression analysis of the computer program as the presentation subsequently executes.
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9. An apparatus that creates a presentation to a user, comprising: a processor that executes a computer program to support the presentation; a memory that stores information under the control of the processor; logic that presents information indicative of a goal, the goal being associated with a skill required for the user in a business endeavor; logic that integrates information that motivates accomplishment of the goal; logic that monitors progress toward the goal and provides feedback that further motivates accomplishment of the goal, wherein: the feedback is characterized by a set of profiles and topics; the profiles trigger the topics in a concept tree to obtain a plurality of activated topics; and the feedback is selected from the plurality of activated topics in the concept tree by: identifying a top-most target group with an activated topic; if the top-most target group is a first type of feedback, selecting that feedback for display to the user without examining any other activated topics; and if the top-most target group is a second type of feedback different from the first type of feedback, grouping the activated feedback in the children target groups and assembling it into a feedback paragraph for display to the user; and logic that performs regression analysis of the computer program as the presentation subsequently executes. 10. An apparatus that creates a presentation as recited in claim 9 , including logic that instantiates a particular feedback model based on the regression analysis.
| 0.62037 |
8,037,059 | 9 | 10 |
9. The method for implementing aggregation combination using rollup depth lists for optimizing database query processing as recited in claim 7 includes defining a rollup set starting with a simple rollup of all cube elements.
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9. The method for implementing aggregation combination using rollup depth lists for optimizing database query processing as recited in claim 7 includes defining a rollup set starting with a simple rollup of all cube elements. 10. The method for implementing aggregation combination using rollup depth lists for optimizing database query processing as recited in claim 9 further includes defining round robin rollups through cube elements with each cube element at a beginning of a rollup.
| 0.5 |
8,903,801 | 5 | 6 |
5. The computer-implemented method of claim 1 , wherein the identifying the plurality of database query language statements for automatic tuning and tuning the subset of database query language statements from the plurality of database query language statements are performed in a controlled environment on a machine in a manner such that activities in the controlled environment do not interrupt activities on the machine outside the controlled environment.
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5. The computer-implemented method of claim 1 , wherein the identifying the plurality of database query language statements for automatic tuning and tuning the subset of database query language statements from the plurality of database query language statements are performed in a controlled environment on a machine in a manner such that activities in the controlled environment do not interrupt activities on the machine outside the controlled environment. 6. The computer-implemented method of claim 5 , wherein the controlled environment limits, to a maximum amount of time, time spent on performing the identifying the plurality of database query language statements for automatic tuning and tuning the subset of database query language statements from the plurality of database query language statements.
| 0.616812 |
8,717,368 | 1 | 4 |
1. A computer-implemented method for rendering an animation presentation on a computing device in communication with a server, the method comprising: receiving, at the server, a request from the computing device to view the animation presentation on the computing device; obtaining, at the server based on the request from the computing device, information identifying a browser application running on the computing device, the information including at least one of a name and a version of the browser application; determining, at the server, requirements of a presentation technology supported by the identified browser application; decomposing, by the server, the animation presentation into animation primitives compatible with the presentation technology supported by the browser application, said decomposing including expressing the animation presentation as a combination of said animation primitives compatible with the presentation technology, each of said animation primitives corresponding to an animation type associated with the animation presentation, each said animation primitive having at least one of a start time, an end time, a start value, and an end value, where the start value and the end value relate to a characteristic of the animation primitive, and said combination of said animation primitives including at least two different types of animation primitives; and generating, storing in memory, and transmitting to the computing device a data stream containing the animation primitives.
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1. A computer-implemented method for rendering an animation presentation on a computing device in communication with a server, the method comprising: receiving, at the server, a request from the computing device to view the animation presentation on the computing device; obtaining, at the server based on the request from the computing device, information identifying a browser application running on the computing device, the information including at least one of a name and a version of the browser application; determining, at the server, requirements of a presentation technology supported by the identified browser application; decomposing, by the server, the animation presentation into animation primitives compatible with the presentation technology supported by the browser application, said decomposing including expressing the animation presentation as a combination of said animation primitives compatible with the presentation technology, each of said animation primitives corresponding to an animation type associated with the animation presentation, each said animation primitive having at least one of a start time, an end time, a start value, and an end value, where the start value and the end value relate to a characteristic of the animation primitive, and said combination of said animation primitives including at least two different types of animation primitives; and generating, storing in memory, and transmitting to the computing device a data stream containing the animation primitives. 4. The method of claim 1 , wherein the data stream includes an expression of the animation presentation as a combination of the animation primitives.
