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9,260,122 | 7 | 10 | 7. An article of manufacture, comprising: a computer readable storage medium having computer readable program code embodied therewith, wherein the computer readable storage medium is not a transitory signal per se, the computer readable program code comprising instructions for execution by a computer processing unit that cause the computer processing unit to: acquire video image data over time from a plurality of synchronized cameras having overlapping views of a plurality of objects moving past the cameras and through a scene image in a linear array and with a determined speed; generate for each camera a plurality of object detection states that each have different times of frames of the acquired video image data within a plurality of frames of the camera video stream data, wherein each of the object detection states are associated with a confidence score; select ones of the plurality of object detection states for each of the different times that have a highest confidence score optimized by using a global energy function to find maximum unary potentials (ψ(s k t )) of the object detection states as a function of a cross-frame constraint that is defined by other confidence scores of other object detection states from the video data that are acquired by a same one of the cameras at different times from a time of the object detection state, and of a cross-view constraint (T(s k t , s l t )) that is defined by other confidence scores of other object detection states in the video data from another different one of the cameras that has an overlapping field-of-view with the same one camera and that are also acquired at the different times; define an optimal state path for a detection of an object from an initial time to a final time of a duration period comprising the selected ones of the plurality of object detection states that have the highest optimized confidence scores; and determine the unary potentials ψ(s k t ) according to:
ψ( s k t )= f ( s k t )Π t≠k T ( s k t ,s l t ); where f(s k t ) is a confidence score of an object state {s k t } returned by an object detector at view {k}; and determine the cross-view spatial constraint as a function of the unary potential according to: T ( s k t , s l t ) = max ( N ( s k t - s l t ; θ kl ) , N ( s k t - s l t + ∈ ; θ kl ) ) ; wherein θ kl =[μ v (k, l), Σ v (k,l)] for views {k} and {l}; “μ v ” is a four-by-four matrix of mean values; Σ v ” is a four-by-four covariance matrix; and “ε” is a cross-object constraint that represents an object spacing constant defined by a sequential context of the linear array of the objects determined as a function of spatial attributes of the objects relative to the determined speed of the movement of the cameras relative to the objects. | 7. An article of manufacture, comprising: a computer readable storage medium having computer readable program code embodied therewith, wherein the computer readable storage medium is not a transitory signal per se, the computer readable program code comprising instructions for execution by a computer processing unit that cause the computer processing unit to: acquire video image data over time from a plurality of synchronized cameras having overlapping views of a plurality of objects moving past the cameras and through a scene image in a linear array and with a determined speed; generate for each camera a plurality of object detection states that each have different times of frames of the acquired video image data within a plurality of frames of the camera video stream data, wherein each of the object detection states are associated with a confidence score; select ones of the plurality of object detection states for each of the different times that have a highest confidence score optimized by using a global energy function to find maximum unary potentials (ψ(s k t )) of the object detection states as a function of a cross-frame constraint that is defined by other confidence scores of other object detection states from the video data that are acquired by a same one of the cameras at different times from a time of the object detection state, and of a cross-view constraint (T(s k t , s l t )) that is defined by other confidence scores of other object detection states in the video data from another different one of the cameras that has an overlapping field-of-view with the same one camera and that are also acquired at the different times; define an optimal state path for a detection of an object from an initial time to a final time of a duration period comprising the selected ones of the plurality of object detection states that have the highest optimized confidence scores; and determine the unary potentials ψ(s k t ) according to:
ψ( s k t )= f ( s k t )Π t≠k T ( s k t ,s l t ); where f(s k t ) is a confidence score of an object state {s k t } returned by an object detector at view {k}; and determine the cross-view spatial constraint as a function of the unary potential according to: T ( s k t , s l t ) = max ( N ( s k t - s l t ; θ kl ) , N ( s k t - s l t + ∈ ; θ kl ) ) ; wherein θ kl =[μ v (k, l), Σ v (k,l)] for views {k} and {l}; “μ v ” is a four-by-four matrix of mean values; Σ v ” is a four-by-four covariance matrix; and “ε” is a cross-object constraint that represents an object spacing constant defined by a sequential context of the linear array of the objects determined as a function of spatial attributes of the objects relative to the determined speed of the movement of the cameras relative to the objects. 10. The article of manufacture of claim 7 , wherein the computer readable program code instructions, for execution by the computer processing unit, further cause the computer processing unit to: determine confidence scores for every one of the object detection states according to real-time dynamic programming formulations: χ k 1 = ψ ( s k 1 ) ; and χ k t = ψ ( s k t ) max j ( χ k t - 1 ϕ ( s k t , s j t - 1 ) ) ; at each time point, select an optimal object state (s v t ) according to formulation: v = arg max k ( χ k t ) ; infer suboptimal object states in other camera views at each time point (t); and if no object detection is found at a time point (t), restart the steps of determining the confidence scores for the object detection states via the real-time dynamic programming formulations and select an optimal object state (s v t ) at a next time point (t+1). | 0.535491 |
9,703,673 | 1 | 3 | 1. A system for setting a stack pattern breakpoint for a COBOL program, the system comprising: a processor in communication with one or more types of memory, the processor configured to: provide a static program control flow view of a plurality of COBOL paragraphs of the COBOL program, enable a user to select the stack pattern using the static program control flow view of the plurality of COBOL paragraphs of the COBOL program, and set the stack pattern breakpoint in source code of the COBOL program using information from a compiler compiling the COBOL program to create a pseudo-stack that can be operated on by a debugger to evaluate stack pattern conditions for the plurality of COBOL paragraphs, wherein setting the stack pattern breakpoint further comprises finding a perform save cells section using the information from the compiler to find a plurality of save cells, scanning the plurality of save cells and associating each of the plurality of save cells with at least one of the plurality of paragraphs, and, responsive to a perform being executed, modifying at least one of the save cells to point back to the perform save cells. | 1. A system for setting a stack pattern breakpoint for a COBOL program, the system comprising: a processor in communication with one or more types of memory, the processor configured to: provide a static program control flow view of a plurality of COBOL paragraphs of the COBOL program, enable a user to select the stack pattern using the static program control flow view of the plurality of COBOL paragraphs of the COBOL program, and set the stack pattern breakpoint in source code of the COBOL program using information from a compiler compiling the COBOL program to create a pseudo-stack that can be operated on by a debugger to evaluate stack pattern conditions for the plurality of COBOL paragraphs, wherein setting the stack pattern breakpoint further comprises finding a perform save cells section using the information from the compiler to find a plurality of save cells, scanning the plurality of save cells and associating each of the plurality of save cells with at least one of the plurality of paragraphs, and, responsive to a perform being executed, modifying at least one of the save cells to point back to the perform save cells. 3. The system of claim 1 , wherein enabling the user to set the stack pattern includes enabling the user to select at least one of the plurality of paragraphs of the COBOL program using the static program control flow view. | 0.654799 |
9,264,545 | 2 | 3 | 2. A system according to claim 1 , further comprising: a speech recognition engine to convert the verbal speech utterances into text; a display to present the text to the agent; a text-to-speech agent to receive outgoing text messages from the agent and to convert the text messages into an outgoing stream of synthesized speech; and the telephony interface to provide the outgoing stream of the synthesized speech to the callee. | 2. A system according to claim 1 , further comprising: a speech recognition engine to convert the verbal speech utterances into text; a display to present the text to the agent; a text-to-speech agent to receive outgoing text messages from the agent and to convert the text messages into an outgoing stream of synthesized speech; and the telephony interface to provide the outgoing stream of the synthesized speech to the callee. 3. A system according to claim 2 , wherein the text messages comprise one or more of pre-formed responses selected by the live agent or responses written by the live agent. | 0.5 |
9,690,779 | 3 | 4 | 3. A pseudo natural language based dialog device ( 5 ) of claim 1 , wherein at least one of said semantic-logic-representation devices ( 1 ) works jointly with a registry or index system, whereby said registry has one or more semantics-to-language converting devices ( 2 ) registered for each one or set of said semantic-logic-representation devices ( 1 ); in accordance to services provided by the said registry, said conversion functions ( 11 ) is capable of being locating and delegating its implementation to said one or more semantics-to-language converting devices. | 3. A pseudo natural language based dialog device ( 5 ) of claim 1 , wherein at least one of said semantic-logic-representation devices ( 1 ) works jointly with a registry or index system, whereby said registry has one or more semantics-to-language converting devices ( 2 ) registered for each one or set of said semantic-logic-representation devices ( 1 ); in accordance to services provided by the said registry, said conversion functions ( 11 ) is capable of being locating and delegating its implementation to said one or more semantics-to-language converting devices. 4. A pseudo natural language based dialog device ( 5 ) of claim 3 , wherein each of said semantics-to-language converting devices ( 2 ) includes another conversion function ( 21 ), wherein said another conversion function ( 21 ) converts said one or more semantic-logic-representation devices ( 1 ), according to an input of a language identity parameter ( 6 ), into said one or more language-presentation devices ( 4 ). | 0.5 |
8,392,193 | 25 | 26 | 25. The method of claim 23 , further comprising: performing a mathematical operation on the second speech recognition result; and comparing a result of the mathematical operation to a portion of the second speech recognition result. | 25. The method of claim 23 , further comprising: performing a mathematical operation on the second speech recognition result; and comparing a result of the mathematical operation to a portion of the second speech recognition result. 26. The method of claim 25 , further comprising: determining that the second speech recognition result is incorrect when the result of the mathematical operation does not match the portion of the second speech recognition result. | 0.5 |
8,610,723 | 16 | 17 | 16. The processor-implemented method of claim 15 , wherein: the deciding to enforce collision constraints comprises determining that the at least two adjacent finger segments of the model are in a specified relative position. | 16. The processor-implemented method of claim 15 , wherein: the deciding to enforce collision constraints comprises determining that the at least two adjacent finger segments of the model are in a specified relative position. 17. The processor-implemented method of claim 16 , wherein: the at least two adjacent finger segments of the model are in the specified relative position when respective longitudinal axes of the at least two adjacent finger segments of the model are substantially parallel, within a specified angular threshold. | 0.5 |
8,473,088 | 9 | 10 | 9. The method of claim 8 , wherein the shape comprises an upper and lower closed curve. | 9. The method of claim 8 , wherein the shape comprises an upper and lower closed curve. 10. The method of claim 9 , wherein placing the text and/or symbols comprises spacing the text and/or dividers within the upper and lower closed curves. | 0.5 |
9,098,862 | 7 | 10 | 7. A computer-implemented method for obtaining human input regarding an item of interest to a user, the method comprising: identifying, by a computing system, a first plurality of human respondents by matching user-defined query criteria with human respondent profile information, wherein the user-defined query criteria identifies one or more characteristics of individual human respondents; based on a determination that the number of human respondents identified in the first plurality of the human respondents does not satisfy a threshold, modifying, by the computing system, the user-defined criteria; identifying, by the computing system, a second plurality of the human respondents by matching the modified user-defined query criteria with human respondent profile information, wherein the number of human respondents identified in the second plurality of human respondents satisfies the threshold; for individual human respondents of the second plurality of human respondents, providing one or more questions regarding the item of interest to a computing device associated with the human respondent; and receiving at least one response to the one or more questions from the human respondent. | 7. A computer-implemented method for obtaining human input regarding an item of interest to a user, the method comprising: identifying, by a computing system, a first plurality of human respondents by matching user-defined query criteria with human respondent profile information, wherein the user-defined query criteria identifies one or more characteristics of individual human respondents; based on a determination that the number of human respondents identified in the first plurality of the human respondents does not satisfy a threshold, modifying, by the computing system, the user-defined criteria; identifying, by the computing system, a second plurality of the human respondents by matching the modified user-defined query criteria with human respondent profile information, wherein the number of human respondents identified in the second plurality of human respondents satisfies the threshold; for individual human respondents of the second plurality of human respondents, providing one or more questions regarding the item of interest to a computing device associated with the human respondent; and receiving at least one response to the one or more questions from the human respondent. 10. The method of claim 7 , wherein the threshold is a minimum number of human respondents identified in the query criteria. | 0.82967 |
7,904,397 | 1 | 16 | 1. A method of processing an inductive learning model for a database containing a dataset of examples, said method comprising: dividing said dataset of examples into a plurality of subsets of data; and generating, using a processor on a computer, a learning model using examples of a first subset of data of said plurality of subsets of data, wherein: said learning model being generated for said first subset comprises an initial stage of an evolving aggregate learning model (ensemble model) for an entirety of said dataset, said ensemble model thereby providing an evolving estimated learning model for the entirety of said dataset if all said subsets were to be processed, and said generating said learning model using data from a subset includes calculating a value for at least one parameter that provides an objective indication of an adequacy of a current stage of said ensemble model. | 1. A method of processing an inductive learning model for a database containing a dataset of examples, said method comprising: dividing said dataset of examples into a plurality of subsets of data; and generating, using a processor on a computer, a learning model using examples of a first subset of data of said plurality of subsets of data, wherein: said learning model being generated for said first subset comprises an initial stage of an evolving aggregate learning model (ensemble model) for an entirety of said dataset, said ensemble model thereby providing an evolving estimated learning model for the entirety of said dataset if all said subsets were to be processed, and said generating said learning model using data from a subset includes calculating a value for at least one parameter that provides an objective indication of an adequacy of a current stage of said ensemble model. 16. The method of claim 1 , as embodied in a set of computer-readable machine instructions stored in a tangible storage medium. | 0.905224 |
6,038,561 | 19 | 29 | 19. A method of analyzing information regarding a plurality of documents, each having a unique document index, the method comprising the steps of: parsing each document into a plurality of elements; generating a first representation of each of said elements; and for selected pairs of documents, comprised of a first document and a second document, determining a first utility measure based on the respective first representation of the plurality of elements for the documents in that pair. | 19. A method of analyzing information regarding a plurality of documents, each having a unique document index, the method comprising the steps of: parsing each document into a plurality of elements; generating a first representation of each of said elements; and for selected pairs of documents, comprised of a first document and a second document, determining a first utility measure based on the respective first representation of the plurality of elements for the documents in that pair. 29. The method of claim 19 further comprising the steps of: generating a second representation of each of said elements; for the selected pairs of documents, determining a second utility measure based on the respective second representation of the plurality of elements for the documents in that pair. | 0.735501 |
7,934,066 | 1 | 15 | 1. An archive method using a primary storage and a secondary storage comprising: receiving a backup request for a target dataset used by an application on a primary storage system to be backed up on a secondary storage system, wherein the primary storage system may have one or more different applications and each application having a corresponding one or more different proprietary application formats for storing their datasets; identifying an application translator module component to be loaded into an extensible backup manager that converts between a proprietary application format associated with the target dataset and a predetermined storage format used by the extensible backup manager; scheduling a baseline backup of entire target dataset from the primary storage to the secondary storage using the application translator module to convert from the proprietary application format into the predetermined storage format when the baseline backup of the target dataset has not yet been performed; performing an incremental backup of the target dataset in addition to the baseline backup of the entire target dataset, if the incremental backup is scheduled, wherein the incremental backup uses the application translator module to convert from the proprietary application format associated with the application into the predetermined storage format of the extensible backup manager; and invoking a data mover component from the extensible backup manager when the application translator module has completed converting from the proprietary application format into the predetermined storage format wherein the data mover component causes the incremental backup and the baseline backup of the entire target dataset, if scheduled, to be moved from the primary storage to the secondary storage as requested and stored in the predetermined storage format rather than the proprietary application format associated with the application. | 1. An archive method using a primary storage and a secondary storage comprising: receiving a backup request for a target dataset used by an application on a primary storage system to be backed up on a secondary storage system, wherein the primary storage system may have one or more different applications and each application having a corresponding one or more different proprietary application formats for storing their datasets; identifying an application translator module component to be loaded into an extensible backup manager that converts between a proprietary application format associated with the target dataset and a predetermined storage format used by the extensible backup manager; scheduling a baseline backup of entire target dataset from the primary storage to the secondary storage using the application translator module to convert from the proprietary application format into the predetermined storage format when the baseline backup of the target dataset has not yet been performed; performing an incremental backup of the target dataset in addition to the baseline backup of the entire target dataset, if the incremental backup is scheduled, wherein the incremental backup uses the application translator module to convert from the proprietary application format associated with the application into the predetermined storage format of the extensible backup manager; and invoking a data mover component from the extensible backup manager when the application translator module has completed converting from the proprietary application format into the predetermined storage format wherein the data mover component causes the incremental backup and the baseline backup of the entire target dataset, if scheduled, to be moved from the primary storage to the secondary storage as requested and stored in the predetermined storage format rather than the proprietary application format associated with the application. 15. The method of claim 1 further comprising: generating an additional application translator module for each restore request that operates to separately convert between a predetermined storage format and a proprietary application format; and identifying, by a single extensible backup manager, each additional application translator module in accordance with the target dataset and application and ensuring, by the single extensible backup manager, each application translator module completes conversion of the target dataset from the predetermined storage format into the proprietary application format. | 0.5 |
8,032,827 | 1 | 7 | 1. A method comprising the steps of: a) for each document in a plurality of documents, generating, by a computer system, text structure tags for the document, said text structure tags identifying a plurality of argumentative text types, wherein a text type comprises a type of argumentative content for an associated portion of a document, said types of argumentative content comprising an argument premise giving support, evidence, or reasoning for or against a conclusion or the conclusion comprising a resulting determination made using one or more argument premises; b) for each document in the plurality of documents, encoding, by the computer system, said document to generate a tree structure comprising a plurality of nodes, wherein said nodes correspond with said text types and hierarchical relationships among said nodes reflect argumentative relationships among said text types; c) selecting, by the computer system, a plurality of tree structures for the plurality of documents; d) combining, by the computer system, the plurality of tree structures as a single logical tree structure; and e) generating, by the computer system, a summary of the plurality of documents by: i) receiving from a user a selection of one or more particular text types for summarization; and ii) extracting, based on said text structure tags, portions of text from the plurality of documents that correspond to nodes from the plurality of tree structures to form a summary of the plurality of documents, the nodes corresponding to said one or more selected text types. | 1. A method comprising the steps of: a) for each document in a plurality of documents, generating, by a computer system, text structure tags for the document, said text structure tags identifying a plurality of argumentative text types, wherein a text type comprises a type of argumentative content for an associated portion of a document, said types of argumentative content comprising an argument premise giving support, evidence, or reasoning for or against a conclusion or the conclusion comprising a resulting determination made using one or more argument premises; b) for each document in the plurality of documents, encoding, by the computer system, said document to generate a tree structure comprising a plurality of nodes, wherein said nodes correspond with said text types and hierarchical relationships among said nodes reflect argumentative relationships among said text types; c) selecting, by the computer system, a plurality of tree structures for the plurality of documents; d) combining, by the computer system, the plurality of tree structures as a single logical tree structure; and e) generating, by the computer system, a summary of the plurality of documents by: i) receiving from a user a selection of one or more particular text types for summarization; and ii) extracting, based on said text structure tags, portions of text from the plurality of documents that correspond to nodes from the plurality of tree structures to form a summary of the plurality of documents, the nodes corresponding to said one or more selected text types. 7. The method as set forth in claim 1 , further comprising, for each document in the plurality of documents: providing a visual hierarchy of the tree structure; providing a user interface for receiving user selection of one or more particular nodes of the tree; and extracting portions of text from said document that correspond to said one or more selected nodes. | 0.598234 |
9,483,532 | 10 | 14 | 10. A method implemented on a computer comprising a processor, the method comprising: receiving a text content containing text; tokenizing the text content into a plurality of terms each comprising an element selected from the group of elements consisting at least of a word, a phrase, a sentence, a paragraph; identifying a first term in the text content; identifying, in at least a portion of the text content, a second term, wherein the portion of the text content contains the first term or is grammatically or semantically associated with the first term; identifying a first grammatical attribute associated with the second term, or identifying a first semantic attribute associated with the second term; selecting the second term as a term related to the first term based at least on the first grammatical attribute or the first semantic attribute; marking the first term for use as a first-level entity in a hierarchical format, and marking the second term for use as a second-level entity in the hierarchical format, wherein the second-level entity is marked as an element under or subordinate to the first-level entity; and outputting the first term and the second term to be used for at least providing a relational or hierarchical representation of the informational elements in the text content; when the first term is used to represent a first-level category node, and the second term is used to represent a second-level category node or the content of the first-level category, an embodiment format of at least one of the category nodes includes a text element, a folder or a directory, or a link name associated with the linked contents on a device selected from the group of devices consisting at least of a computer file system, an email system, a web-based or cloud-based system, a mobile or handheld computing or communication device; when the first term and the second term are displayed, a display format comprises at least representing the first term as a topic or information focus in the text content, and the second term as a comment or attribute associated with the topic or the information focus; or representing the first term as a folder or directory in an electronic content management system, and the second term as a sub-folder or sub-directory in the electronic content management system; when the text content or the first term is made searchable using a query or is associated with a search index to produce a search result, a display format of the search result comprises the first term with one or more of its corresponding second terms if the first term matches a keyword in the search query. | 10. A method implemented on a computer comprising a processor, the method comprising: receiving a text content containing text; tokenizing the text content into a plurality of terms each comprising an element selected from the group of elements consisting at least of a word, a phrase, a sentence, a paragraph; identifying a first term in the text content; identifying, in at least a portion of the text content, a second term, wherein the portion of the text content contains the first term or is grammatically or semantically associated with the first term; identifying a first grammatical attribute associated with the second term, or identifying a first semantic attribute associated with the second term; selecting the second term as a term related to the first term based at least on the first grammatical attribute or the first semantic attribute; marking the first term for use as a first-level entity in a hierarchical format, and marking the second term for use as a second-level entity in the hierarchical format, wherein the second-level entity is marked as an element under or subordinate to the first-level entity; and outputting the first term and the second term to be used for at least providing a relational or hierarchical representation of the informational elements in the text content; when the first term is used to represent a first-level category node, and the second term is used to represent a second-level category node or the content of the first-level category, an embodiment format of at least one of the category nodes includes a text element, a folder or a directory, or a link name associated with the linked contents on a device selected from the group of devices consisting at least of a computer file system, an email system, a web-based or cloud-based system, a mobile or handheld computing or communication device; when the first term and the second term are displayed, a display format comprises at least representing the first term as a topic or information focus in the text content, and the second term as a comment or attribute associated with the topic or the information focus; or representing the first term as a folder or directory in an electronic content management system, and the second term as a sub-folder or sub-directory in the electronic content management system; when the text content or the first term is made searchable using a query or is associated with a search index to produce a search result, a display format of the search result comprises the first term with one or more of its corresponding second terms if the first term matches a keyword in the search query. 14. The method of claim 10 , wherein the first term is obtained from an input source. | 0.952408 |
7,797,316 | 7 | 8 | 7. A system comprising: one or more server devices comprising: a search engine to: perform a search of a corpus of documents to return a plurality of documents, where the plurality of documents includes a first document, identify a set of second documents that each contains, or previously contained, a link to the first document, determine a freshness attribute associated with each document of the set of second documents, where the freshness attribute indicates when each document was last modified, assign a freshness score to the first document based on the freshness attribute associated with each document of the set of second documents, and rank the first document among the plurality of documents based, at least in part, on the assigned freshness score. | 7. A system comprising: one or more server devices comprising: a search engine to: perform a search of a corpus of documents to return a plurality of documents, where the plurality of documents includes a first document, identify a set of second documents that each contains, or previously contained, a link to the first document, determine a freshness attribute associated with each document of the set of second documents, where the freshness attribute indicates when each document was last modified, assign a freshness score to the first document based on the freshness attribute associated with each document of the set of second documents, and rank the first document among the plurality of documents based, at least in part, on the assigned freshness score. 8. The system of claim 7 , where the search engine is further to: identify times at which each of the links to the first document existed; where assigning the freshness score to the first document is further based on the identified times. | 0.782847 |
8,774,519 | 3 | 4 | 3. The non-transitory program storage device of claim 2 , wherein the instructions to cause the processor to generate a candidate vector for each of a second plurality of pixels, comprise instructions to cause the processor to: select one of the plurality of evaluation regions; and generate a candidate vector for each of a second plurality of pixels from the selected evaluation region. | 3. The non-transitory program storage device of claim 2 , wherein the instructions to cause the processor to generate a candidate vector for each of a second plurality of pixels, comprise instructions to cause the processor to: select one of the plurality of evaluation regions; and generate a candidate vector for each of a second plurality of pixels from the selected evaluation region. 4. The non-transitory program storage device of claim 3 , wherein the instructions to cause the processor to select one of the plurality of evaluation regions, comprise instructions to cause the processor to select an evaluation region in which a landmark is expected. | 0.5 |
5,574,826 | 17 | 18 | 17. The electronic controller of claim 16, wherein a length L of a memory word of the memory section is dependent on the largest number of non-zero values. | 17. The electronic controller of claim 16, wherein a length L of a memory word of the memory section is dependent on the largest number of non-zero values. 18. The electronic controller of claim 17, wherein the length L of the memory word is L=nf(m)*(dim(G)+dim(f(m)), where nf(m) is the largest number of non-zero values, dim(G) is a word size required to represent a value indicative of a degree of membership, and dim(f(m)) is a word size required to represent the number of the membership functions. | 0.5 |
9,135,314 | 1 | 2 | 1. A method comprising: loading, in a computer system comprising at least one processor, a multidimensional data set having a plurality of data fields; retrieving, in the computer system, automatically without user intervention, an analysis digest specification associated with a particular user, wherein: the analysis digest specification includes a plurality of specifications for discrete analysis digests, each of the plurality of specifications specifies: one or more definitions for a particular analysis results, wherein the one or more definitions specify a pre-determined subset of data fields, in the plurality of data fields, customized by a first user, and a visual representation of the particular analysis results; parsing, in the computer system, the multidimensional data set according to the analysis digest specification to generate a first parsed multidimensional data set; generating, in the computer system, a first plurality of analysis digests in response to the first parsed multidimensional data set and the analysis digest specification; determining, in the computer system, that a third user is associated with the particular user; retrieving, in the computer system, automatically without user intervention, another analysis digest specification associated with the third user that is different than the analysis digest specification associated with the particular user; generating, in the computer system, an additional analysis digest in response to the first parsed multidimensional data set and the another analysis digest specification, wherein the additional analysis digest is different than the plurality of analysis digests; and assigning the first plurality of analysis digests and the additional analysis digest to a second user by causing the first plurality of analysis digests and the additional analysis digest to be automatically displayed to the second user, wherein the first user is associated with a first business role, and the second user is associated with a second business role different from the first business role. | 1. A method comprising: loading, in a computer system comprising at least one processor, a multidimensional data set having a plurality of data fields; retrieving, in the computer system, automatically without user intervention, an analysis digest specification associated with a particular user, wherein: the analysis digest specification includes a plurality of specifications for discrete analysis digests, each of the plurality of specifications specifies: one or more definitions for a particular analysis results, wherein the one or more definitions specify a pre-determined subset of data fields, in the plurality of data fields, customized by a first user, and a visual representation of the particular analysis results; parsing, in the computer system, the multidimensional data set according to the analysis digest specification to generate a first parsed multidimensional data set; generating, in the computer system, a first plurality of analysis digests in response to the first parsed multidimensional data set and the analysis digest specification; determining, in the computer system, that a third user is associated with the particular user; retrieving, in the computer system, automatically without user intervention, another analysis digest specification associated with the third user that is different than the analysis digest specification associated with the particular user; generating, in the computer system, an additional analysis digest in response to the first parsed multidimensional data set and the another analysis digest specification, wherein the additional analysis digest is different than the plurality of analysis digests; and assigning the first plurality of analysis digests and the additional analysis digest to a second user by causing the first plurality of analysis digests and the additional analysis digest to be automatically displayed to the second user, wherein the first user is associated with a first business role, and the second user is associated with a second business role different from the first business role. 2. The method of claim 1 wherein the one or more definitions for the visual representation comprise definitions for size and location for displaying the first plurality of analysis digests to the first user. | 0.776458 |
9,621,732 | 9 | 10 | 9. The method of claim 7 , wherein the calculating further includes normalizing the sum to a value between 0 and 1 using a refining function. | 9. The method of claim 7 , wherein the calculating further includes normalizing the sum to a value between 0 and 1 using a refining function. 10. The method of claim 9 , wherein the refining function includes: calculating a first value as the natural log of the negative of the sum; adding 1 to the first value to obtain a second value; and inverting the second value. | 0.5 |
10,084,745 | 7 | 15 | 7. A non-transitory, computer-readable medium comprising instructions for a subscriber device, the instructions executable by a processor to configure the subscriber device to: transmit a request to a publisher device within a fabric of smart devices, the publisher device including at least one sensor, the request being configured to cause the publisher device to establish a subscription to data related to a state or the at least one sensor of the publisher device; encode a data management action into a message for transmission to the publisher device, the encoded message including a profile identifier that causes a data management entity of the publisher device to perform the data management action, the profile identifier identifying a data management profile, and the encoded message including a command tag that encodes the data management action to be performed; transmit the encoded message to the publisher device, the transmission of the encoded message being effective to cause the publisher device to: send the data related to the state or the at least one sensor of the publisher device to the subscriber device upon an event; or send the data related to the state or the at least one sensor of the publisher device that is stored locally on the publisher device to the subscriber device; receive the data related to the state or the at least one sensor of the publisher device; and based upon the reception of the data, perform a control action based on the data related to the state or the at least one sensor of the publisher device. | 7. A non-transitory, computer-readable medium comprising instructions for a subscriber device, the instructions executable by a processor to configure the subscriber device to: transmit a request to a publisher device within a fabric of smart devices, the publisher device including at least one sensor, the request being configured to cause the publisher device to establish a subscription to data related to a state or the at least one sensor of the publisher device; encode a data management action into a message for transmission to the publisher device, the encoded message including a profile identifier that causes a data management entity of the publisher device to perform the data management action, the profile identifier identifying a data management profile, and the encoded message including a command tag that encodes the data management action to be performed; transmit the encoded message to the publisher device, the transmission of the encoded message being effective to cause the publisher device to: send the data related to the state or the at least one sensor of the publisher device to the subscriber device upon an event; or send the data related to the state or the at least one sensor of the publisher device that is stored locally on the publisher device to the subscriber device; receive the data related to the state or the at least one sensor of the publisher device; and based upon the reception of the data, perform a control action based on the data related to the state or the at least one sensor of the publisher device. 15. The non-transitory, computer-readable medium of claim 7 , wherein the transmitted message includes: a pathlist profile identifier that identifies a profile within which the data is located; a version for the data; and a pathlist that locates a location of the data within the profile. | 0.773228 |
9,761,032 | 12 | 16 | 12. A non-transitory computer-readable medium comprising instructions to cause a computing device, in response to execution of the instructions, to: receive a plurality of facial motion parameters and a plurality of head gestures parameters, respectively associated with a face and a head of a user; and drive an avatar model with facial and skeleton animations to animate the avatar, employing at least a head bone and a torso bone connected at a joint and using the facial motion parameters and the head gestures parameters to replicate a facial expression of the user on the avatar, that includes impact of head post rotation of the user; wherein the plurality of facial motion parameters depict facial action movements of the face, and the plurality of head gestures parameters depict head pose gestures of the head; wherein to drive comprises application of head rotation impact weights while driving the avatar model with facial and skeleton animations; and wherein application comprises application of head rotation impact weights from a head rotation impact weight map pre-generated employing the at least a head bone and a torso bone connected at a joint. | 12. A non-transitory computer-readable medium comprising instructions to cause a computing device, in response to execution of the instructions, to: receive a plurality of facial motion parameters and a plurality of head gestures parameters, respectively associated with a face and a head of a user; and drive an avatar model with facial and skeleton animations to animate the avatar, employing at least a head bone and a torso bone connected at a joint and using the facial motion parameters and the head gestures parameters to replicate a facial expression of the user on the avatar, that includes impact of head post rotation of the user; wherein the plurality of facial motion parameters depict facial action movements of the face, and the plurality of head gestures parameters depict head pose gestures of the head; wherein to drive comprises application of head rotation impact weights while driving the avatar model with facial and skeleton animations; and wherein application comprises application of head rotation impact weights from a head rotation impact weight map pre-generated employing the at least a head bone and a torso bone connected at a joint. 16. The computer-readable medium of claim 12 , wherein to drive comprises employment of a 2-dimensional texture map; wherein the head rotation impact weight map has layout or dimensions that correspond to the 2-dimensional texture map; wherein application comprises retrieval of an impact weight for a vertex from the head rotation impact weight map, using corresponding coordinates of the vertex in the 2-dimensional texture map. | 0.5 |
9,298,703 | 1 | 15 | 1. A system comprising: means for selecting, from a data store, a word or phrase associated with a failure to translate a message from a first language to a second language; means for selecting a person from whom to solicit user feedback for the translation failure; means for generating a query to request user feedback from the person; means for offering an incentive to the person; means for receiving the user feedback, the user feedback potentially assisting to translate the word or phrase; and means for rewarding the person with the incentive wherein the incentive is determined based on a complexity of the word or phrase or an importance of the word or phrase. | 1. A system comprising: means for selecting, from a data store, a word or phrase associated with a failure to translate a message from a first language to a second language; means for selecting a person from whom to solicit user feedback for the translation failure; means for generating a query to request user feedback from the person; means for offering an incentive to the person; means for receiving the user feedback, the user feedback potentially assisting to translate the word or phrase; and means for rewarding the person with the incentive wherein the incentive is determined based on a complexity of the word or phrase or an importance of the word or phrase. 15. The system of claim 1 , further comprising means for updating a transformation or translation of the word or phrase from the first language to the second language. | 0.647679 |
9,607,081 | 16 | 18 | 16. The system of claim 11 , further including: a database that stores the user's usage of the one or more marked GUI features; and a repository that stores the one or more generated user-specific ontologies. | 16. The system of claim 11 , further including: a database that stores the user's usage of the one or more marked GUI features; and a repository that stores the one or more generated user-specific ontologies. 18. The system of claim 16 , wherein the repository is an S-RAMP (Service Oriented Architecture Repository Artifact Model and Protocol) repository. | 0.5 |
7,773,822 | 1 | 7 | 1. A computer apparatus for creating a text searchable data structure containing electronic image documents, comprising: a first module that converts an electronic image document into a visually optimized electronic image document, wherein the first module includes a processor and a memory coupled to the processor; a second module that converts an electronic image document into a detail optimized electronic image document, which is different than the visually optimized image, wherein the first module includes a processor and a memory coupled to the processor; an OCR engine that abstracts character information from the detail optimized electronic image document and writes the character information into a text file; and, a linking module that links the visually optimized electronic image document with the text file in a data structure. | 1. A computer apparatus for creating a text searchable data structure containing electronic image documents, comprising: a first module that converts an electronic image document into a visually optimized electronic image document, wherein the first module includes a processor and a memory coupled to the processor; a second module that converts an electronic image document into a detail optimized electronic image document, which is different than the visually optimized image, wherein the first module includes a processor and a memory coupled to the processor; an OCR engine that abstracts character information from the detail optimized electronic image document and writes the character information into a text file; and, a linking module that links the visually optimized electronic image document with the text file in a data structure. 7. The apparatus of claim 1 , wherein the linking module is free from the detail optimized electronic image document. | 0.788809 |
7,640,497 | 11 | 15 | 11. The method of claim 1 , wherein: the input hierarchical data structure is represented by an input data structure tree having a root node and a plurality of children nodes, each node representing a key in the input hierarchical data structure; and for each node of the input hierarchical data structure tree, specifying an associated scope dictionary containing zero or more key value pairs in the temporary data structure. | 11. The method of claim 1 , wherein: the input hierarchical data structure is represented by an input data structure tree having a root node and a plurality of children nodes, each node representing a key in the input hierarchical data structure; and for each node of the input hierarchical data structure tree, specifying an associated scope dictionary containing zero or more key value pairs in the temporary data structure. 15. The method of claim 11 , wherein generating the output hierarchical data structure comprises traversing upward on the input data structure tree to create output data to be contained in the output hierarchical data structure. | 0.532787 |
9,350,913 | 3 | 4 | 3. The computing device of claim 1 , wherein the instructions, when executed, further cause the computing device to: receive a selection of a text input field being displayed on the interface; receive a selection of a word from of the selectable list; and populate the text input field with the word, wherein the text input field includes at least one of a title field, a product description field, a product search phrases field, a keyword search field, a price field, a quantity field, or a quality field. | 3. The computing device of claim 1 , wherein the instructions, when executed, further cause the computing device to: receive a selection of a text input field being displayed on the interface; receive a selection of a word from of the selectable list; and populate the text input field with the word, wherein the text input field includes at least one of a title field, a product description field, a product search phrases field, a keyword search field, a price field, a quantity field, or a quality field. 4. The computing device of claim 3 , wherein the instructions, when executed, further cause the computing device to: receive an indication that input to the text input field is complete; and generate a product listing using entries in the text input field. | 0.5 |