| 0.586111 |
7,508,324 | 8 | 12 |
8. A method for word recognition associated with finger activated text input on a reduced keyboard apparatus, the method comprising; inputting an at least one word through a keyboard adapted for displaying at least two letters and characterized by having no discrete boundary between the at least two letters, and adapted for enabling input of the at least one word by a succession of keystrokes on the keyboard, wherein a keystroke on the keyboard activates an at least one keyboard region defined according to characteristics of the keystroke and contains an at least one letter candidate therein, computing a probability value associated with the at least one letter candidate in the at least one keyboard region; selecting at least one class of an at least one dictionary, the at least one dictionary comprising an at least one word class categorized according to the first or last letters of an at least one candidate word in the at least one dictionary with which an at least one input word is associated therewith thereby producing an at least one candidate word list, and successively eliminating and sorting words in the candidate word list to provide an at least one solution for the at least one input word; wherein the step of eliminating and sorting weighs the probability value associated with the at least one letter candidate determined by the maximum horizontal distortion, the number of keystrokes performed during input of a word, and the frequency of use value of the at least one candidate word.
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8. A method for word recognition associated with finger activated text input on a reduced keyboard apparatus, the method comprising; inputting an at least one word through a keyboard adapted for displaying at least two letters and characterized by having no discrete boundary between the at least two letters, and adapted for enabling input of the at least one word by a succession of keystrokes on the keyboard, wherein a keystroke on the keyboard activates an at least one keyboard region defined according to characteristics of the keystroke and contains an at least one letter candidate therein, computing a probability value associated with the at least one letter candidate in the at least one keyboard region; selecting at least one class of an at least one dictionary, the at least one dictionary comprising an at least one word class categorized according to the first or last letters of an at least one candidate word in the at least one dictionary with which an at least one input word is associated therewith thereby producing an at least one candidate word list, and successively eliminating and sorting words in the candidate word list to provide an at least one solution for the at least one input word; wherein the step of eliminating and sorting weighs the probability value associated with the at least one letter candidate determined by the maximum horizontal distortion, the number of keystrokes performed during input of a word, and the frequency of use value of the at least one candidate word. 12. The method of claim 8 wherein the letters are arranged in three rows and wherein the method further comprises identifying the row of the keyboard upon which a keystroke was performed.
| 0.652416 |
9,811,599 | 12 | 14 |
12. The non-transitory computer-readable medium of claim 9 , the method further comprising: transmitting the brand-driven keyword data to the user; receiving acceptance information indicating whether the user accepts or declines the brand-driven keyword data; and storing the brand-driven keyword data if the user accepts the brand-driven keyword data.
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12. The non-transitory computer-readable medium of claim 9 , the method further comprising: transmitting the brand-driven keyword data to the user; receiving acceptance information indicating whether the user accepts or declines the brand-driven keyword data; and storing the brand-driven keyword data if the user accepts the brand-driven keyword data. 14. The non-transitory computer-readable medium of claim 12 , the method further comprising: determining whether the keyword matches a prohibited keyword, wherein the user automatically declines the brand-driven keyword data if it is determined that the keyword matches a prohibited keyword.
| 0.746073 |
9,971,846 | 8 | 10 |
8. A client device comprising: at least one processor; and data storage storing program instructions that upon execution by the at least one processor cause a web browser of the client device to perform operations including: based on a first document received from a server device, rendering a web page for display, wherein the rendered web page includes a set of selectable hyperlinks that are ordered based on a user preference of a user associated with the client device; determining that a scroll position of the rendered web page has passed a first threshold scroll position; responsive to determining that the scroll position of the rendered web page has passed the first threshold scroll position; requesting and receiving a second document from the server device, wherein the second document is reachable by way of a first selectable hyperlink as ordered on the rendered web page; re-rendering the web page for display, including combined content from both the first document and the second document; after re-rendering the web page for display, determining that the scroll position of the re-rendered web page has passed a second threshold scroll position; responsive to determining that the scroll position of the re-rendered web page has passed the second threshold scroll position, further re-rendering the web page to eliminate content from one of the first document or the second document; and removing a representation of the eliminated content from memory of the client device that is associated with the web browser.