9,280,525 | 1 | 6 | 1. A method of forming a structured document from an unstructured input document, the method comprising: receiving the input document from a data communication network; storing the received input document in a storage system; in a first computer process, extracting a plurality of textual tokens from the input document, each extracted token having a visual style; in a second computer process, applying a content classifier to the plurality of tokens to produce, for each token therein, a first probability distribution of the given token with respect to a plurality of textual classes, the first probability distribution comprising a plurality of first probabilities each associated with a corresponding textual class of the plurality of textual classes such that each textual class has one of the plurality of first probabilities associated therewith, each of the plurality of textual classes being: related to information conveyed by the textual tokens; and specific to a type of unstructured data items of the input document; in a third computer process, applying a context classifier to each token to redistribute the first probability distribution of each token, based on the textual class having the highest first probability of the token's surrounding tokens in context, thereby producing a second probability distribution of the given token with respect to the plurality of textual classes, the second probability distribution comprising a plurality of second probabilities each associated with a corresponding textual class of the plurality of textual classes such that each textual class has one of the plurality of second probabilities associated therewith; in a fourth computer process, applying a visual style classifier to each token based on its visual style and the second probability distribution, and not based on a corpus containing any visual style that is not found in the input document, thereby producing a third probability distribution of the given token with respect to the plurality of textual classes, the third probability distribution comprising a plurality of third probabilities each associated with a corresponding textual class of the plurality of textual classes such that each textual class has one of the plurality of third probabilities associated therewith; determining a classification for each token into one of the plurality of textual classes as a function of the second and third probability distributions; and in the storage system, forming a structured document from the plurality of classified tokens. | 1. A method of forming a structured document from an unstructured input document, the method comprising: receiving the input document from a data communication network; storing the received input document in a storage system; in a first computer process, extracting a plurality of textual tokens from the input document, each extracted token having a visual style; in a second computer process, applying a content classifier to the plurality of tokens to produce, for each token therein, a first probability distribution of the given token with respect to a plurality of textual classes, the first probability distribution comprising a plurality of first probabilities each associated with a corresponding textual class of the plurality of textual classes such that each textual class has one of the plurality of first probabilities associated therewith, each of the plurality of textual classes being: related to information conveyed by the textual tokens; and specific to a type of unstructured data items of the input document; in a third computer process, applying a context classifier to each token to redistribute the first probability distribution of each token, based on the textual class having the highest first probability of the token's surrounding tokens in context, thereby producing a second probability distribution of the given token with respect to the plurality of textual classes, the second probability distribution comprising a plurality of second probabilities each associated with a corresponding textual class of the plurality of textual classes such that each textual class has one of the plurality of second probabilities associated therewith; in a fourth computer process, applying a visual style classifier to each token based on its visual style and the second probability distribution, and not based on a corpus containing any visual style that is not found in the input document, thereby producing a third probability distribution of the given token with respect to the plurality of textual classes, the third probability distribution comprising a plurality of third probabilities each associated with a corresponding textual class of the plurality of textual classes such that each textual class has one of the plurality of third probabilities associated therewith; determining a classification for each token into one of the plurality of textual classes as a function of the second and third probability distributions; and in the storage system, forming a structured document from the plurality of classified tokens. 6. A method according to claim 1 , further comprising: displaying the tokens on a video display; receiving an indication from an individual viewing the video display that a token has been misclassified; and reclassifying the token into a different textual class according to the indication. | 0.567164 |
9,390,243 | 17 | 18 | 17. A system providing dynamic trust scores for evaluating ongoing online relationships, the system comprising: a client of a first user; a first server including a first processor configured to execute an online service; a second server including a second processor configured to: receive a first request from the online service for a trust score assigned to an online relationship between the first user and a second user; calculate the trust score using a plurality of user data variables derived from a platform database referencing the first user and the second user, the user data variables including certified data and proffered data, wherein a weight given to each of the plurality of user data variables corresponds to a trust importance of each of the plurality of user data variables, and wherein the weight is adjusted over time for calculation of future trust scores based on a change in the trust importance of each of the plurality of user data variables; save the trust scores as a previous trust score in a memory; send the trust score to the online service in response to the first request, wherein the trust score affects the client of the first user; and perform dynamic recalculation of the trust score in response to a change to the plurality of user data variables over a period of time, wherein the change includes varying the weight given to each of the plurality of user data variables over the period of time, wherein the dynamic recalculation includes using the previous trust score. | 17. A system providing dynamic trust scores for evaluating ongoing online relationships, the system comprising: a client of a first user; a first server including a first processor configured to execute an online service; a second server including a second processor configured to: receive a first request from the online service for a trust score assigned to an online relationship between the first user and a second user; calculate the trust score using a plurality of user data variables derived from a platform database referencing the first user and the second user, the user data variables including certified data and proffered data, wherein a weight given to each of the plurality of user data variables corresponds to a trust importance of each of the plurality of user data variables, and wherein the weight is adjusted over time for calculation of future trust scores based on a change in the trust importance of each of the plurality of user data variables; save the trust scores as a previous trust score in a memory; send the trust score to the online service in response to the first request, wherein the trust score affects the client of the first user; and perform dynamic recalculation of the trust score in response to a change to the plurality of user data variables over a period of time, wherein the change includes varying the weight given to each of the plurality of user data variables over the period of time, wherein the dynamic recalculation includes using the previous trust score. 18. The system of claim 17 , wherein the second processor is further configured to: receive a second request from the online service for the trust score; recalculate the trust score in response to a change to the plurality of user data variables over a period of time; send the trust score to the online service in response to the second request. | 0.5 |
8,681,098 | 1 | 57 | 1. A system comprising: a data funnel coupled to a processor, wherein the data funnel collates input data from a plurality of sources, wherein the input data is semantically uncorrelated three-space data of an instantaneous spatial and geometric state of an object in a frame of reference of the object, wherein the plurality of sources comprise disparate sources, wherein the data funnel conforms the input data into a stream of spatiotemporal data, wherein the spatiotemporal data of the stream is uniformly represented; a gesture engine coupled to the data funnel, wherein the gesture engine generates gestural events from the spatiotemporal data using a plurality of gesture descriptions, wherein the gesture engine represents the gestural events in a protoevent comprising a data format that is application-neutral and fully articulated; and a distributor coupled to the gesture engine, wherein the distributor provides access to the gestural events by at least one event consumer via corresponding protoevents in a spatial-semantic frame of reference of the at least one event consumer. | 1. A system comprising: a data funnel coupled to a processor, wherein the data funnel collates input data from a plurality of sources, wherein the input data is semantically uncorrelated three-space data of an instantaneous spatial and geometric state of an object in a frame of reference of the object, wherein the plurality of sources comprise disparate sources, wherein the data funnel conforms the input data into a stream of spatiotemporal data, wherein the spatiotemporal data of the stream is uniformly represented; a gesture engine coupled to the data funnel, wherein the gesture engine generates gestural events from the spatiotemporal data using a plurality of gesture descriptions, wherein the gesture engine represents the gestural events in a protoevent comprising a data format that is application-neutral and fully articulated; and a distributor coupled to the gesture engine, wherein the distributor provides access to the gestural events by at least one event consumer via corresponding protoevents in a spatial-semantic frame of reference of the at least one event consumer. 57. The system of claim 1 , wherein the object is a human hand. | 0.925882 |
8,682,241 | 2 | 3 | 2. The analysis system as recited in claim 1 , wherein the report includes at least one of hyperlinks to specific classroom moments that influenced each specific score and hyperlinks to specific classroom examples from expert educators that provide examples of best practices. | 2. The analysis system as recited in claim 1 , wherein the report includes at least one of hyperlinks to specific classroom moments that influenced each specific score and hyperlinks to specific classroom examples from expert educators that provide examples of best practices. 3. The analysis system as recited in claim 2 , wherein the report includes comparative analyses and critiques between practices of an evaluated teacher participant and the examples from expert educators. | 0.5 |
9,564,057 | 13 | 14 | 13. The system of claim 10 , wherein the computing module is further configured to collect information from the subject person and the user interface module is further configured to provide a real-time communication indicating whether the one or more science inquiry skills of the subject person as assessed within the at least one of the simulation and the microworld match the information collected from the subject person, when the subject person performs at least one of: generating hypotheses; collecting data to test the hypotheses; interpreting the collected data; warranting claims with data; and communicating respective findings. | 13. The system of claim 10 , wherein the computing module is further configured to collect information from the subject person and the user interface module is further configured to provide a real-time communication indicating whether the one or more science inquiry skills of the subject person as assessed within the at least one of the simulation and the microworld match the information collected from the subject person, when the subject person performs at least one of: generating hypotheses; collecting data to test the hypotheses; interpreting the collected data; warranting claims with data; and communicating respective findings. 14. The system of claim 13 , wherein the user interface module is further configured to provide the real-time communication to another person, the real-time communication including at least one of: an alert associated with the one or more science inquiry skills of the subject person; and an assessment of the one or more science inquiry skills of the subject person. | 0.5 |
7,809,708 | 1 | 4 | 1. A method for searching a corpus of documents, comprising: defining a knowledge domain; identifying a set of reference documents in the corpus pertinent to the domain; inputting a first query; searching the corpus using the set of reference documents to find one or more of the documents in the corpus that contain information in the domain relevant to the first query; and adding at least one of the found documents to the set of reference documents for use in searching the corpus for information in the domain relevant to a second, subsequent query, which is substantially different from the first query, wherein searching the corpus comprises searching the corpus to find the documents that contain the information relevant to the query and ranking the found documents by comparing them to the set of reference documents, and wherein ranking the found documents comprises assessing links between the found documents and the reference documents. | 1. A method for searching a corpus of documents, comprising: defining a knowledge domain; identifying a set of reference documents in the corpus pertinent to the domain; inputting a first query; searching the corpus using the set of reference documents to find one or more of the documents in the corpus that contain information in the domain relevant to the first query; and adding at least one of the found documents to the set of reference documents for use in searching the corpus for information in the domain relevant to a second, subsequent query, which is substantially different from the first query, wherein searching the corpus comprises searching the corpus to find the documents that contain the information relevant to the query and ranking the found documents by comparing them to the set of reference documents, and wherein ranking the found documents comprises assessing links between the found documents and the reference documents. 4. The method according to claim 1 , wherein inputting the first query comprises specifying one or more documents representative of the information to be found in the corpus. | 0.6375 |
10,062,104 | 10 | 12 | 10. A client machine system comprising: at least one processor and executable instructions accessible on a computer-readable medium that, when executed, cause the at least one processor to perform operations comprising: sending a connection request, from a seller application, to a network-based transaction facility, the seller application being customizable and being customized by receiving configuration information from the network-based transaction facility, the configuration information including a hierarchal product category structure for generating a listing for a product for sale; receiving, by the seller application, a current version of the configuration information from the network-based transaction facility; sending, by the seller application, configuration confirmation to the network-based transaction facility, the configuration confirmation indicating the seller application is configured using the current version of the configuration information including the hierarchal product category structure; and sending a request for a transaction listing, the transaction listing being generated based on the current version of the configuration information. | 10. A client machine system comprising: at least one processor and executable instructions accessible on a computer-readable medium that, when executed, cause the at least one processor to perform operations comprising: sending a connection request, from a seller application, to a network-based transaction facility, the seller application being customizable and being customized by receiving configuration information from the network-based transaction facility, the configuration information including a hierarchal product category structure for generating a listing for a product for sale; receiving, by the seller application, a current version of the configuration information from the network-based transaction facility; sending, by the seller application, configuration confirmation to the network-based transaction facility, the configuration confirmation indicating the seller application is configured using the current version of the configuration information including the hierarchal product category structure; and sending a request for a transaction listing, the transaction listing being generated based on the current version of the configuration information. 12. The system of claim 10 , wherein: the receiving the configuration information includes receiving the hierarchal product category structure, the configuration information being configured to enable a user of the seller application to categorize user-composed information to be communicated from the seller application to the network-based transaction facility. | 0.5 |
8,799,210 | 1 | 12 | 1. A framework suitable for use in transition of one or more applications of an organization, the one or more applications being transitioned from a first set of users to a second set of users, the framework comprising: a. a transition module configured for generating one or more transition plans for the one or more applications, the one or more transition plans being generated based on information corresponding to the one or more applications, the one or more transition plans comprising one or more transition activities; b. a knowledge capture module configured for: i. capturing a plurality of knowledge elements corresponding to the one or more applications and the one or more transition activities, wherein the plurality of knowledge elements are captured using (1) one or more knowledge capturing tools and (2) one or more predefined knowledge reference components, and wherein the plurality of knowledge elements comprise a set of source code, a use case, and an incident resolution; and ii. associating each of the plurality of knowledge elements corresponding to an application being transitioned with one or more other knowledge elements corresponding to the same application, wherein the associations enable identification or search of knowledge elements corresponding to said application; c. a knowledge validation module configured for: i. securing from the second set of users responses to queries regarding the captured knowledge elements and the associations between the plurality of knowledge elements; and ii. enabling the first set of users to evaluate the responses received from the second set of users for assessing and validating the knowledge of the second set of users regarding the plurality of knowledge elements and the association between the plurality of knowledge elements, wherein the validation is performed based on a predefined set of rules; and d. a collaboration module configured for providing collaboration between the first set of users and the second set of users, wherein the collaboration enables communication between the first set of users and the second set of users during the transition of the one or more applications. | 1. A framework suitable for use in transition of one or more applications of an organization, the one or more applications being transitioned from a first set of users to a second set of users, the framework comprising: a. a transition module configured for generating one or more transition plans for the one or more applications, the one or more transition plans being generated based on information corresponding to the one or more applications, the one or more transition plans comprising one or more transition activities; b. a knowledge capture module configured for: i. capturing a plurality of knowledge elements corresponding to the one or more applications and the one or more transition activities, wherein the plurality of knowledge elements are captured using (1) one or more knowledge capturing tools and (2) one or more predefined knowledge reference components, and wherein the plurality of knowledge elements comprise a set of source code, a use case, and an incident resolution; and ii. associating each of the plurality of knowledge elements corresponding to an application being transitioned with one or more other knowledge elements corresponding to the same application, wherein the associations enable identification or search of knowledge elements corresponding to said application; c. a knowledge validation module configured for: i. securing from the second set of users responses to queries regarding the captured knowledge elements and the associations between the plurality of knowledge elements; and ii. enabling the first set of users to evaluate the responses received from the second set of users for assessing and validating the knowledge of the second set of users regarding the plurality of knowledge elements and the association between the plurality of knowledge elements, wherein the validation is performed based on a predefined set of rules; and d. a collaboration module configured for providing collaboration between the first set of users and the second set of users, wherein the collaboration enables communication between the first set of users and the second set of users during the transition of the one or more applications. 12. The framework of claim 1 , wherein the collaboration module comprises a workflow module configured for enabling a user to manage the plurality of knowledge elements, wherein managing the plurality of knowledge elements comprises maintaining a historical log for managing successive versions of one or more of the plurality of knowledge elements. | 0.639463 |
8,635,173 | 12 | 15 | 12. A system for publishing data, comprising: at least one receiving computing device in a first region of control that: receives from at least one publishing computing device in a second region of control, at least one data set published by the at least one publishing computing device; analyzes a subset of the at least one data set and inferring semantic information about the at least one data set relating to identifying information associated with data of the at least one data set or relating to type of the data of the at least one data set; and requests verification from the at least one publishing computing device that the semantic information inferred by the inferring is correct. | 12. A system for publishing data, comprising: at least one receiving computing device in a first region of control that: receives from at least one publishing computing device in a second region of control, at least one data set published by the at least one publishing computing device; analyzes a subset of the at least one data set and inferring semantic information about the at least one data set relating to identifying information associated with data of the at least one data set or relating to type of the data of the at least one data set; and requests verification from the at least one publishing computing device that the semantic information inferred by the inferring is correct. 15. The system of claim 12 , wherein the at least one receiving computing device further, in response to receiving the verification, re-defines semantics associated with data requests over the at least one data set based on the semantics information. | 0.601911 |
8,447,139 | 1 | 2 | 1. A method for detecting objects in a digital image, the method comprising: receiving at least one image representing at least one frame of a video sequence comprising zero or more objects of at least one desired object type; placing a sliding window of different window sizes at different locations in the at least one image; applying, for each window size and each location, a cascaded classifier comprising a plurality of increasingly accurate layers, each layer comprising a plurality of classifiers; evaluating, at each layer in the plurality of increasingly accurate layers, an area of the at least one image within a current sliding window using one or more weak classifiers in the plurality of classifiers based on at least one of Haar features and Histograms of Oriented Gradients (HOG) features, wherein an output of each weak classifier is a weak decision as to whether the area of the at least one image within the current sliding window comprises an instance of an object of the desired object type; identifying, based on the evaluating, a location within the image of the zero or more objects associated with the desired object type; and training each weak classifier in the plurality of classifiers based on Haar features and HOG features, wherein a selection of a subsequent weak classifier during the training is based on the subsequent weak classifier that provides a strongest separation between desired object types than other available weak classifiers independent of the subsequent weak classifier being associated with one of a Haar feature and a HOG feature. | 1. A method for detecting objects in a digital image, the method comprising: receiving at least one image representing at least one frame of a video sequence comprising zero or more objects of at least one desired object type; placing a sliding window of different window sizes at different locations in the at least one image; applying, for each window size and each location, a cascaded classifier comprising a plurality of increasingly accurate layers, each layer comprising a plurality of classifiers; evaluating, at each layer in the plurality of increasingly accurate layers, an area of the at least one image within a current sliding window using one or more weak classifiers in the plurality of classifiers based on at least one of Haar features and Histograms of Oriented Gradients (HOG) features, wherein an output of each weak classifier is a weak decision as to whether the area of the at least one image within the current sliding window comprises an instance of an object of the desired object type; identifying, based on the evaluating, a location within the image of the zero or more objects associated with the desired object type; and training each weak classifier in the plurality of classifiers based on Haar features and HOG features, wherein a selection of a subsequent weak classifier during the training is based on the subsequent weak classifier that provides a strongest separation between desired object types than other available weak classifiers independent of the subsequent weak classifier being associated with one of a Haar feature and a HOG feature. 2. The method of claim 1 , further comprising: visually indicating, in response to the identifying, the location of the zero or more objects. | 0.891871 |