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8. A client device comprising: at least one processor; and data storage storing program instructions that upon execution by the at least one processor cause a web browser of the client device to perform operations including: based on a first document received from a server device, rendering a web page for display, wherein the rendered web page includes a set of selectable hyperlinks that are ordered based on a user preference of a user associated with the client device; determining that a scroll position of the rendered web page has passed a first threshold scroll position; responsive to determining that the scroll position of the rendered web page has passed the first threshold scroll position; requesting and receiving a second document from the server device, wherein the second document is reachable by way of a first selectable hyperlink as ordered on the rendered web page; re-rendering the web page for display, including combined content from both the first document and the second document; after re-rendering the web page for display, determining that the scroll position of the re-rendered web page has passed a second threshold scroll position; responsive to determining that the scroll position of the re-rendered web page has passed the second threshold scroll position, further re-rendering the web page to eliminate content from one of the first document or the second document; and removing a representation of the eliminated content from memory of the client device that is associated with the web browser. 10. The client device of claim 8 , wherein the second document follows the first document in the ordering defined by the user preference, and wherein re-rendering the web page for display comprises re-rendering the web page to represent the first document followed by the second document.
| 0.588571 |
7,657,839 | 17 | 18 |
17. The medium of claim 10 , wherein populating the reply message comprises automatically inserting the name of the sender of the selected message, the name of the at least one recipient of the selected message, and a name of at least one second recipient into fields of the reply message corresponding to the fields from which the names originated.
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17. The medium of claim 10 , wherein populating the reply message comprises automatically inserting the name of the sender of the selected message, the name of the at least one recipient of the selected message, and a name of at least one second recipient into fields of the reply message corresponding to the fields from which the names originated. 18. The medium of claim 17 , wherein the corresponding fields of the reply message are determined by a rule set that specifies which originating fields cause names to be inserted into which address fields of the reply message with priority information should the same name originate in more than one type of field.
| 0.5 |
7,516,198 | 2 | 3 |
2. The network node of claim 1 , wherein the XML tags within the at least one message are based on a group of predetermined XML tags specifying respective prescribed quality of service attributes identifiable by the application resource, the application resource configured for selecting, for each corresponding predetermined XML tag, a corresponding prescribed network parameter for implementation of the corresponding prescribed quality of service attribute.
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2. The network node of claim 1 , wherein the XML tags within the at least one message are based on a group of predetermined XML tags specifying respective prescribed quality of service attributes identifiable by the application resource, the application resource configured for selecting, for each corresponding predetermined XML tag, a corresponding prescribed network parameter for implementation of the corresponding prescribed quality of service attribute. 3. The network node of claim 2 , wherein the application resource is configured for identifying the flow of data packets for transfer according to the selected network parameters based on flow information specified within the at least one message for the corresponding application service.
| 0.5 |
8,589,407 | 12 | 21 |
12. An apparatus for generating a suggested personalized reaction, the apparatus comprising: one or more processors; a collector module, stored on a memory and executable by the one or more processors, for collecting interaction items accessible to a first user from an electronic communication system, the interaction items including an online user post and a user reaction, the collector module coupled to receive interaction items from the electronic communication system; a suggestion analyzer module for processing the collected interaction items to produce one or more labels for the collected interaction items, ranking each collected interaction item based on the labels and the first user's prior reactions to other interaction items and the respective labels of the first user's prior reactions, determining that the online user post satisfies a threshold likelihood of being important or interesting to the first user, and automatically generating a suggested personalized reaction to online user post on behalf of the first user, the suggested personalized reaction based on the one or more labels associated with online user post; and a user interface module for presenting the suggested personalized reaction and related information and for receiving input from the first user, the user interface module coupled to receive the suggested personalized reaction from the suggestion analyzer module, the user interface module configured to receive input from the first user.