9,031,845 | 18 | 21 | 18. A computer-implemented method for processing natural language utterances, the method being implemented by a computer system that includes one or more physical processors at a vehicle executing one or more computer program instructions which, when executed, perform the method, the method comprising: receiving, at the one or more physical processors, a natural language utterance associated with a user; performing, by the one or more physical processors, speech recognition on the natural language utterance; parsing and interpreting, by the one or more physical processors, the speech recognized natural language utterance; determining, by the one or more physical processors, a domain and a context that are associated with the parsed and interpreted natural language utterance; formulating, by the one or more physical processors, a command or query based on the domain and the context; determining, by the one or more physical processors, whether the command or query is to be executed on-board or off-board the vehicle; executing, by the one or more physical processors, the command or query at the vehicle in response to a determination that the command or query is to be executed on-board the vehicle; and invoking, by the one or more physical processors, a device that communicates wirelessly over a wide area network to process the command or query such that the command or query is executed off-board the vehicle in response to a determination that the command or query is to be executed off-board the vehicle. | 18. A computer-implemented method for processing natural language utterances, the method being implemented by a computer system that includes one or more physical processors at a vehicle executing one or more computer program instructions which, when executed, perform the method, the method comprising: receiving, at the one or more physical processors, a natural language utterance associated with a user; performing, by the one or more physical processors, speech recognition on the natural language utterance; parsing and interpreting, by the one or more physical processors, the speech recognized natural language utterance; determining, by the one or more physical processors, a domain and a context that are associated with the parsed and interpreted natural language utterance; formulating, by the one or more physical processors, a command or query based on the domain and the context; determining, by the one or more physical processors, whether the command or query is to be executed on-board or off-board the vehicle; executing, by the one or more physical processors, the command or query at the vehicle in response to a determination that the command or query is to be executed on-board the vehicle; and invoking, by the one or more physical processors, a device that communicates wirelessly over a wide area network to process the command or query such that the command or query is executed off-board the vehicle in response to a determination that the command or query is to be executed off-board the vehicle. 21. The method of claim 18 , further comprising: determining, by the one or more physical processors, whether executing the command or query will create a hazardous condition for the vehicle; providing, by the one or more physical processors, interactive guidance to resolve the hazardous condition via an output device connected to the vehicle based on a determination that executing the command or query will create the hazardous condition; and receiving, at the one or more physical processors, an input that manually overrides the hazardous condition determination, wherein the command or query is executed based on the manual override. | 0.637188 |
9,710,462 | 2 | 3 | 2. The method of claim 1 , wherein the determining of the plurality of alternative groups of symbols further comprises: identifying within the flattened surface representation a plurality of surface segments; and associating a respective symbol estimate with each surface segment of the plurality of surface segments. | 2. The method of claim 1 , wherein the determining of the plurality of alternative groups of symbols further comprises: identifying within the flattened surface representation a plurality of surface segments; and associating a respective symbol estimate with each surface segment of the plurality of surface segments. 3. The method of claim 2 , wherein the determining of the plurality of alternative groups of symbols further comprising: matching, by the system, at least one marking of the plurality of markings to symbols of a symbol dictionary. | 0.5 |
8,965,751 | 15 | 16 | 15. The social networking server of claim 11 , wherein the preferred language is determined automatically by querying the client for a language setting of the client. | 15. The social networking server of claim 11 , wherein the preferred language is determined automatically by querying the client for a language setting of the client. 16. The social networking server of claim 15 , wherein the client is a browser. | 0.5 |
9,152,730 | 10 | 11 | 10. A method, according to claim 9 , wherein a first set of formulas is used to determine the top score candidate and a second, different, set of formulas is used to determine if a different top score candidate should be selected. | 10. A method, according to claim 9 , wherein a first set of formulas is used to determine the top score candidate and a second, different, set of formulas is used to determine if a different top score candidate should be selected. 11. A method, according to claim 10 , wherein a different top score candidate is not used if using the second set of formulas results in the same top score candidate as using the first set of formulas. | 0.5 |
8,078,633 | 2 | 3 | 2. The method of claim 1 , wherein a frequency with which a candidate word group occurs in the corpus is based upon a combination of frequencies of one or more of the words from the candidate word group in the corpus. | 2. The method of claim 1 , wherein a frequency with which a candidate word group occurs in the corpus is based upon a combination of frequencies of one or more of the words from the candidate word group in the corpus. 3. The method of claim 2 , wherein the combination of frequencies comprises an aggregation of the frequencies of the one or more of the words from the candidate word group in the corpus. | 0.609244 |
6,047,293 | 13 | 19 | 13. The semiconductor test system of claim 12 in which said Dcontainer stores an indicator of the position of said named data in said ordered sequence vector. | 13. The semiconductor test system of claim 12 in which said Dcontainer stores an indicator of the position of said named data in said ordered sequence vector. 19. The semiconductor test system of claim 13 in which a commercially available program stores and searches said named data in said ordered sequence. | 0.641827 |
8,150,700 | 1 | 5 | 1. A mobile terminal, comprising: a display unit; an input unit configured to receive an input to activate a voice recognition function on the mobile terminal; a memory configured to store information related to a plurality of menus for operations performed on the mobile terminal; and a controller configured to: activate the voice recognition function upon receiving the input to activate the voice recognition function; determine a meaning of an input voice instruction based on at least one prior operation performed on the mobile terminal and a language included in the voice instruction; determine, among the plurality of menus, a number of menus that match the meaning of the input voice instruction with a probability greater than a predetermined threshold; and execute a single menu if there is the single menu that matches the meaning of the input voice instruction with the probability greater than the predetermined threshold, or display the menus on the display unit if the number of menus is greater than one. | 1. A mobile terminal, comprising: a display unit; an input unit configured to receive an input to activate a voice recognition function on the mobile terminal; a memory configured to store information related to a plurality of menus for operations performed on the mobile terminal; and a controller configured to: activate the voice recognition function upon receiving the input to activate the voice recognition function; determine a meaning of an input voice instruction based on at least one prior operation performed on the mobile terminal and a language included in the voice instruction; determine, among the plurality of menus, a number of menus that match the meaning of the input voice instruction with a probability greater than a predetermined threshold; and execute a single menu if there is the single menu that matches the meaning of the input voice instruction with the probability greater than the predetermined threshold, or display the menus on the display unit if the number of menus is greater than one. 5. The mobile terminal of claim 1 , wherein the controller is further configured to discriminately display a menu option corresponding to the meaning of the input voice instruction and having a highest probability among the number of menus that match the meaning of the input voice instruction with the probability greater than the predetermined threshold. | 0.711039 |
9,128,994 | 9 | 13 | 9. A computer program product for use at computer system, the computer system including a display device, the computer program product for implementing a method for visually representing a query of multi-source data, the computer program product comprising one or more computer storage devices having stored thereon computer-executable instructions that, when executed a processor, cause the computer system to perform the method, including the following: access a data set, the data set including data combined from a plurality of different sources; supplement presentation of data contained in a portion of the data set on the display device by using a visual cue to visually indicate any occurrences of a query term within the data contained in the portion of the data set on the display device, visually indicating occurrences of the query term highlighting the volume of the query term within the data contained in the portion of the data set; receive a command to query a larger portion of the data set for the query term, the larger portion of the data set including the portion of the data set; determine that data contained in the data set is not to be displayed on the display device in response to the command; and present the visual cue in an arrangement of locations on the display device to visually indicate occurrences of the query term in the data contained in the larger portion of the data set, each location in the arrangement of locations indicating where the query term would have be presented on the display device had the data contained in the larger portion of the data set been presented at the display device, presenting the arrangement of locations permitting insight into the volume of the query term in the data contained in the larger portion of the data set without consuming presentation space on the display device to present the data contained in the larger portion of the data set. | 9. A computer program product for use at computer system, the computer system including a display device, the computer program product for implementing a method for visually representing a query of multi-source data, the computer program product comprising one or more computer storage devices having stored thereon computer-executable instructions that, when executed a processor, cause the computer system to perform the method, including the following: access a data set, the data set including data combined from a plurality of different sources; supplement presentation of data contained in a portion of the data set on the display device by using a visual cue to visually indicate any occurrences of a query term within the data contained in the portion of the data set on the display device, visually indicating occurrences of the query term highlighting the volume of the query term within the data contained in the portion of the data set; receive a command to query a larger portion of the data set for the query term, the larger portion of the data set including the portion of the data set; determine that data contained in the data set is not to be displayed on the display device in response to the command; and present the visual cue in an arrangement of locations on the display device to visually indicate occurrences of the query term in the data contained in the larger portion of the data set, each location in the arrangement of locations indicating where the query term would have be presented on the display device had the data contained in the larger portion of the data set been presented at the display device, presenting the arrangement of locations permitting insight into the volume of the query term in the data contained in the larger portion of the data set without consuming presentation space on the display device to present the data contained in the larger portion of the data set. 13. The computer program product of claim 9 , further comprising computer-executable instructions that, when executed, cause the computer system to: receive a second query term for searching the data set; and assign a second different visual cue to the second query term; and wherein computer-executable instructions that, when executed, cause the computer system to supplement presentation of the data contained in the portion of the data set comprise computer-executable instructions that, when executed, cause the computer system to supplement presentation of the data contained in the portion of the data set by using the visual cue and the second different visual cue to visually indicate any occurrences of the query term and the second query term respectively within the data contained in the portion of the data set, visually indicating occurrences of the query term and the second query term within the data contained in the portion of the data set indicating the relative differences in volume between the query term and the second query term within the data contained in the portion of the data set. | 0.5 |
8,447,602 | 3 | 8 | 3. A correction device for correcting a text recognized by a speech recognition device, the correction device comprising: a storage device configured to store a lexicon of alternatives comprising a plurality of entries, wherein the plurality of entries include one or more alternatives determined from one or more sources of knowledge that are independent of an analysis by an acoustic model and a language model used by the speech recognition device, wherein the one or more sources of knowledge include confusion information compiled from corrected texts and associated recognized texts; and at least one processor configured to: display at least some of the plurality of entries as a list of alternatives to individual word parts, words and/or word sequences of the recognized text. | 3. A correction device for correcting a text recognized by a speech recognition device, the correction device comprising: a storage device configured to store a lexicon of alternatives comprising a plurality of entries, wherein the plurality of entries include one or more alternatives determined from one or more sources of knowledge that are independent of an analysis by an acoustic model and a language model used by the speech recognition device, wherein the one or more sources of knowledge include confusion information compiled from corrected texts and associated recognized texts; and at least one processor configured to: display at least some of the plurality of entries as a list of alternatives to individual word parts, words and/or word sequences of the recognized text. 8. A correction device as claimed in claim 3 , wherein the at least one processor is further configured to: update the list of alternatives for at least some of the plurality of entries in the lexicon of alternatives displayed for a particular individual word part, word, and/or word sequence based, at least in part, on information about at least one previous correction made by the correction device for the particular individual word part, word, and/or word sequence with a text element replacement selected by a user, wherein the list of alternatives is updated only when at least a predetermined degree of phonetic similarity exists between the particular individual word part, word, and/or word sequence and a text replacement in the at least one previous correction. | 0.5 |
9,715,943 | 3 | 5 | 3. The semiconductor memory cell MWA encoding apparatus of claim 2 , the MCRA decoder further comprising: a data word generator to generate a series of m-bit data words D, each data word having a binary weighted value K, from K=1 through K=2^m−1; a data word index generator to generate a series of data word bit position indices i from i=0 through i=m−1; a data bit indexer coupled to the data word index generator and to the data word generator to receive a current value of the index i from the data word index generator and to receive a current value of the data word K from the data word generator and to output a state of the i th bit of the current K, B(K,i); a code bit indexer coupled to the data word generator to receive the current K and coupled to a decoder input terminal to receive a code word C to be decoded, the code bit indexer to output a state of the (K−1) th bit of C, C(K−1); and intersection XOR logic communicatively coupled to the data bit indexer and to the code bit indexer to perform, for each i, an XOR operation on all bits for which B(K,i) and C(K−1) are both logical “1” in order to generate a corresponding bit D(i) of a decoded data word D. | 3. The semiconductor memory cell MWA encoding apparatus of claim 2 , the MCRA decoder further comprising: a data word generator to generate a series of m-bit data words D, each data word having a binary weighted value K, from K=1 through K=2^m−1; a data word index generator to generate a series of data word bit position indices i from i=0 through i=m−1; a data bit indexer coupled to the data word index generator and to the data word generator to receive a current value of the index i from the data word index generator and to receive a current value of the data word K from the data word generator and to output a state of the i th bit of the current K, B(K,i); a code bit indexer coupled to the data word generator to receive the current K and coupled to a decoder input terminal to receive a code word C to be decoded, the code bit indexer to output a state of the (K−1) th bit of C, C(K−1); and intersection XOR logic communicatively coupled to the data bit indexer and to the code bit indexer to perform, for each i, an XOR operation on all bits for which B(K,i) and C(K−1) are both logical “1” in order to generate a corresponding bit D(i) of a decoded data word D. 5. The semiconductor memory cell MWA encoding apparatus of claim 3 , the MCRA decoder further comprising: data value tracking logic coupled to the data word generator to determine whether all possible data words have been generated while i is set to a current value, to request a next K if all possible data words have not been generated for a current value of i, and to initiate the XOR operation if all possible data words have been generated while i is set to the current value; and data word index tracking logic coupled to the data word index generator to determine whether all values of i have been generated following an XOR operation, to request a next i if all values of i have not been generated, and to terminate decoding if all value of i have been generated. | 0.5 |
8,706,653 | 2 | 3 | 2. A method as claimed in claim 1 wherein accessing features comprises accessing question features and judge features and wherein using the probabilistic learning system to identify an expertise of each judge comprises mapping the question features and judge features into a trait space which may be any of: a one dimensional trait space, a two dimensional trait space, a trait space of higher dimension than two. | 2. A method as claimed in claim 1 wherein accessing features comprises accessing question features and judge features and wherein using the probabilistic learning system to identify an expertise of each judge comprises mapping the question features and judge features into a trait space which may be any of: a one dimensional trait space, a two dimensional trait space, a trait space of higher dimension than two. 3. A method as claimed in claim 2 wherein mapping the judges and questions into a trait space is achieved using a linear mapping. | 0.5 |
7,945,632 | 31 | 36 | 31. The hardware-implemented system of claim 1 , wherein said correlation module configured to correlate the acquired subjective user state data with the acquired objective occurrence data by determining at least one sequential pattern associated with the at least one subjective user state and the at least one objective occurrence comprises: a sequential pattern comparison module configured to compare the one sequential pattern to a second sequential pattern to determine whether the one sequential pattern at least substantially matches the second sequential pattern. | 31. The hardware-implemented system of claim 1 , wherein said correlation module configured to correlate the acquired subjective user state data with the acquired objective occurrence data by determining at least one sequential pattern associated with the at least one subjective user state and the at least one objective occurrence comprises: a sequential pattern comparison module configured to compare the one sequential pattern to a second sequential pattern to determine whether the one sequential pattern at least substantially matches the second sequential pattern. 36. The hardware-implemented system of claim 31 , wherein said sequential pattern comparison module configured to compare the one sequential pattern to a second sequential pattern to determine whether the one sequential pattern at least substantially matches the second sequential pattern comprises: an objective occurrence equivalence determination module configured to determine whether the one objective occurrence is equivalent to a second objective occurrence indicated by the objective occurrence data. | 0.615734 |
8,438,053 | 2 | 3 | 2. The computer processor implemented method of claim 1 , further comprising a step of: cc. create a root node for the work breakdown structure code on level 1 from an application work breakdown structure table. | 2. The computer processor implemented method of claim 1 , further comprising a step of: cc. create a root node for the work breakdown structure code on level 1 from an application work breakdown structure table. 3. The computer processor implemented method of claim 2 , further comprising a step of: dd. increment the work breakdown structure level by 1. | 0.5 |
8,620,909 | 22 | 24 | 22. A computer system for contextual personalized search, the system comprising: a processor; a non-transitory computer-readable storage medium storing program code, executable by the processor, comprising: a knowledge base comprising a hierarchy of nodes, each node associated with a concept, each concept being an instance of a category, wherein one or more documents are mapped to each of the nodes of the knowledge base; and a search engine for: receiving an input query for a search for one or more of the documents mapped to the nodes, the input query comprising a plurality of components; mapping at least some of the components of the search query into the knowledge base, each of the at least some of the components mapped to at least one of the nodes of the knowledge base having a concept that matches the component as a query concept; matching the query concepts to documents mapped to the knowledge base that match the query concepts, the matching performed by: traversing the hierarchy of the knowledge base to match nodes of each of the query concepts across other nodes in the hierarchy of the knowledge base to a plurality of target nodes, at least one of the query concepts being in a first category and being a) matched using transitivity from the first category through at least one second category to at least one of the target nodes in a third category, or being b) matched using transitive closure to a plurality of the target nodes in the first category; selecting, as matches for the input query, each of the documents mapped to one of the target nodes, the selected documents being target documents for the input query; scoring each of the target documents against each of the query concepts to provide a score for each of the target documents; and generating a search result for the input query, the search result comprising at least some of the target documents in ranked order based on the score for each target document. | 22. A computer system for contextual personalized search, the system comprising: a processor; a non-transitory computer-readable storage medium storing program code, executable by the processor, comprising: a knowledge base comprising a hierarchy of nodes, each node associated with a concept, each concept being an instance of a category, wherein one or more documents are mapped to each of the nodes of the knowledge base; and a search engine for: receiving an input query for a search for one or more of the documents mapped to the nodes, the input query comprising a plurality of components; mapping at least some of the components of the search query into the knowledge base, each of the at least some of the components mapped to at least one of the nodes of the knowledge base having a concept that matches the component as a query concept; matching the query concepts to documents mapped to the knowledge base that match the query concepts, the matching performed by: traversing the hierarchy of the knowledge base to match nodes of each of the query concepts across other nodes in the hierarchy of the knowledge base to a plurality of target nodes, at least one of the query concepts being in a first category and being a) matched using transitivity from the first category through at least one second category to at least one of the target nodes in a third category, or being b) matched using transitive closure to a plurality of the target nodes in the first category; selecting, as matches for the input query, each of the documents mapped to one of the target nodes, the selected documents being target documents for the input query; scoring each of the target documents against each of the query concepts to provide a score for each of the target documents; and generating a search result for the input query, the search result comprising at least some of the target documents in ranked order based on the score for each target document. 24. The system of claim 22 , further comprising a plurality of knowledge bases, each knowledge base including a set of categories, each category including a set of attributes defining a relationship between categories or within a category, and an attribute path defines a relationship between categories and attributes across the knowledge bases. | 0.667308 |
5,438,180 | 12 | 20 | 12. A cooking oven having means defining an oven cavity, heating means for heating said oven cavity to defined heating temperatures and a control means for receiving user input selections and for establishing a time and temperature heating profile of said oven cavity and for controlling said heating means according to said heating profile, said control means comprising: input selection means for receiving user input selections; logic means responsive to said input means for establishing a time and temperature heating profile of said oven and for controlling said heating means according to said heating profile; and said input selection means including a panel and selection means on said panel for receiving user input selections of a heating function, heating parameters, timing function and timing parameters, said selection means including parameter display devices positioned on said panel each for displaying a selected value of one of said parameters, selection devices for selecting parameter values for said parameter display devices, parameter indicia on said panel adjacent said display device for identifying which of said parameters is displayed on the display devices and modifying indicia on said panel adjacent said parameter indicia for providing grammatical modification to the parameter indicia, wherein said modifying indicia and said parameter indicia read as a grammatical sentence format. | 12. A cooking oven having means defining an oven cavity, heating means for heating said oven cavity to defined heating temperatures and a control means for receiving user input selections and for establishing a time and temperature heating profile of said oven cavity and for controlling said heating means according to said heating profile, said control means comprising: input selection means for receiving user input selections; logic means responsive to said input means for establishing a time and temperature heating profile of said oven and for controlling said heating means according to said heating profile; and said input selection means including a panel and selection means on said panel for receiving user input selections of a heating function, heating parameters, timing function and timing parameters, said selection means including parameter display devices positioned on said panel each for displaying a selected value of one of said parameters, selection devices for selecting parameter values for said parameter display devices, parameter indicia on said panel adjacent said display device for identifying which of said parameters is displayed on the display devices and modifying indicia on said panel adjacent said parameter indicia for providing grammatical modification to the parameter indicia, wherein said modifying indicia and said parameter indicia read as a grammatical sentence format. 20. The cooking oven of claim 12 wherein said selection devices include a heating mode selection device for receiving a user selection of a heating mode from one of a cooking mode and a cleaning mode, a temperature entry device for entering a heating temperature for said oven cavity, a time mode selection device for receiving user selections of at least one timing mode independently of the selected heating mode wherein any of said timing modes may be selected with any one of said heating functions, and a time entry device for entering time values for the selected timing modes. | 0.5 |
7,668,719 | 1 | 6 | 1. An information processing method for setting data to each of a plurality of settable graphical user interface items, the method comprising: detecting a settable graphical user interface item not displayed on a display screen; selecting a speech recognition grammar corresponding to the detected item, wherein the selected speech recognition grammar is speech recognition grammar which is limited to a subset of the speech recognition grammar used in a case where the item is displayed; recognizing received speech information using the selected speech recognition grammar; and setting data to the detected item using a recognition result of the recognizing step. | 1. An information processing method for setting data to each of a plurality of settable graphical user interface items, the method comprising: detecting a settable graphical user interface item not displayed on a display screen; selecting a speech recognition grammar corresponding to the detected item, wherein the selected speech recognition grammar is speech recognition grammar which is limited to a subset of the speech recognition grammar used in a case where the item is displayed; recognizing received speech information using the selected speech recognition grammar; and setting data to the detected item using a recognition result of the recognizing step. 6. The information processing method according to claim 1 , wherein the limited speech recognition grammar is a speech recognition grammar in which a specific vocabulary set has been deleted. | 0.81528 |
9,224,038 | 21 | 27 | 21. A non-transitory computer-readable medium storing software comprising instructions executable by one or more computers which, upon such execution, cause the one or more computers to perform operations comprising: receiving a sequence of strokes that represent a handwritten input; providing the sequence of strokes to both a delayed stroke handwriting recognizer and an overlapping handwriting recognizer, wherein a delayed stroke handwriting recognizer is a handwriting recognizer that orders strokes according to their respective horizontal spatial location before selecting one or more characters that correspond to the strokes, wherein an overlapping handwriting recognizer is a handwriting recognizer that does not reorder strokes according to their respective horizontal spatial location before selecting one or more characters that correspond to the strokes; receiving, from each of the delayed stroke handwriting recognizer and the overlapping handwriting recognizer, (i) a set of one or more candidate letters that the handwriting recognizer has selected as corresponding to the sequence of strokes, and (ii) a confidence score associated with the selection; and selecting, from among the set of candidate letters from the delayed stroke handwriting recognizer and the set of candidate letters from the overlapping handwriting recognizer, a particular set of candidate letters based on the confidence scores. | 21. A non-transitory computer-readable medium storing software comprising instructions executable by one or more computers which, upon such execution, cause the one or more computers to perform operations comprising: receiving a sequence of strokes that represent a handwritten input; providing the sequence of strokes to both a delayed stroke handwriting recognizer and an overlapping handwriting recognizer, wherein a delayed stroke handwriting recognizer is a handwriting recognizer that orders strokes according to their respective horizontal spatial location before selecting one or more characters that correspond to the strokes, wherein an overlapping handwriting recognizer is a handwriting recognizer that does not reorder strokes according to their respective horizontal spatial location before selecting one or more characters that correspond to the strokes; receiving, from each of the delayed stroke handwriting recognizer and the overlapping handwriting recognizer, (i) a set of one or more candidate letters that the handwriting recognizer has selected as corresponding to the sequence of strokes, and (ii) a confidence score associated with the selection; and selecting, from among the set of candidate letters from the delayed stroke handwriting recognizer and the set of candidate letters from the overlapping handwriting recognizer, a particular set of candidate letters based on the confidence scores. 27. The medium of claim 21 , wherein the confidence score for each of the overlapping handwriting recognizer and the delayed stroke handwriting recognizer is based at least in part on a similarity between a portion of the sequence of strokes and handwriting characters and on confidence scores of a previously received portion of the sequence of strokes. | 0.5 |
8,701,032 | 4 | 5 | 4. The method of claim 1 , wherein the word-level token is a first word-level token comprising a first single string of the plurality of predicted characters, and wherein determining the phrase-level token comprises: generating, in response to determining that the first word-level token represents the candidate word in the lexicon, a next-word token that indicates that a next selected key of the plurality of keys is a prefix of a second word-level token; determining, in response to determining the next-word token, a second word-level token comprising a second single string of the plurality of predicted characters, wherein the second key is a prefix of the second word-level token; and determining the phrase-level token as a combination of the first word-level token and the second word-level token. | 4. The method of claim 1 , wherein the word-level token is a first word-level token comprising a first single string of the plurality of predicted characters, and wherein determining the phrase-level token comprises: generating, in response to determining that the first word-level token represents the candidate word in the lexicon, a next-word token that indicates that a next selected key of the plurality of keys is a prefix of a second word-level token; determining, in response to determining the next-word token, a second word-level token comprising a second single string of the plurality of predicted characters, wherein the second key is a prefix of the second word-level token; and determining the phrase-level token as a combination of the first word-level token and the second word-level token. 5. The method of claim 4 , wherein determining the phrase-level token further comprises: determining, based on a plurality of features associated with the gesture, a group of alignment points traversed by the gesture; determining respective cost values for each of at least the first key, the second key, and a third key, wherein each of the respective cost values represents a probability that an alignment point of the group of alignment points indicates a key of the plurality of keys; determining a first combined cost value based at least in part on the determined cost value for the first key and the determined cost value for the second key; determining a second combined cost value based at least in part on the determined cost value for the first key and the determined cost value for the third key; comparing the first combined cost value and the second combined cost value; and determining the second word-level token based on the comparison of the first combined cost value and the second combined cost value. | 0.5 |
8,275,619 | 7 | 8 | 7. A computer program product comprising a non-transitory computer readable medium having computer-executable instructions thereon for speech recognition of a speech signal comprising, the computer-executable instructions comprising: computer code for providing at least one codebook comprising code-book entries, in particular, multivariate Gaussians of feature vectors, that are frequency weighted; and computer code for processing the speech signal for speech recognition comprising: computer code for extracting at least one feature vector from the speech signal and computer code for matching the feature vector with the entries of the codebook; computer code for providing at least one additional codebook comprising codebook entries, in particular, multivariate Gaussians of feature vectors, without frequency weights; computer code for determining whether the speech signal corresponds to an utterance of a native speaker or to an utterance of a non-native speaker; and computer code for using the at least one additional codebook comprising codebook entries without frequency weights for the speech recognition if it is determined that the speech signal corresponds to the utterance of a native speaker; computer code for using the at least one codebook comprising codebook entries that are frequency weighted for the speech recognition if it is determined that the speech signal corresponds to the utterance of a non-native speaker. | 7. A computer program product comprising a non-transitory computer readable medium having computer-executable instructions thereon for speech recognition of a speech signal comprising, the computer-executable instructions comprising: computer code for providing at least one codebook comprising code-book entries, in particular, multivariate Gaussians of feature vectors, that are frequency weighted; and computer code for processing the speech signal for speech recognition comprising: computer code for extracting at least one feature vector from the speech signal and computer code for matching the feature vector with the entries of the codebook; computer code for providing at least one additional codebook comprising codebook entries, in particular, multivariate Gaussians of feature vectors, without frequency weights; computer code for determining whether the speech signal corresponds to an utterance of a native speaker or to an utterance of a non-native speaker; and computer code for using the at least one additional codebook comprising codebook entries without frequency weights for the speech recognition if it is determined that the speech signal corresponds to the utterance of a native speaker; computer code for using the at least one codebook comprising codebook entries that are frequency weighted for the speech recognition if it is determined that the speech signal corresponds to the utterance of a non-native speaker. 8. The computer program product according to claim 7 , wherein the entries of the at least one code-book are obtained by: detecting training speech signals corresponding to utterances of one or more native speakers; transforming the training speech signals into a frequency domain; applying a MEL filterbank to the transformed training speech signals and saving the log value of each bank to derive first representations of the training speech signals; applying a Discrete Cosine Transform to the first representations of the training speech signals to obtain second transformed training speech signals; applying a Linear Discriminant Analysis to the second transformed training speech signals to obtain third transformed training speech signals; extracting feature vectors from the third transformed training speech signals; determining covariance matrices for the extracted feature vectors in the Linear Discriminant Analysis domain; transforming the covariance matrices into the log-MEL domain; applying weights to the covariances of the covariance matrices in the log-MEL domain to obtain modified covariance matrices in the log-MEL domain; transforming the modified covariance matrices from the log-MEL domain into the Linear Discriminant Analysis domain; and determining Gaussians from the modified covariance matrices to obtain the entries of the at least one codebook. | 0.5 |
9,614,724 | 15 | 16 | 15. The method as recited in claim 13 , further comprising: receiving an indication of a change in the communication session; generating a reconfiguration event that includes at least one of a change to the parameter for the communication session or a change to a different parameter for the communication session; and communicating the reconfiguration event to the client device. | 15. The method as recited in claim 13 , further comprising: receiving an indication of a change in the communication session; generating a reconfiguration event that includes at least one of a change to the parameter for the communication session or a change to a different parameter for the communication session; and communicating the reconfiguration event to the client device. 16. The method as recited in claim 15 , wherein the indication of the change includes an indication of a problem with session quality for the communication session, and wherein at least one of the change to the parameter for the communication session or the change to the different parameter is specified to increase the session quality for the communication session. | 0.5 |
6,119,114 | 3 | 4 | 3. The method of claim 1 further comprising the step of determining, on the basis of said absolute-relevance score of said newly-received document and said first collective absolute relevance, whether to replace said first set of training data by a second set of training data formed by adding said newly-received document to said first set of training data. | 3. The method of claim 1 further comprising the step of determining, on the basis of said absolute-relevance score of said newly-received document and said first collective absolute relevance, whether to replace said first set of training data by a second set of training data formed by adding said newly-received document to said first set of training data. 4. The method of claim 3 wherein the step of determining whether to replace said first set of training data further comprises the steps of: evaluating an absolute relevance for said newly-received document; selecting, on the basis of said relative relevance threshold, a first training document to be a least-relevant document; determining whether said newly-received document has a higher absolute relevance score than said least-relevant document; if said newly-received document has a higher absolute relevance score than said least-relevant document, replacing said first set of training data with a second set of training data formed by incorporating said newly-received document into said plurality of training documents; and selecting, on the basis of said relative-relevance threshold, a second training document from said second set of training data to be said least-relevant document, said second training document having a higher absolute relevance score than said first training document; whereby successive newly-received documents require progressively higher absolute relevance scores to surpass said relative relevance threshold. | 0.5 |
8,676,627 | 3 | 4 | 3. The method as claimed in claim 1 wherein the first business process model project is a business modeler project, the second business process model project is an integration developer project and the new business process model project is a new business modeler project. | 3. The method as claimed in claim 1 wherein the first business process model project is a business modeler project, the second business process model project is an integration developer project and the new business process model project is a new business modeler project. 4. The method as claimed in claim 3 wherein the business modeler project includes the first business modeler language. | 0.5 |
9,519,707 | 13 | 16 | 13. A method of identifying one or more document within a collection of documents, the method being performed by a computer system that comprises one or more processors, a memory operatively coupled to at least one of the processors, and a computer-readable storage medium encoded with instructions executable by at least one of the processors and operatively coupled to at least one of the processors, the method comprising: storing in the memory a search level that is a whole number that is at least two; storing in the memory a definition of a subset of a collection of electronic documents, the collection of documents comprising a plurality of documents, and the subset comprising one or more source documents within the collection and one or more additional documents within the collection, the one or more additional documents being identifiable by a process carried out for a number of iterations equal to the search level and comprising: (1) a first iteration that comprises finding one or more citing documents within the collection, each citing document comprising at least one reference to at least one of the electronic source documents, and adding to the subset each of the found citing documents that is not already in the subset, and (2) one or more subsequent iterations, each of which comprises finding one or more citing documents within the collection, each citing document comprising at least one reference to at least one of the documents added to the subset in the immediately previous iteration, and adding to the subset each of the found citing documents that is not already in the subset, at least one of the processors receiving through at least one interface operatively coupled to the processor a definition of a search query through an interface operatively coupled to at least one of the processors, the search query comprising one or more criteria that a user has explicitly entered, and the search query having an association with a topical area for a search; at least one of the processors executing instructions retrieved from the computer readable storage medium to (i) identify all responsive documents within the subset that satisfy the one or more criteria comprised by the search query such that each responsive document includes each of the one or more criteria of the search query, (ii) retrieve a definition of a search space, the definition of the search space comprising one or more normalized citations to every document within the search space, and the search space having an association with the topical area for the search, (iii) filter the responsive documents resulting from the execution of the search query by checking each responsive document against the definition of the search space and (iv) removing from further consideration an responsive document not found in the definition of the search space; at least one of the processors executing instructions retrieved from the computer-readable storage medium to transmit through the at least one interface information for display to the user that identifies one or more of the remaining responsive documents. | 13. A method of identifying one or more document within a collection of documents, the method being performed by a computer system that comprises one or more processors, a memory operatively coupled to at least one of the processors, and a computer-readable storage medium encoded with instructions executable by at least one of the processors and operatively coupled to at least one of the processors, the method comprising: storing in the memory a search level that is a whole number that is at least two; storing in the memory a definition of a subset of a collection of electronic documents, the collection of documents comprising a plurality of documents, and the subset comprising one or more source documents within the collection and one or more additional documents within the collection, the one or more additional documents being identifiable by a process carried out for a number of iterations equal to the search level and comprising: (1) a first iteration that comprises finding one or more citing documents within the collection, each citing document comprising at least one reference to at least one of the electronic source documents, and adding to the subset each of the found citing documents that is not already in the subset, and (2) one or more subsequent iterations, each of which comprises finding one or more citing documents within the collection, each citing document comprising at least one reference to at least one of the documents added to the subset in the immediately previous iteration, and adding to the subset each of the found citing documents that is not already in the subset, at least one of the processors receiving through at least one interface operatively coupled to the processor a definition of a search query through an interface operatively coupled to at least one of the processors, the search query comprising one or more criteria that a user has explicitly entered, and the search query having an association with a topical area for a search; at least one of the processors executing instructions retrieved from the computer readable storage medium to (i) identify all responsive documents within the subset that satisfy the one or more criteria comprised by the search query such that each responsive document includes each of the one or more criteria of the search query, (ii) retrieve a definition of a search space, the definition of the search space comprising one or more normalized citations to every document within the search space, and the search space having an association with the topical area for the search, (iii) filter the responsive documents resulting from the execution of the search query by checking each responsive document against the definition of the search space and (iv) removing from further consideration an responsive document not found in the definition of the search space; at least one of the processors executing instructions retrieved from the computer-readable storage medium to transmit through the at least one interface information for display to the user that identifies one or more of the remaining responsive documents. 16. The method of claim 13 , wherein the search query is a natural-language query that the user has entered. | 0.928287 |
9,183,464 | 14 | 21 | 14. A face annotation system for a current owner to annotate contacts in online social networks (OSNs), the face annotation system comprising: a pyramid database access control (PDAC) module, consisting of a plurality of pyramid database units and performing a first batch of access control procedure and a non-first batch of access control procedure, wherein the pyramid database unit includes a plurality layers of database and is constructed according to social relationship information; and a multiple-kernel learning face recognition (MKL-FR) module of the current owner implemented through the use of a MKL classifier unit, using a MKL algorithm to achieve a face identification, wherein the MKL classifier unit is trained with the pyramid database unit accessed in OSNs and includes an offline learning procedure and an online recognition procedure. | 14. A face annotation system for a current owner to annotate contacts in online social networks (OSNs), the face annotation system comprising: a pyramid database access control (PDAC) module, consisting of a plurality of pyramid database units and performing a first batch of access control procedure and a non-first batch of access control procedure, wherein the pyramid database unit includes a plurality layers of database and is constructed according to social relationship information; and a multiple-kernel learning face recognition (MKL-FR) module of the current owner implemented through the use of a MKL classifier unit, using a MKL algorithm to achieve a face identification, wherein the MKL classifier unit is trained with the pyramid database unit accessed in OSNs and includes an offline learning procedure and an online recognition procedure. 21. The face annotation system as recited in claim 14 , wherein the pyramid database unit is constructed by evaluating a strength of recent social relationships between each contact and the current owner and normalizing a connection score, wherein the connection score is calculated by an equation which reflects the connection strength between contacts during a recent time period, and is evaluated by incorporating two main considerations including an unidirectional connection function and a bidirectional connection function, wherein the unidirectional connection function estimates the distribution of likelihoods of each contact occurring in recent personal photographs of the current owner, and the bidirectional connection function estimates the distribution of likelihoods of each contact and the current owner co-occurring in the recent personal photographs in the entire OSN. | 0.54888 |
9,779,028 | 17 | 19 | 17. The method of claim 11 , wherein searching of any of the VA-indexed structures in the first processing element to find any entries that correspond to a virtual address associated with any set of one or more translation context values causes an interruption to a pipeline of the first processing element. | 17. The method of claim 11 , wherein searching of any of the VA-indexed structures in the first processing element to find any entries that correspond to a virtual address associated with any set of one or more translation context values causes an interruption to a pipeline of the first processing element. 19. The method of claim 17 , wherein the delaying causes the third invalidation message to be handled without interrupting the pipeline of the first processing element at least until the one or more invalidation-tracking structures are flushed. | 0.5 |