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12. An apparatus for generating a suggested personalized reaction, the apparatus comprising: one or more processors; a collector module, stored on a memory and executable by the one or more processors, for collecting interaction items accessible to a first user from an electronic communication system, the interaction items including an online user post and a user reaction, the collector module coupled to receive interaction items from the electronic communication system; a suggestion analyzer module for processing the collected interaction items to produce one or more labels for the collected interaction items, ranking each collected interaction item based on the labels and the first user's prior reactions to other interaction items and the respective labels of the first user's prior reactions, determining that the online user post satisfies a threshold likelihood of being important or interesting to the first user, and automatically generating a suggested personalized reaction to online user post on behalf of the first user, the suggested personalized reaction based on the one or more labels associated with online user post; and a user interface module for presenting the suggested personalized reaction and related information and for receiving input from the first user, the user interface module coupled to receive the suggested personalized reaction from the suggestion analyzer module, the user interface module configured to receive input from the first user. 21. The apparatus of claim 12 wherein the decision tree is an artificial intelligence-based decision tree.
| 0.84273 |
9,741,138 | 7 | 8 |
7. A computer program product for improving a computer system, the computer program product comprising a tangible non-transitory computer readable storage medium having program code embodied therewith, the program code readable and executable by a processor to perform a method comprising: receiving a data stream that describes graph nodes in a non-hierarchical graph database; defining multiple graph node clusters from the graph nodes in the non-hierarchical graph database; generating a cluster edge between two graph node clusters from the multiple graph node clusters in the non-hierarchical graph database, wherein the cluster edge describes a relationship between the two graph node clusters, and wherein the relationship between the two graph node clusters comprises a description of an upstream connection from one of the graph node clusters to an upstream node cluster, and wherein the relationship between the two graph node clusters comprises a description of a downstream connection from one of the graph node clusters to a downstream node cluster; transmitting only information in the cluster edge to a party that is not allowed to view contents of the graph nodes in the non-hierarchical graph database, wherein transmitting only the information in the cluster edge enhances security of the non-hierarchical graph database; communicating the information from the cluster edge without the content of the graph nodes to a cache at a remote location, wherein communicating the information from the cluster edge without the content of the graph nodes reduces communication bandwidth consumption for a network that is coupled to the cache, and wherein communicating the information from the cluster edge without the content of the graph nodes reduces an amount of storage consumed in the cache; and executing a computation using only information stored in the cache that came from the cluster edge without the content of the graph nodes, wherein execution of the computation is improved by using only information from the cluster edge without the content of the graph nodes.
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7. A computer program product for improving a computer system, the computer program product comprising a tangible non-transitory computer readable storage medium having program code embodied therewith, the program code readable and executable by a processor to perform a method comprising: receiving a data stream that describes graph nodes in a non-hierarchical graph database; defining multiple graph node clusters from the graph nodes in the non-hierarchical graph database; generating a cluster edge between two graph node clusters from the multiple graph node clusters in the non-hierarchical graph database, wherein the cluster edge describes a relationship between the two graph node clusters, and wherein the relationship between the two graph node clusters comprises a description of an upstream connection from one of the graph node clusters to an upstream node cluster, and wherein the relationship between the two graph node clusters comprises a description of a downstream connection from one of the graph node clusters to a downstream node cluster; transmitting only information in the cluster edge to a party that is not allowed to view contents of the graph nodes in the non-hierarchical graph database, wherein transmitting only the information in the cluster edge enhances security of the non-hierarchical graph database; communicating the information from the cluster edge without the content of the graph nodes to a cache at a remote location, wherein communicating the information from the cluster edge without the content of the graph nodes reduces communication bandwidth consumption for a network that is coupled to the cache, and wherein communicating the information from the cluster edge without the content of the graph nodes reduces an amount of storage consumed in the cache; and executing a computation using only information stored in the cache that came from the cluster edge without the content of the graph nodes, wherein execution of the computation is improved by using only information from the cluster edge without the content of the graph nodes. 8. The computer program product of claim 7 , wherein program code further comprises instructions that are readable and executable by the processor for: generating a display of the non-hierarchical graph database, wherein the non-hierarchical graph database comprises the two graph node clusters and the cluster edge.
| 0.728055 |
10,092,844 | 13 | 15 |
13. The system of claim 11 , wherein the vision recognition reference is an augmented version of the user selected content based on image recognition patterns.