9,076,459 | 24 | 26 | 24. A speech recognition system, comprising: a sound classifier that includes at least one non-transitory processor-readable medium and at least one processor communicatively coupled to the at least one non-transitory processor-readable medium, and that analyzes each of a plurality of frames of audio, classifies a first number of the frames of audio as speech by the sound classifier, classifies a second number of the frames of audio as non-transient background noise by the sound classifier, classifies a third number of the frames of audio as transient noise events by the sound classifier, and provides signals indicative at least of the classifications of the frames of audio. | 24. A speech recognition system, comprising: a sound classifier that includes at least one non-transitory processor-readable medium and at least one processor communicatively coupled to the at least one non-transitory processor-readable medium, and that analyzes each of a plurality of frames of audio, classifies a first number of the frames of audio as speech by the sound classifier, classifies a second number of the frames of audio as non-transient background noise by the sound classifier, classifies a third number of the frames of audio as transient noise events by the sound classifier, and provides signals indicative at least of the classifications of the frames of audio. 26. The speech recognition system of claim 24 , further comprising: a speech recognizer communicatively coupled to receive the signals from the sound classifier and operable to distinguish sets of fragments containing speech from sets of fragments not containing speech based at least in part on the classifications indicated in the signals received from the sound classifier. | 0.559719 |
7,711,569 | 1 | 2 | 1. A chat information system having a voice recognition device for recognizing voices, a voice synthesizer, a humanoid robot, a microphone for receiving the voices and a speaker for pronouncing synthesized voices, comprising: news capturing means for capturing news from the Internet; a news database for storing the captured news as a news reply, wherein the news reply has a plurality of priorities; a conversation database including at least a general conversation database storing a set of inquires and responses as conversation replies and a random chat database storing a plurality of predetermined responses, wherein a conversation reply has a plurality of priorities and at least one of the conversation replies belongs to one of a plurality of predetermined chat patterns; and a chat engine configured to: extract one or more keywords from a user's speech that has been recognized by the voice recognition device; determine whether a reserved chat flag associated with the extracted keywords is set, the reserved chat flag being set responsive to the extracted keywords belonging to one of the predetermined chat patterns; responsive to the reserved chat flag being set, setting a reply to a predetermined response of the plurality of the predetermined responses or a predetermined chat pattern of the plurality of the chat patterns; responsive to the reserved chat flag not being set, search at least one of the news database, the conversation database, and responsive to no matching being found from at least one of the news database and the conversation database, search the random chat database with the extracted keywords; select the reply among a plurality of replies found by the search based on the priorities of the replies; update the plurality of priorities associated with the replies; and output via the speaker the reply. | 1. A chat information system having a voice recognition device for recognizing voices, a voice synthesizer, a humanoid robot, a microphone for receiving the voices and a speaker for pronouncing synthesized voices, comprising: news capturing means for capturing news from the Internet; a news database for storing the captured news as a news reply, wherein the news reply has a plurality of priorities; a conversation database including at least a general conversation database storing a set of inquires and responses as conversation replies and a random chat database storing a plurality of predetermined responses, wherein a conversation reply has a plurality of priorities and at least one of the conversation replies belongs to one of a plurality of predetermined chat patterns; and a chat engine configured to: extract one or more keywords from a user's speech that has been recognized by the voice recognition device; determine whether a reserved chat flag associated with the extracted keywords is set, the reserved chat flag being set responsive to the extracted keywords belonging to one of the predetermined chat patterns; responsive to the reserved chat flag being set, setting a reply to a predetermined response of the plurality of the predetermined responses or a predetermined chat pattern of the plurality of the chat patterns; responsive to the reserved chat flag not being set, search at least one of the news database, the conversation database, and responsive to no matching being found from at least one of the news database and the conversation database, search the random chat database with the extracted keywords; select the reply among a plurality of replies found by the search based on the priorities of the replies; update the plurality of priorities associated with the replies; and output via the speaker the reply. 2. The system of claim 1 , wherein the chat engine includes priority changing means for lowering the priority of the reply that has been found by the search and has been output as a response relative to the other replies. | 0.539583 |
8,972,410 | 7 | 8 | 7. A method according to claim 1 , wherein plural of the sample vectors have been selected to represent exemplary patterns of a category to which the existing object belongs. | 7. A method according to claim 1 , wherein plural of the sample vectors have been selected to represent exemplary patterns of a category to which the existing object belongs. 8. A method according to claim 7 , wherein the exemplary patterns comprise image patterns. | 0.5 |
8,024,317 | 1 | 9 | 1. A method comprising the steps: receiving, over a network, by a computing device, a user context query from a user, wherein the user context query is formatted as a parameter of a universal resource locator (URL) and comprises user context criteria; formulating, via the network, a network data query based on the user context criteria so as to search, via the network, for user profile data, social network data, spatial data, temporal data, topical data and context query bid data that is available via the network and relates to the user context criteria so as to identify a plurality of entries in a context query bid database that relate to the user context criteria, each of the plurality of entries in the context query bid database comprising bid context criteria, a bid amount, an identification of a bid advertiser, and an identification of a bid advertisement; selecting, via the network, a selected context query bid database entry from the plurality of entries in the context query bid database, such that the selected context query bid database entry has a highest bid amount; retrieving, via the network, a selected advertisement database entry from an advertisement database such that an identification of an advertiser and an identification of an advertisement on the selected advertisement database entry matches the identification of the bid advertiser and the identification of the bid advertisement on the selected context query bid database entry, the selected advertisement database entry additionally comprising an advertisement data object; generating, via the network, a dynamic webpage having content relating to the user context query; inserting, via the network, the data object into the dynamic webpage; transmitting, over the network, the dynamic webpage to the user; charging the advertiser a fee associated with the selected context query bid database entry when a user interface event relating to the dynamic webpage occurs. | 1. A method comprising the steps: receiving, over a network, by a computing device, a user context query from a user, wherein the user context query is formatted as a parameter of a universal resource locator (URL) and comprises user context criteria; formulating, via the network, a network data query based on the user context criteria so as to search, via the network, for user profile data, social network data, spatial data, temporal data, topical data and context query bid data that is available via the network and relates to the user context criteria so as to identify a plurality of entries in a context query bid database that relate to the user context criteria, each of the plurality of entries in the context query bid database comprising bid context criteria, a bid amount, an identification of a bid advertiser, and an identification of a bid advertisement; selecting, via the network, a selected context query bid database entry from the plurality of entries in the context query bid database, such that the selected context query bid database entry has a highest bid amount; retrieving, via the network, a selected advertisement database entry from an advertisement database such that an identification of an advertiser and an identification of an advertisement on the selected advertisement database entry matches the identification of the bid advertiser and the identification of the bid advertisement on the selected context query bid database entry, the selected advertisement database entry additionally comprising an advertisement data object; generating, via the network, a dynamic webpage having content relating to the user context query; inserting, via the network, the data object into the dynamic webpage; transmitting, over the network, the dynamic webpage to the user; charging the advertiser a fee associated with the selected context query bid database entry when a user interface event relating to the dynamic webpage occurs. 9. The method of claim 1 wherein the user interface event is selected from the list: click on the inserted data object, mouseover the inserted data object. | 0.807692 |
8,171,393 | 1 | 9 | 1. A method for producing and organizing electronically stored information, the method comprising: identifying, with one or more processors associated with one or more computer systems, a plurality of documents from the electronically stored information as satisfying similarity criteria; identifying, with the one or more processors associated with one or more computer systems, a first document in the plurality of documents as a pivot document for the plurality of documents that satisfy the similarity criteria, the pivot document being representative of the plurality of documents that satisfy the similarity criteria; generating, with the one or more processors associated with one or more computer systems, information configured to display a graphical user interface that enables users of the graphical user interface to associate review content with each pivot document in a collection of pivot documents visually represented in the graphical user interface; receiving, at the one or more computer systems, the review content that is indicative of textual information or one or more annotations provided by a user of the graphical user interface; associating, with the one or more processors associated with one or more computer systems, the review content indicative of the textual information or the one or more annotations with the pivot document; and propagating, with the one or more processors associated with one or more computer systems, the review content indicative of the textual information or the one or more annotations from the pivot document to one or more documents in the plurality of documents that satisfy the similarity criteria. | 1. A method for producing and organizing electronically stored information, the method comprising: identifying, with one or more processors associated with one or more computer systems, a plurality of documents from the electronically stored information as satisfying similarity criteria; identifying, with the one or more processors associated with one or more computer systems, a first document in the plurality of documents as a pivot document for the plurality of documents that satisfy the similarity criteria, the pivot document being representative of the plurality of documents that satisfy the similarity criteria; generating, with the one or more processors associated with one or more computer systems, information configured to display a graphical user interface that enables users of the graphical user interface to associate review content with each pivot document in a collection of pivot documents visually represented in the graphical user interface; receiving, at the one or more computer systems, the review content that is indicative of textual information or one or more annotations provided by a user of the graphical user interface; associating, with the one or more processors associated with one or more computer systems, the review content indicative of the textual information or the one or more annotations with the pivot document; and propagating, with the one or more processors associated with one or more computer systems, the review content indicative of the textual information or the one or more annotations from the pivot document to one or more documents in the plurality of documents that satisfy the similarity criteria. 9. The method of claim 1 further comprising organizing the plurality of documents in accordance with the review content, wherein the plurality of documents is adapted to be navigated in accordance to the review content. | 0.763499 |
9,122,637 | 4 | 6 | 4. The computer-implemented method of claim 1 , wherein enhancing the result of the search query comprises: identifying a file identifier within the result of the search query; determining that the action performed by the user was performed on a file corresponding to the file identifier; and contextualizing the file identifier within the result of the search query with a representation of the action. | 4. The computer-implemented method of claim 1 , wherein enhancing the result of the search query comprises: identifying a file identifier within the result of the search query; determining that the action performed by the user was performed on a file corresponding to the file identifier; and contextualizing the file identifier within the result of the search query with a representation of the action. 6. The computer-implemented method of claim 4 , wherein enhancing the result of the search query further comprises contextualizing the representation of the action within a sequence of actions performed by the user on the computing system that facilitated the action performed by the user on the file. | 0.5 |
7,603,626 | 22 | 29 | 22. In a computer network comprising a host computer system and a plurality of computers associated with a plurality of participants having access to the host system over the network, a method of creating a collaborative work, the collaborative work including multiple segments submitted by multiple participants, the method comprising: presenting to the multiple participants instructions for creating a segment; receiving from at least a subset of the multiple participants segments submitted over the network; presenting the submitted segments to a voting audience over the network to vote for a favored segment; and creating the collaborative work by selecting winning segments based on the votes submitted by the voting audience, wherein a promo, which is only authored by the author of the submitted segment and constitutes a subset of the submitted segment, is submitted with each submitted segment candidate, said promo being indicative of the content of the submitted segment candidate, wherein said promo is designed to generate interest amongst members of the voting audience to view the submitted segment in full, wherein said promo comprises an image or graphic, and a title, summary and excerpt, and wherein participation in said voting audience is open to the general public. | 22. In a computer network comprising a host computer system and a plurality of computers associated with a plurality of participants having access to the host system over the network, a method of creating a collaborative work, the collaborative work including multiple segments submitted by multiple participants, the method comprising: presenting to the multiple participants instructions for creating a segment; receiving from at least a subset of the multiple participants segments submitted over the network; presenting the submitted segments to a voting audience over the network to vote for a favored segment; and creating the collaborative work by selecting winning segments based on the votes submitted by the voting audience, wherein a promo, which is only authored by the author of the submitted segment and constitutes a subset of the submitted segment, is submitted with each submitted segment candidate, said promo being indicative of the content of the submitted segment candidate, wherein said promo is designed to generate interest amongst members of the voting audience to view the submitted segment in full, wherein said promo comprises an image or graphic, and a title, summary and excerpt, and wherein participation in said voting audience is open to the general public. 29. The method according to claim 22 , wherein the promo is prepared in advance and stored for submission with the segment. | 0.783451 |
7,836,148 | 1 | 11 | 1. A method of dynamically generating a display page, comprising: using a processor to obtain an object tree comprising a plurality of hierarchically organized objects, each object containing data and methods for processing a corresponding definitional element of said display page; using the processor to modify said object tree at runtime; and using the processor to invoke said methods of the objects comprising the object tree as modified to generate, dynamically at runtime, a plurality of definitional statements, each definitional statement being associated with one or more definitional elements of said display page; wherein said methods for processing a corresponding definitional element of said display page comprise computer instructions which when executed generate said plurality of definitional statements; wherein said definitional statements comprise hypertext markup language (HTML) or other statements usable by a browser or other software to render the display page; and wherein the display page generated using the objects comprising the object tree as modified reflects the modification made to said object tree at runtime. | 1. A method of dynamically generating a display page, comprising: using a processor to obtain an object tree comprising a plurality of hierarchically organized objects, each object containing data and methods for processing a corresponding definitional element of said display page; using the processor to modify said object tree at runtime; and using the processor to invoke said methods of the objects comprising the object tree as modified to generate, dynamically at runtime, a plurality of definitional statements, each definitional statement being associated with one or more definitional elements of said display page; wherein said methods for processing a corresponding definitional element of said display page comprise computer instructions which when executed generate said plurality of definitional statements; wherein said definitional statements comprise hypertext markup language (HTML) or other statements usable by a browser or other software to render the display page; and wherein the display page generated using the objects comprising the object tree as modified reflects the modification made to said object tree at runtime. 11. The method of claim 1 , wherein said object tree is modified based at least in part on an output provided at runtime by an application. | 0.820413 |
7,620,959 | 20 | 22 | 20. A system that performs reflection-based processing on parameters input to a command, the system comprising: a processor; and a memory, the memory being allocated for a plurality of computer-executable instructions which are loaded into the memory for execution by the processor, the computer-executable instructions performing a method comprising: receiving a parsable stream that includes an identifier associated with the command; retrieving definitional information based on the identifier that describes an expected parameter for the command, wherein the definitional information includes specific size limits for strings and for collections that can be processed; creating an object based on the definitional information; storing a parameter obtained from the parsable stream in the object in accordance with the definitional information associated with the expected parameter; applying a plurality of directives to the parsable stream, the plurality of directives comprising: a processing directive configured to manipulate the parameter before providing the object with the parameter to the command; a documentation directive that, when requested, generates textual information about the parameter, and provides a description of correct syntax when an invalid syntax is encountered; an interaction directive that determines a user interface for input of the expected parameter, wherein the interaction directive is applied if the expected parameter is not received in the parsable stream; and providing the object to the command, the object having a method invocable by the command, wherein: the definitional information and the plurality of directives are either derived from a reflection-based shell or extended by a developer of the command; the reflection-based shell provides one or more categories of directives and one or more directives under each category of directives; and the definition information and the plurality of directives associated with a first command are different from the definition information and the plurality of directives associated with a second command. | 20. A system that performs reflection-based processing on parameters input to a command, the system comprising: a processor; and a memory, the memory being allocated for a plurality of computer-executable instructions which are loaded into the memory for execution by the processor, the computer-executable instructions performing a method comprising: receiving a parsable stream that includes an identifier associated with the command; retrieving definitional information based on the identifier that describes an expected parameter for the command, wherein the definitional information includes specific size limits for strings and for collections that can be processed; creating an object based on the definitional information; storing a parameter obtained from the parsable stream in the object in accordance with the definitional information associated with the expected parameter; applying a plurality of directives to the parsable stream, the plurality of directives comprising: a processing directive configured to manipulate the parameter before providing the object with the parameter to the command; a documentation directive that, when requested, generates textual information about the parameter, and provides a description of correct syntax when an invalid syntax is encountered; an interaction directive that determines a user interface for input of the expected parameter, wherein the interaction directive is applied if the expected parameter is not received in the parsable stream; and providing the object to the command, the object having a method invocable by the command, wherein: the definitional information and the plurality of directives are either derived from a reflection-based shell or extended by a developer of the command; the reflection-based shell provides one or more categories of directives and one or more directives under each category of directives; and the definition information and the plurality of directives associated with a first command are different from the definition information and the plurality of directives associated with a second command. 22. The system of claim 20 , wherein retrieving definitional information includes identifying a class associated with the command. | 0.74 |
9,183,289 | 1 | 20 | 1. A method for document classification in a document creation application comprising: providing a security classification toolbar incorporated within an extendable mark-up language (XML) based word processing document of the document creation application, the classification toolbar providing a security classification selection input; populating the security classification selection input in the classification toolbar with a plurality of security classification selections based upon pre-defined security classification criteria, the pre-defined security classification criteria defining the plurality of security classification selections available to a user of the document creation application for applying classification to the word processing document; receiving a security classification selection input from the plurality of security classification selections, selected by the user, from the security classification selections populated in the toolbar; applying visual markings associated with the selected security classification input, the visual markings inserted within editable text portions in a body, a header or a footer of the XML based word processing document in the document creation application, the visual markings to provide visual identification within the editable text portion of the XML based word processing document that the XML based word processing document has been security classified when the security classification has been selected; and assigning XML security classification properties associated with the selected security classification input in document metadata embedded in the XML based word processing document identifying the selected security classification input. | 1. A method for document classification in a document creation application comprising: providing a security classification toolbar incorporated within an extendable mark-up language (XML) based word processing document of the document creation application, the classification toolbar providing a security classification selection input; populating the security classification selection input in the classification toolbar with a plurality of security classification selections based upon pre-defined security classification criteria, the pre-defined security classification criteria defining the plurality of security classification selections available to a user of the document creation application for applying classification to the word processing document; receiving a security classification selection input from the plurality of security classification selections, selected by the user, from the security classification selections populated in the toolbar; applying visual markings associated with the selected security classification input, the visual markings inserted within editable text portions in a body, a header or a footer of the XML based word processing document in the document creation application, the visual markings to provide visual identification within the editable text portion of the XML based word processing document that the XML based word processing document has been security classified when the security classification has been selected; and assigning XML security classification properties associated with the selected security classification input in document metadata embedded in the XML based word processing document identifying the selected security classification input. 20. The method of claims 1 wherein the received security classification selection input is validated against a defined administration policy before application of the visual markings, wherein the visual markings are not applied if the selection security classification input does not comply with the defined administration policy for the document or user. | 0.5 |