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13. The system of claim 11 , wherein the vision recognition reference is an augmented version of the user selected content based on image recognition patterns. 15. The system of claim 13 , wherein generating the vision recognition reference includes adding a digital watermark to the user selected content.
| 0.695833 |
8,024,334 | 1 | 4 |
1. A method, relating to creating and maintaining, in at least one database available to a population of users, on a database server computer, information about a plurality of database of subjects, comprising the steps of: a) associating with each database subject of such plurality of database subjects at least one plurality of natural-language tags potentially descriptive of such each database subject according to an involved subset of such population of database users; said at least one plurality of natural language tags comprising other user chosen and/or other user provided natural language terms potentially descriptive of a subject or subjects of said database; b) assessing at least one measure of descriptive relevance of each of such at least one plurality of natural-language tags to such each database subject according to each particular database user of such involved subset of such population of database users; c) associatively indexing, in such at least one database, such respective particular database users, such respective natural-language tags, such respective measures of relevance, and such respective database subjects; and d) accumulating and storing such respective measures of relevance.
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1. A method, relating to creating and maintaining, in at least one database available to a population of users, on a database server computer, information about a plurality of database of subjects, comprising the steps of: a) associating with each database subject of such plurality of database subjects at least one plurality of natural-language tags potentially descriptive of such each database subject according to an involved subset of such population of database users; said at least one plurality of natural language tags comprising other user chosen and/or other user provided natural language terms potentially descriptive of a subject or subjects of said database; b) assessing at least one measure of descriptive relevance of each of such at least one plurality of natural-language tags to such each database subject according to each particular database user of such involved subset of such population of database users; c) associatively indexing, in such at least one database, such respective particular database users, such respective natural-language tags, such respective measures of relevance, and such respective database subjects; and d) accumulating and storing such respective measures of relevance. 4. The method, according to claim 1 , further comprising the steps of: a) accumulating, storing, and analyzing all associations, including subject categorizations, of such overall measures of relevance of such plurality of natural-language tags associated with such database subjects; and b) determining preferred such natural-language tags, according to such population of users, relating to selected categories of subjects.
| 0.5 |
9,734,839 | 1 | 4 |
1. A voice controlled system comprising: one or more processors; computer-readable media accessible by the one or more processors; a first application and a second application stored on the computer-readable media to be executed by the one or more processors; a microphone to receive audio input; a speech recognition module to identify first data from a signal representing the audio input, the first data including text representing one or more words; and a command router to determine, using second data that is different from the first data, a first application probability of the first application being a recipient of a next command, wherein the second data is available to the command router prior to identification of the first data, determine, using the second data, a second application probability of the second application being a recipient of the next command, provide, to the first application, the text, receive, from the first application, a first matching probability indicating a degree of matching between the one or more words and a command which the first application can interpret, provide, to the second application, the text, receive, from the second application, a second matching probability indicating a degree of matching between the one or more words and a command which the second application can interpret, and select, based at least in part on the first application probability, the second application probability, the first matching probability, and the second matching probability, the first application to receive the command in the one or more words and to perform at least one operation associated with the next command.
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1. A voice controlled system comprising: one or more processors; computer-readable media accessible by the one or more processors; a first application and a second application stored on the computer-readable media to be executed by the one or more processors; a microphone to receive audio input; a speech recognition module to identify first data from a signal representing the audio input, the first data including text representing one or more words; and a command router to determine, using second data that is different from the first data, a first application probability of the first application being a recipient of a next command, wherein the second data is available to the command router prior to identification of the first data, determine, using the second data, a second application probability of the second application being a recipient of the next command, provide, to the first application, the text, receive, from the first application, a first matching probability indicating a degree of matching between the one or more words and a command which the first application can interpret, provide, to the second application, the text, receive, from the second application, a second matching probability indicating a degree of matching between the one or more words and a command which the second application can interpret, and select, based at least in part on the first application probability, the second application probability, the first matching probability, and the second matching probability, the first application to receive the command in the one or more words and to perform at least one operation associated with the next command. 4. The voice controlled system of claim 1 , wherein the first application probability is a Bayesian prior.