8,417,511 | 2 | 5 | 2. The method of claim 1 , wherein storing the data in the data structure comprises the data access service populating a data graph based on the data. | 2. The method of claim 1 , wherein storing the data in the data structure comprises the data access service populating a data graph based on the data. 5. The method of claim 2 , further comprising updating the at least one back-end data source based on the first command. | 0.516129 |
9,916,063 | 3 | 4 | 3. The method according to claim 2 , wherein the user prompt icon further includes an arrow. | 3. The method according to claim 2 , wherein the user prompt icon further includes an arrow. 4. The method according to claim 3 , further comprising: rotating the arrow from an initial direction as the pull-down gesture lengthens; wherein a down-ward moving of the text box, a presenting of the message and a rotating of the arrow are performed simultaneously as the pull-down gesture lengthens. | 0.5 |
9,886,949 | 1 | 16 | 1. A computer-implemented method comprising: receiving a first channel of audio data corresponding to an utterance and a second channel of audio data corresponding to the utterance; generating, using a trained recurrent neural network, (i) a first set of filter parameters for a first filter based on the first channel of audio data and the second channel of audio data and (ii) a second set of filter parameters for a second filter based on the first channel of audio data and the second channel of audio data; generating a single combined channel of audio data by combining (i) audio data of the first channel that has been filtered using the first filter and (ii) audio data of the second channel that has been filtered using the second filter; inputting the audio data for the single combined channel to a neural network trained as an acoustic model; and providing a transcription for the utterance that is determined based at least on output that the neural network provides in response to receiving the audio data for the single combined channel. | 1. A computer-implemented method comprising: receiving a first channel of audio data corresponding to an utterance and a second channel of audio data corresponding to the utterance; generating, using a trained recurrent neural network, (i) a first set of filter parameters for a first filter based on the first channel of audio data and the second channel of audio data and (ii) a second set of filter parameters for a second filter based on the first channel of audio data and the second channel of audio data; generating a single combined channel of audio data by combining (i) audio data of the first channel that has been filtered using the first filter and (ii) audio data of the second channel that has been filtered using the second filter; inputting the audio data for the single combined channel to a neural network trained as an acoustic model; and providing a transcription for the utterance that is determined based at least on output that the neural network provides in response to receiving the audio data for the single combined channel. 16. The method of claim 1 , comprising: convolving the audio data for the first channel with a first filter having the first set of filter parameters to generate first convolution outputs; convolving the audio data for the second channel with a second filter having the second set of filter parameters to generate second convolution outputs; and combining the first convolution outputs and the second convolution outputs. | 0.613051 |
8,453,128 | 1 | 6 | 1. A method for implementing a just-in-time compiler, comprising: obtaining a plurality of high-level code templates in a high-level programming language, wherein the high-level programming language is designed for compilation to an intermediate language capable of execution by a virtual machine, and wherein each high-level code template selected from the plurality of high-level code templates represents an instruction in the intermediate language; compiling the plurality of high-level code templates to native code to obtain a plurality of optimized native code templates, wherein compiling the plurality of high-level code templates is performed, prior to runtime, using an optimizing static compiler designed for runtime use with the virtual machine; marking a constant in an optimized native code template selected from the plurality of optimized native code templates with an annotation, wherein the annotation indicates that the constant requires modification by the just-in-time compiler at runtime; and implementing the just-in-time compiler using the plurality of optimized native code templates, wherein the just-in-time compiler is configured to substitute a copy of an optimized native code template selected from the plurality of optimized native code templates when a corresponding instruction in the intermediate language is encountered at runtime. | 1. A method for implementing a just-in-time compiler, comprising: obtaining a plurality of high-level code templates in a high-level programming language, wherein the high-level programming language is designed for compilation to an intermediate language capable of execution by a virtual machine, and wherein each high-level code template selected from the plurality of high-level code templates represents an instruction in the intermediate language; compiling the plurality of high-level code templates to native code to obtain a plurality of optimized native code templates, wherein compiling the plurality of high-level code templates is performed, prior to runtime, using an optimizing static compiler designed for runtime use with the virtual machine; marking a constant in an optimized native code template selected from the plurality of optimized native code templates with an annotation, wherein the annotation indicates that the constant requires modification by the just-in-time compiler at runtime; and implementing the just-in-time compiler using the plurality of optimized native code templates, wherein the just-in-time compiler is configured to substitute a copy of an optimized native code template selected from the plurality of optimized native code templates when a corresponding instruction in the intermediate language is encountered at runtime. 6. The method of claim 1 , wherein the plurality of optimized native code templates comprises different versions of templates for resolved classes and unresolved classes. | 0.819149 |
9,418,118 | 9 | 11 | 9. A system for producing search results, comprising: memory; one or more processors; and one or more programs, stored in the memory and executed by the one or more processors, the one or more programs including instructions for: receiving a search request associated with a user from the client system, the search request having one or more search terms; obtaining a user profile corresponding to the user, wherein the user profile is generated based in part on the user's prior computing activities, comprising one or more of browsing, searching, and messaging; obtaining search results for the search request; generating a personalized snippet for at least one of the search results in accordance with the obtained user profile, the snippet comprising a text portion of the search result chosen based on at least one or more search terms and one or more terms of the obtained user profile; and transmitting the search results and personalized snippet to the client system for display, wherein the generating includes identifying content associated with one of the search results, determining a profile similarity score for a term in the content, and generating a snippet based at least in part on the term when the profile similarity score is above a threshold, and wherein determining the profile similarity score includes identifying a respective term profile associated with the at least one term and determining a similarity between the profile information associated with the user profile and the respective term profile. | 9. A system for producing search results, comprising: memory; one or more processors; and one or more programs, stored in the memory and executed by the one or more processors, the one or more programs including instructions for: receiving a search request associated with a user from the client system, the search request having one or more search terms; obtaining a user profile corresponding to the user, wherein the user profile is generated based in part on the user's prior computing activities, comprising one or more of browsing, searching, and messaging; obtaining search results for the search request; generating a personalized snippet for at least one of the search results in accordance with the obtained user profile, the snippet comprising a text portion of the search result chosen based on at least one or more search terms and one or more terms of the obtained user profile; and transmitting the search results and personalized snippet to the client system for display, wherein the generating includes identifying content associated with one of the search results, determining a profile similarity score for a term in the content, and generating a snippet based at least in part on the term when the profile similarity score is above a threshold, and wherein determining the profile similarity score includes identifying a respective term profile associated with the at least one term and determining a similarity between the profile information associated with the user profile and the respective term profile. 11. The system of claim 9 , wherein each of the profile information associated with the user profile and the respective term profile are represented as a vector of a plurality of profile categories and respective weights, and wherein determining the similarity between the profile information associated with the user profile and the respective term profile comprises computing a distance between the vector corresponding to the profile information associated with the user profile and the vector corresponding to the respective term profile. | 0.5 |
7,627,862 | 8 | 9 | 8. The method of claim 7 , wherein generating the existential type with path abstraction that binds a type variable and a path variable further comprises: generating an approximated record type to be associated with a path type containing the type variable and the path variable. | 8. The method of claim 7 , wherein generating the existential type with path abstraction that binds a type variable and a path variable further comprises: generating an approximated record type to be associated with a path type containing the type variable and the path variable. 9. The method of claim 8 , wherein the approximated record type comprises representation of at least one displacement to a virtual superclass, and the representation includes the type variable and the path variable bound by the existential type for which the approximated record type was generated. | 0.5 |
7,966,332 | 9 | 10 | 9. A computer program product comprising a computer program stored in a memory and executed by a processor to perform operations for updating a distributed text index for parallel query processing by a number of nodes, the distributed text index having a set of node text indices indexing a set of documents, each node text index covering a subset of the documents, the operations of the computer program product comprising: for each node text index: calculating a local frequency measure for each term on the basis of a frequency of documents containing the term in the subset of the documents of the node; and calculating a quality measure for each node text index as a mean value of each difference for each term of the node of the local frequency measure of the term and a precalculated global frequency measure of the term, wherein the global frequency measure has been calculated before the updating of the text index, the global frequency measure of the term being expressive of a frequency of documents containing the term in the entire set of documents, wherein the quality measure indicates a sufficiency of the global frequency measure for each node text index. | 9. A computer program product comprising a computer program stored in a memory and executed by a processor to perform operations for updating a distributed text index for parallel query processing by a number of nodes, the distributed text index having a set of node text indices indexing a set of documents, each node text index covering a subset of the documents, the operations of the computer program product comprising: for each node text index: calculating a local frequency measure for each term on the basis of a frequency of documents containing the term in the subset of the documents of the node; and calculating a quality measure for each node text index as a mean value of each difference for each term of the node of the local frequency measure of the term and a precalculated global frequency measure of the term, wherein the global frequency measure has been calculated before the updating of the text index, the global frequency measure of the term being expressive of a frequency of documents containing the term in the entire set of documents, wherein the quality measure indicates a sufficiency of the global frequency measure for each node text index. 10. The computer program product of claim 9 , the calculation of the mean value being performed by a rank broker node, whereby the difference is communicated from one of the nodes to the rank broker node only if the difference surpasses the second predefined threshold. | 0.661209 |
8,666,724 | 4 | 5 | 4. A method performed by an apparatus that includes a translation memory storing previously translated sentences in association with source sentences, the method comprising: reading, from a storage unit of the apparatus, an original text including original sentences to be translated, a coefficient table defining coefficients associated with matching rates, and a delivery period table defining values indicating numbers of days and associated with total numbers of words; comparing the original sentences with the source sentences in the translation memory and thereby calculating the matching rates of the original sentences to the source sentences; counting a number of words in each of the original sentences after the matching rates are calculated and thereby obtaining numbers of words for the respective matching rates; adjusting the numbers of words of the respective matching rates by multiplying the numbers of words by the corresponding coefficients defined in the coefficient table; adding the adjusted numbers of words of the respective matching rates to obtain a total number of words of the original text; and obtaining one of the values corresponding to the obtained total number of words from the delivery period table and setting the one of the values as a delivery period for a translation of the original text. | 4. A method performed by an apparatus that includes a translation memory storing previously translated sentences in association with source sentences, the method comprising: reading, from a storage unit of the apparatus, an original text including original sentences to be translated, a coefficient table defining coefficients associated with matching rates, and a delivery period table defining values indicating numbers of days and associated with total numbers of words; comparing the original sentences with the source sentences in the translation memory and thereby calculating the matching rates of the original sentences to the source sentences; counting a number of words in each of the original sentences after the matching rates are calculated and thereby obtaining numbers of words for the respective matching rates; adjusting the numbers of words of the respective matching rates by multiplying the numbers of words by the corresponding coefficients defined in the coefficient table; adding the adjusted numbers of words of the respective matching rates to obtain a total number of words of the original text; and obtaining one of the values corresponding to the obtained total number of words from the delivery period table and setting the one of the values as a delivery period for a translation of the original text. 5. The method as claimed in claim 4 , further comprising: recording set delivery periods set for translations of original texts in association with actual delivery periods of the translations of the original texts; calculating an average of differences between the set delivery periods and the actual delivery periods; and if the average of differences is greater than a predetermined threshold, modifying the coefficients in the coefficient table. | 0.5 |
8,381,299 | 63 | 68 | 63. A system for outputting a dataset based upon anomaly detection, the system comprising: a digital processing device that: receives a training dataset having a plurality of n-grams that includes a first plurality of distinct training n-grams, wherein each of the first plurality of distinct training n-grams is a first size; computes a first plurality of appearance frequencies, wherein each of the first plurality of appearance frequencies corresponds to one of the first plurality of distinct training n-grams; receives an input dataset including first input n-grams, wherein each of the first input n-grams is the first size; defines a first window in the input dataset; identifies first matching n-grams by determining whether the first input n-grams in the first window correspond to one of the first plurality of distinct training n-grams; computes a first anomaly detection score for the input dataset using the first matching n-grams and the first plurality of appearance frequencies, wherein the first anomaly detection score is indicative of the presence of anomalous n-grams in the input dataset; and outputs the input dataset based on the first anomaly detection score. | 63. A system for outputting a dataset based upon anomaly detection, the system comprising: a digital processing device that: receives a training dataset having a plurality of n-grams that includes a first plurality of distinct training n-grams, wherein each of the first plurality of distinct training n-grams is a first size; computes a first plurality of appearance frequencies, wherein each of the first plurality of appearance frequencies corresponds to one of the first plurality of distinct training n-grams; receives an input dataset including first input n-grams, wherein each of the first input n-grams is the first size; defines a first window in the input dataset; identifies first matching n-grams by determining whether the first input n-grams in the first window correspond to one of the first plurality of distinct training n-grams; computes a first anomaly detection score for the input dataset using the first matching n-grams and the first plurality of appearance frequencies, wherein the first anomaly detection score is indicative of the presence of anomalous n-grams in the input dataset; and outputs the input dataset based on the first anomaly detection score. 68. The system of claim 63 , wherein the first plurality of distinct training n-grams comprises grouped n-grams and the first matching n-grams comprise grouped n-grams. | 0.809524 |
7,562,009 | 8 | 9 | 8. The system of claim 7 wherein a set of Attribute Descriptions can be used to define a set of standard Attribute names that Agents can expect to encounter. | 8. The system of claim 7 wherein a set of Attribute Descriptions can be used to define a set of standard Attribute names that Agents can expect to encounter. 9. The system of claim 8 wherein Agents collectively develop a common set of Attributes during operation. | 0.5 |
8,164,572 | 17 | 18 | 17. The image forming apparatus as claimed in claim 12 , wherein the predetermined format of the character string complies with Internet domain name standards. | 17. The image forming apparatus as claimed in claim 12 , wherein the predetermined format of the character string complies with Internet domain name standards. 18. The image forming apparatus as claimed in claim 17 , wherein the identifier of the language-specific entity is a country code top-level domain. | 0.5 |
8,407,625 | 1 | 5 | 1. A method of behavior recognition, comprising the steps of: storing a dynamic motion model representative of designated human behaviors in a form of differential equations having position and velocity components; sensing at least one human subject to be analyzed; representing the body of the subject as a plurality of connected, non-deformable links; determining the dynamic motion of the body, if any, by monitoring the position of the links in space as a function of time; using the dynamic motion to derive kinematic link relationships at specific body locations; comparing the kinematic link relationships to the position and velocity components; and, if a match is found, outputting the results of the comparison as the designated behavior. | 1. A method of behavior recognition, comprising the steps of: storing a dynamic motion model representative of designated human behaviors in a form of differential equations having position and velocity components; sensing at least one human subject to be analyzed; representing the body of the subject as a plurality of connected, non-deformable links; determining the dynamic motion of the body, if any, by monitoring the position of the links in space as a function of time; using the dynamic motion to derive kinematic link relationships at specific body locations; comparing the kinematic link relationships to the position and velocity components; and, if a match is found, outputting the results of the comparison as the designated behavior. 5. The method of claim 1 , wherein the designated behavior represents a threat. | 0.760606 |
8,538,184 | 1 | 3 | 1. In a document analysis system including a server, a method of enhancing electronic documents to improve automatic recognition and classification of the electronic documents, the method comprising: receiving, by the server, jobs containing job documents from a plurality of users, wherein each received job document is a binarized, one-bit-per-document-pixel image version of an original grayscale or color image source document; filtering, by the server, each page of the received binarized document to infer binarized background artifacts which result from the binarization of the original grayscale or color image source document and reside in the vicinity of binarized text and binarized image features in the page; distinguishing, by the server, the binarized text and binarized image features from the binarized background artifacts; extracting, by the server, the binarized text and binarized image features from the received binarized document; automatically recognizing and classifying, by the server, the received binarized document into a document category by using the extracted binarized text and binarized image features; organizing, by the server, each received job according to the document category of the corresponding job document; and removing, by the server, the binarized background artifacts, wherein the removal of the binarized background artifacts is performed after the binarized document image is inverted to white-on-black. | 1. In a document analysis system including a server, a method of enhancing electronic documents to improve automatic recognition and classification of the electronic documents, the method comprising: receiving, by the server, jobs containing job documents from a plurality of users, wherein each received job document is a binarized, one-bit-per-document-pixel image version of an original grayscale or color image source document; filtering, by the server, each page of the received binarized document to infer binarized background artifacts which result from the binarization of the original grayscale or color image source document and reside in the vicinity of binarized text and binarized image features in the page; distinguishing, by the server, the binarized text and binarized image features from the binarized background artifacts; extracting, by the server, the binarized text and binarized image features from the received binarized document; automatically recognizing and classifying, by the server, the received binarized document into a document category by using the extracted binarized text and binarized image features; organizing, by the server, each received job according to the document category of the corresponding job document; and removing, by the server, the binarized background artifacts, wherein the removal of the binarized background artifacts is performed after the binarized document image is inverted to white-on-black. 3. The method of claim 1 , wherein the removal of the binarized background artifacts is performed utilizing Gaussian filtering. | 0.746 |
9,852,233 | 1 | 4 | 1. A computer-implemented method comprising: accessing a plurality of social activity signals associated with a user and indicative of actions that are performed by the user and viewable by other users; receiving, from the user, user-entered text in a search field for a search engine; determining, by a machine having a memory and at least one processor, predicted queries based on the user-entered text and the plurality of social activity signals, each one of the predicted queries comprising predicted text and at least a portion of the user-entered text, the predicted text being absent from the user-entered text, the determining comprising: determining potential predicted queries based on the user-entered text; and assigning a corresponding predicted query value to each one of the potential predicted queries based on a determination for each potential predicted query of whether the potential predicted query corresponds to one of the social activity signals indicative of an action performed by the user that is viewable by other users, the assigning including: for each potential predicted query determined to correspond to one of the plurality of social activity signals indicative of an action performed by the user that is viewable by other users, determining a corresponding social activity type of the one of the plurality of social activity signals indicative of an action performed by the user that is viewable by other users; and for each potential predicted query determined to correspond to the one of the plurality of social activity signals, calculating the corresponding predicted query value based on a corresponding weight of the corresponding social activity type; and causing the predicted queries to be displayed, to the user, in an autocomplete user interface element of the search field. | 1. A computer-implemented method comprising: accessing a plurality of social activity signals associated with a user and indicative of actions that are performed by the user and viewable by other users; receiving, from the user, user-entered text in a search field for a search engine; determining, by a machine having a memory and at least one processor, predicted queries based on the user-entered text and the plurality of social activity signals, each one of the predicted queries comprising predicted text and at least a portion of the user-entered text, the predicted text being absent from the user-entered text, the determining comprising: determining potential predicted queries based on the user-entered text; and assigning a corresponding predicted query value to each one of the potential predicted queries based on a determination for each potential predicted query of whether the potential predicted query corresponds to one of the social activity signals indicative of an action performed by the user that is viewable by other users, the assigning including: for each potential predicted query determined to correspond to one of the plurality of social activity signals indicative of an action performed by the user that is viewable by other users, determining a corresponding social activity type of the one of the plurality of social activity signals indicative of an action performed by the user that is viewable by other users; and for each potential predicted query determined to correspond to the one of the plurality of social activity signals, calculating the corresponding predicted query value based on a corresponding weight of the corresponding social activity type; and causing the predicted queries to be displayed, to the user, in an autocomplete user interface element of the search field. 4. The computer-implemented method of claim 1 , further comprising storing a mapping of social activity types to weights, each social activity type having a corresponding weight. | 0.659004 |