| 0.851541 |
8,098,409 | 1 | 9 |
1. An image distribution system via e-mail comprising: a first user terminal; a server serving to receive a message consisting of an ideogram string input from said first user terminal and to transmit the received message together with an image attached thereto to said first user terminal; and an internetwork via which said first user terminal and said server are connected to each other, wherein said server comprises: storage means adapted to store an ideogram string element or elements including characters, symbols, graphics or combination thereof respectively corresponding to an expression, attitude or posture representing an emotion or situation put into a message inputted by a user of said first user terminal, said storage means being adapted to store images corresponding to said ideogram string element or elements, recognizing means adapted to recognize the ideogram string element or elements from the message inputted by the user of said first user terminal, and image distribution means adapted to pick up the image corresponding to the ideogram string element or elements having been recognized by said recognizing means and to distribute the corresponding image to said first user terminal via the internetwork.
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1. An image distribution system via e-mail comprising: a first user terminal; a server serving to receive a message consisting of an ideogram string input from said first user terminal and to transmit the received message together with an image attached thereto to said first user terminal; and an internetwork via which said first user terminal and said server are connected to each other, wherein said server comprises: storage means adapted to store an ideogram string element or elements including characters, symbols, graphics or combination thereof respectively corresponding to an expression, attitude or posture representing an emotion or situation put into a message inputted by a user of said first user terminal, said storage means being adapted to store images corresponding to said ideogram string element or elements, recognizing means adapted to recognize the ideogram string element or elements from the message inputted by the user of said first user terminal, and image distribution means adapted to pick up the image corresponding to the ideogram string element or elements having been recognized by said recognizing means and to distribute the corresponding image to said first user terminal via the internetwork. 9. The image distribution system via e-mail according to claim 1 , wherein the image comprises a robot.
| 0.913591 |
8,694,896 | 1 | 13 |
1. A computerized method for creating a story by a plurality of collaborators each capable of supplying story content associated with a story concept over a network, comprising: making the story concept available for access over the network to enable the plurality of collaborators to submit competing story content related to at least one element in the story concept; accepting the competing story content associated with the story concept from the plurality of collaborators if the received competing story content meets a first predetermined criteria; determining a selected story content from the competing story content submitted from each of the plurality of collaborators, the selected story content being approved for inclusion in the story based on communications over the network; and creating the story from the selected story content that is approved.
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1. A computerized method for creating a story by a plurality of collaborators each capable of supplying story content associated with a story concept over a network, comprising: making the story concept available for access over the network to enable the plurality of collaborators to submit competing story content related to at least one element in the story concept; accepting the competing story content associated with the story concept from the plurality of collaborators if the received competing story content meets a first predetermined criteria; determining a selected story content from the competing story content submitted from each of the plurality of collaborators, the selected story content being approved for inclusion in the story based on communications over the network; and creating the story from the selected story content that is approved. 13. The computerized method of claim 1 further comprising: determining a reward for a collaborator of the plurality of collaborators submitting the selected story content.
| 0.691336 |
10,127,314 | 11 | 14 |
11. A non-transitory computer readable medium storing instructions, which when executed by one or more processors of a data processing system causes the data processing system to perform a method of optimizing search engine performance comprising: receiving from a user a seed string including one or more letters; generating at least one search query based on the seed string, the search query including at least one of an example, suggestion, or a term; sending, to the search engine, the at least one search query for execution of a search associated with each of the at least one search query; receiving, from the search engine, a set of search results associated with each of the at least one search query; retrieving, from a data source different than a data source used by the search, a previously stored set of expected results associated with the search query, wherein the previously stored set of expected results is distinct from the set of search results generated in response to receiving the seed string, and wherein the expected results have a predetermined degree of relevance to the search query; determining a search query evaluation value for each of the at least one search query based at least in part on comparing the set of search results associated with each of the at least one search query with the previously stored set of expected results, wherein the search query evaluation value includes one or more of a precision value or a recall value; determining a mean relevancy value for each of the at least one search query, wherein the mean relevancy value is based on a plurality of relevancy values calculated based on different weighting of the search query evaluation value associated with the at least one search query; and configuring a search criteria of the search engine based on the mean relevancy value, the configured search criteria for use in a subsequent search after the set of search results have been generated.
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11. A non-transitory computer readable medium storing instructions, which when executed by one or more processors of a data processing system causes the data processing system to perform a method of optimizing search engine performance comprising: receiving from a user a seed string including one or more letters; generating at least one search query based on the seed string, the search query including at least one of an example, suggestion, or a term; sending, to the search engine, the at least one search query for execution of a search associated with each of the at least one search query; receiving, from the search engine, a set of search results associated with each of the at least one search query; retrieving, from a data source different than a data source used by the search, a previously stored set of expected results associated with the search query, wherein the previously stored set of expected results is distinct from the set of search results generated in response to receiving the seed string, and wherein the expected results have a predetermined degree of relevance to the search query; determining a search query evaluation value for each of the at least one search query based at least in part on comparing the set of search results associated with each of the at least one search query with the previously stored set of expected results, wherein the search query evaluation value includes one or more of a precision value or a recall value; determining a mean relevancy value for each of the at least one search query, wherein the mean relevancy value is based on a plurality of relevancy values calculated based on different weighting of the search query evaluation value associated with the at least one search query; and configuring a search criteria of the search engine based on the mean relevancy value, the configured search criteria for use in a subsequent search after the set of search results have been generated. 14. The medium of claim 11 , wherein the search query evaluation value includes the precision value determined based on a sum of query precisions associated with a set of queries related to the seed string.
| 0.5 |
7,752,266 | 1 | 8 |
1. A method to facilitate translation of communications between hardware entities over a network, said method comprising: communicating a plurality of predetermined language constructs in a first language to a first entity as a first transmission over said network, the plurality of predetermined language constructs in the first language displayed to the first entity in a first set of one or more interactive fields, each predetermined language construct of the plurality of language constructs in the first language being associated with a predetermined numerical identifier; receiving, from said first entity, an identifier of a second entity; receiving, from said first entity, a first numerical identifier of a first language construct selected by the first entity from said plurality of predetermined language constructs in the first language, the first numerical identifier comprising a numerical indicator of the first language construct and not including the text of the first language construct; responsive to receipt of said first numerical identifier, determining a translated language construct corresponding to said first numerical identifier, said determining further comprises: retrieving entity information relating to said second entity based on the identifier of said second entity; and retrieving said translated language construct from a table based on said entity information and said first numerical identifier of the first language construct; communicating said translated language construct to said second entity as a second transmission over said network; and communicating the plurality of predetermined language constructs in a second language to the second entity in a second set of one or more interactive fields, the second entity to respond to the first entity by selecting a second language construct from the plurality of predetermined language constructs in the second language, each predetermined language construct of the plurality of language constructs in the second language being associated with the predetermined numerical identifier.
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1. A method to facilitate translation of communications between hardware entities over a network, said method comprising: communicating a plurality of predetermined language constructs in a first language to a first entity as a first transmission over said network, the plurality of predetermined language constructs in the first language displayed to the first entity in a first set of one or more interactive fields, each predetermined language construct of the plurality of language constructs in the first language being associated with a predetermined numerical identifier; receiving, from said first entity, an identifier of a second entity; receiving, from said first entity, a first numerical identifier of a first language construct selected by the first entity from said plurality of predetermined language constructs in the first language, the first numerical identifier comprising a numerical indicator of the first language construct and not including the text of the first language construct; responsive to receipt of said first numerical identifier, determining a translated language construct corresponding to said first numerical identifier, said determining further comprises: retrieving entity information relating to said second entity based on the identifier of said second entity; and retrieving said translated language construct from a table based on said entity information and said first numerical identifier of the first language construct; communicating said translated language construct to said second entity as a second transmission over said network; and communicating the plurality of predetermined language constructs in a second language to the second entity in a second set of one or more interactive fields, the second entity to respond to the first entity by selecting a second language construct from the plurality of predetermined language constructs in the second language, each predetermined language construct of the plurality of language constructs in the second language being associated with the predetermined numerical identifier. 8. The method according to claim 1 , wherein said first transmission is a Hyper Text Markup Language (HTTP) message.
| 0.882828 |
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