5,493,608 | 24 | 26 | 24. An apparatus according to claim 23, further comprising means for decreasing said subsequent predetermined speaking rate to a rate lower than said initial predetermined speaking rate, said decreasing means comprising: (i) means for reducing said initial predetermined speaking rate by a predetermined fixed number of words per minute to arrive at said subsequent predetermined speaking rate; and (ii) means for limiting said subsequent predetermined speaking rate to a minimum speaking rate. | 24. An apparatus according to claim 23, further comprising means for decreasing said subsequent predetermined speaking rate to a rate lower than said initial predetermined speaking rate, said decreasing means comprising: (i) means for reducing said initial predetermined speaking rate by a predetermined fixed number of words per minute to arrive at said subsequent predetermined speaking rate; and (ii) means for limiting said subsequent predetermined speaking rate to a minimum speaking rate. 26. An apparatus according to claim 24, wherein said initial predetermined speaking rate is approximately 200 words per minute, said first predetermined amount of time is approximately 5 seconds, said second predetermined amount of time is approximately 5 seconds, said maximum speaking rate is approximately 300 words per minute, said first predetermined fixed number is approximately 20, said predetermined fixed number of words per minute is approximately 20, and said minimum speaking rate is approximately 100 words per minute. | 0.5 |
9,141,695 | 15 | 16 | 15. The system of claim 11 , wherein the one or more ranking criteria comprise a relevance of the plurality of audio microposts to the search query and/or one or more metadata tags associated with the plurality of audio microposts. | 15. The system of claim 11 , wherein the one or more ranking criteria comprise a relevance of the plurality of audio microposts to the search query and/or one or more metadata tags associated with the plurality of audio microposts. 16. The system of claim 15 , wherein the one or more metadata tags associated with the plurality of audio microposts comprise a creation time associated with the individual audio microposts of the plurality of audio microposts. | 0.5 |
8,209,162 | 16 | 18 | 16. A method comprising: dividing, based on a specified pre-determined length, a source text to be translated into multiple source text portions; uploading each respective one of the multiple source text portions to a back end processor before any attempt is made to translate the corresponding respective one of the multiple source text portions; identifying, using the back end processor, a corresponding translation knowledge subset for each of the multiple source text portions; downloading the corresponding translation knowledge subset for each of the multiple source text portions; and running a translation engine on a processor other than the back end processor to generate a translation for each of the multiple source text portions as a function of the corresponding translation knowledge subset for each of the multiple source text portions, wherein the back end processor splits each source text portion of the multiple source text portions into minimal independent translation segments, and wherein the back end processor identifies, for each minimal independent translation segment of the minimal independent translation segments, a corresponding knowledge segment, and wherein the back end processor assembles the identified knowledge segments corresponding to respective ones of the minimal independent translation segments to form the corresponding translation knowledge subset. | 16. A method comprising: dividing, based on a specified pre-determined length, a source text to be translated into multiple source text portions; uploading each respective one of the multiple source text portions to a back end processor before any attempt is made to translate the corresponding respective one of the multiple source text portions; identifying, using the back end processor, a corresponding translation knowledge subset for each of the multiple source text portions; downloading the corresponding translation knowledge subset for each of the multiple source text portions; and running a translation engine on a processor other than the back end processor to generate a translation for each of the multiple source text portions as a function of the corresponding translation knowledge subset for each of the multiple source text portions, wherein the back end processor splits each source text portion of the multiple source text portions into minimal independent translation segments, and wherein the back end processor identifies, for each minimal independent translation segment of the minimal independent translation segments, a corresponding knowledge segment, and wherein the back end processor assembles the identified knowledge segments corresponding to respective ones of the minimal independent translation segments to form the corresponding translation knowledge subset. 18. The method of claim 16 wherein the back end processor comprises a computing cluster. | 0.712418 |
8,370,328 | 1 | 5 | 1. A method for creating an electronic database of disambiguated entity mentions from a corpus of electronic documents using a microprocessor, the method comprising: (a) executing on the microprocessor a data harvesting module to automatically extract entity mentions from the electronic documents in the corpus and parse the entity mentions into mention objects; (b) executing on the microprocessor a mention group creation module to create one or more mention groups by automatically grouping the mention objects together according to a distinguishing attribute common to a given class of mention objects; (c) selecting a mention group from the one or more mention groups for comparison processing; (d) executing on the microprocessor a collection of comparison modules that automatically (i) compares every mention object in the selected mention group with every other mention object in the selected mention group to produce a collection of comparison algorithm scores for every pair of mention objects in the selected mention group, and (ii) generates an overall confidence score for every pair of mention objects in the selected mention group based on the collection of comparison algorithm scores for said every pair of mention objects; (e) executing on the microprocessor an entity object creation module to create one or more new entity objects for the selected mention group by automatically (i) grouping together mention objects with other mention objects, based on the confidence scores of each pair of mention objects and a specified confidence threshold, wherein pairs of mention objects having a confidence score greater than or equal to the specified threshold are assigned to the same new entity object, and (ii) merging previously-created entity objects with other previously-created entity objects, based on the confidence scores of each pair of entity objects, and a specified confidence threshold, wherein pairs of entity objects having a confidence score greater than or equal to the specified threshold are assigned to the same new entity object; (f) storing said one or more new entity objects in the electronic database of disambiguated entity mentions; and (g) repeating steps (c) through (f) above until all of the one or more mention groups have been comparison processed. | 1. A method for creating an electronic database of disambiguated entity mentions from a corpus of electronic documents using a microprocessor, the method comprising: (a) executing on the microprocessor a data harvesting module to automatically extract entity mentions from the electronic documents in the corpus and parse the entity mentions into mention objects; (b) executing on the microprocessor a mention group creation module to create one or more mention groups by automatically grouping the mention objects together according to a distinguishing attribute common to a given class of mention objects; (c) selecting a mention group from the one or more mention groups for comparison processing; (d) executing on the microprocessor a collection of comparison modules that automatically (i) compares every mention object in the selected mention group with every other mention object in the selected mention group to produce a collection of comparison algorithm scores for every pair of mention objects in the selected mention group, and (ii) generates an overall confidence score for every pair of mention objects in the selected mention group based on the collection of comparison algorithm scores for said every pair of mention objects; (e) executing on the microprocessor an entity object creation module to create one or more new entity objects for the selected mention group by automatically (i) grouping together mention objects with other mention objects, based on the confidence scores of each pair of mention objects and a specified confidence threshold, wherein pairs of mention objects having a confidence score greater than or equal to the specified threshold are assigned to the same new entity object, and (ii) merging previously-created entity objects with other previously-created entity objects, based on the confidence scores of each pair of entity objects, and a specified confidence threshold, wherein pairs of entity objects having a confidence score greater than or equal to the specified threshold are assigned to the same new entity object; (f) storing said one or more new entity objects in the electronic database of disambiguated entity mentions; and (g) repeating steps (c) through (f) above until all of the one or more mention groups have been comparison processed. 5. The method of claim 1 , further comprising executing program instructions on the microprocessor to cause the microprocessor to assign a mention object having a name that is slightly misspelled to the same mention group as another mention object having a correctly-spelled version of said name. | 0.745704 |
9,762,387 | 7 | 14 | 7. An apparatus, comprising: at least one processor; and at least one non-transitory computer-readable medium including computer program code which when executed by the at least one processor causes the apparatus to at least: generate at least one query identifier based on a first set of keywords, wherein each query identifier comprises a first hash of an identified keyword from the first set of keywords; transmit, to at least one other apparatus, a query comprising the at least one query identifier, wherein the query is transmitted via an ad hoc network; receive, from the other apparatus, a response comprising at least one match identifier and an encrypted message, wherein each match identifier comprises a second hash of the identified keyword that the other apparatus is able to identify based on the at least one query identifier; determine one or more keywords from the first set of keywords that corresponds to the at least one match identifier; generate an encryption key based on the one or more keywords identified by the at least one match identifier; and decrypt the encrypted message using the encryption key. | 7. An apparatus, comprising: at least one processor; and at least one non-transitory computer-readable medium including computer program code which when executed by the at least one processor causes the apparatus to at least: generate at least one query identifier based on a first set of keywords, wherein each query identifier comprises a first hash of an identified keyword from the first set of keywords; transmit, to at least one other apparatus, a query comprising the at least one query identifier, wherein the query is transmitted via an ad hoc network; receive, from the other apparatus, a response comprising at least one match identifier and an encrypted message, wherein each match identifier comprises a second hash of the identified keyword that the other apparatus is able to identify based on the at least one query identifier; determine one or more keywords from the first set of keywords that corresponds to the at least one match identifier; generate an encryption key based on the one or more keywords identified by the at least one match identifier; and decrypt the encrypted message using the encryption key. 14. The apparatus of claim 7 , wherein the determining of the one or more keywords from the first set of keywords causes the apparatus to at least: locate, in a lookup table, the at least one match identifier, wherein the look up table comprises a plurality of match identifiers and a corresponding plurality of keywords. | 0.71743 |
8,655,654 | 3 | 4 | 3. The method of claim 1 , further comprising associating the conversation path with auxiliary data. | 3. The method of claim 1 , further comprising associating the conversation path with auxiliary data. 4. The method of claim 3 , wherein the auxiliary data is media data. | 0.736434 |
8,176,119 | 19 | 24 | 19. A system comprising: a first computer; a content viewer operably installed on the first computer; a content object accessible to the first computer via the content viewer; a second computer different from the first computer; a content viewer readable code component accessible to the second computer, the content viewer readable code component including dynamically selectable characteristics, the content viewer readable code component being useable with a plurality of different content viewers; combining the content viewer readable code component into the content object; serving the combined content object to a user system; and changing a portion of the content object upon execution of the content viewer readable code component at the user system without altering the remainder of the content object. | 19. A system comprising: a first computer; a content viewer operably installed on the first computer; a content object accessible to the first computer via the content viewer; a second computer different from the first computer; a content viewer readable code component accessible to the second computer, the content viewer readable code component including dynamically selectable characteristics, the content viewer readable code component being useable with a plurality of different content viewers; combining the content viewer readable code component into the content object; serving the combined content object to a user system; and changing a portion of the content object upon execution of the content viewer readable code component at the user system without altering the remainder of the content object. 24. The system as claimed in claim 19 wherein the dynamically selectable characteristics of the content viewer readable code component include at least one characteristic from the group: shape, color, text, and category. | 0.5 |
8,527,369 | 15 | 18 | 15. A method for supplementing customer-facing listings in an item catalog, the method comprising: receiving, at one or more computers, a selection of a feature of the item catalog; generating, by the one or more computers, a supplemental feed template from a second set of semantics maintained in feed metadata, the second set of semantics corresponding to an attribute of items in the item catalog, the attribute related to the selected feature of the item catalog, the supplemental feed template comprising header information identifying the selected feature of the item catalog; receiving, at the one or more computers, supplemental feed data from a seller comprising a value for the attribute of an item listed in the item catalog, the supplemental feed data created from the supplemental feed template; validating, by the one or more computers, the supplemental feed data based on the second set of semantics corresponding to the attribute, wherein the feed metadata further comprises a first set of semantics describing a transformation of seller feed data to customer-facing data for inclusion in the item catalog; transforming, by the one or more computers, the supplemental feed data to supplemental listings data for the item based on the second set of semantics corresponding to the; and merging, by the one or more computers, the supplemental listings data into the item catalog containing customer-facing listings data corresponding to the item loaded from seller feed data based on the first set of semantics, wherein the value for the attribute in the supplemental listings data takes precedence over a value for the attribute in the item catalog loaded from the seller feed data. | 15. A method for supplementing customer-facing listings in an item catalog, the method comprising: receiving, at one or more computers, a selection of a feature of the item catalog; generating, by the one or more computers, a supplemental feed template from a second set of semantics maintained in feed metadata, the second set of semantics corresponding to an attribute of items in the item catalog, the attribute related to the selected feature of the item catalog, the supplemental feed template comprising header information identifying the selected feature of the item catalog; receiving, at the one or more computers, supplemental feed data from a seller comprising a value for the attribute of an item listed in the item catalog, the supplemental feed data created from the supplemental feed template; validating, by the one or more computers, the supplemental feed data based on the second set of semantics corresponding to the attribute, wherein the feed metadata further comprises a first set of semantics describing a transformation of seller feed data to customer-facing data for inclusion in the item catalog; transforming, by the one or more computers, the supplemental feed data to supplemental listings data for the item based on the second set of semantics corresponding to the; and merging, by the one or more computers, the supplemental listings data into the item catalog containing customer-facing listings data corresponding to the item loaded from seller feed data based on the first set of semantics, wherein the value for the attribute in the supplemental listings data takes precedence over a value for the attribute in the item catalog loaded from the seller feed data. 18. The method of claim 15 , wherein the supplemental feed template comprises a flat file having rows corresponding to one or more distinct items and columns corresponding to one or more seller-facing fields corresponding to the attribute related to the selected feature of the item catalog. | 0.5 |
8,145,632 | 59 | 62 | 59. A graphical user interface on a computer display, comprising: one or more document links, wherein each document link has a corresponding document satisfying user-specified search keywords in accordance with a first set of predefined criteria, one or more chunks within the corresponding document identified by applying a second set of predefined criteria to the corresponding document, each identified chunk has an associated chunk link and includes each of the user-specified search keywords, wherein the user-specified search keywords are highlighted in the chunk in a visually distinguishable manner, wherein: in response to a user selection of a chunk's chunk link, the corresponding document is concurrently displayed with the one or more document links in a window on the computer display, wherein at least a portion of the chunk in the corresponding document is highlighted in the window; and the first set of predefined criteria requires that all the search keywords be found within an identified document, and the second set of predefined criteria requires that all the search keywords be found within an identified chunk. | 59. A graphical user interface on a computer display, comprising: one or more document links, wherein each document link has a corresponding document satisfying user-specified search keywords in accordance with a first set of predefined criteria, one or more chunks within the corresponding document identified by applying a second set of predefined criteria to the corresponding document, each identified chunk has an associated chunk link and includes each of the user-specified search keywords, wherein the user-specified search keywords are highlighted in the chunk in a visually distinguishable manner, wherein: in response to a user selection of a chunk's chunk link, the corresponding document is concurrently displayed with the one or more document links in a window on the computer display, wherein at least a portion of the chunk in the corresponding document is highlighted in the window; and the first set of predefined criteria requires that all the search keywords be found within an identified document, and the second set of predefined criteria requires that all the search keywords be found within an identified chunk. 62. The graphical user interface of claim 59 , wherein one or more chunks associated with a respective document link are displayed in an order consistent with their relative relevancy to the user-specified search keywords. | 0.797814 |
9,864,590 | 19 | 20 | 19. The non-transitory machine-readable medium of claim 12 , wherein constructing the traversable representation comprises: parsing code of the program to identify control structures that govern alternative statements; and converting each control structure into a sub-graph with conditioning function and with alternative statements represented as separate paths in the graph that later merge and loop. | 19. The non-transitory machine-readable medium of claim 12 , wherein constructing the traversable representation comprises: parsing code of the program to identify control structures that govern alternative statements; and converting each control structure into a sub-graph with conditioning function and with alternative statements represented as separate paths in the graph that later merge and loop. 20. The non-transitory machine-readable medium of claim 19 , further comprising: linking the sub-graph structure to the functional dataflow graph by linking input and output interfaces of the sub-graph structure to surrounding nodes of the functional dataflow graph. | 0.5 |
9,804,581 | 7 | 8 | 7. A data processing system comprising: a processor; and an accessible memory, the data processing system particularly configured to: parse a functional model; receive a functional operator for a function within a simulation component of the functional model; receive a structural template of the functional operator from a functional operator structural template library; map a plurality of functions according to the structural template of the functional operator to update the simulation component; and generate a simulation model with the updated simulation component, wherein the functional operator comprises a functional operator type and a functional operator parameter, wherein to map at least one of the plurality of functions comprises either: simulating an amount of the functions equal to the functional operator parameter mapped using the structural template for a series functional operator, based on the functional operator type of the functional operator received being a series functional operator; or simulating an amount of the functions equal to the functional operator parameter mapped using the structural template for a parallel functional operator, based on the functional operator type of the functional operator received being a parallel functional operator. | 7. A data processing system comprising: a processor; and an accessible memory, the data processing system particularly configured to: parse a functional model; receive a functional operator for a function within a simulation component of the functional model; receive a structural template of the functional operator from a functional operator structural template library; map a plurality of functions according to the structural template of the functional operator to update the simulation component; and generate a simulation model with the updated simulation component, wherein the functional operator comprises a functional operator type and a functional operator parameter, wherein to map at least one of the plurality of functions comprises either: simulating an amount of the functions equal to the functional operator parameter mapped using the structural template for a series functional operator, based on the functional operator type of the functional operator received being a series functional operator; or simulating an amount of the functions equal to the functional operator parameter mapped using the structural template for a parallel functional operator, based on the functional operator type of the functional operator received being a parallel functional operator. 8. The data processing system of claim 7 further comprising: create a functional decomposition on the simulation component for use in a higher fidelity simulation model. | 0.769126 |
8,478,623 | 9 | 12 | 9. A computer system comprising: a central processing unit (CPU), a computer readable memory, and a computer readable storage media; first program instructions to select a candidate industry type to describe a customer's operations, wherein the candidate industry type has specific information environment requirements; second program instructions to select a set of information environment components that is specific to the candidate industry type; third program instructions to identify inherent constraints on the set of information environment components, wherein the inherent constraints are for the candidate industry type and the set of information environment components; fourth program instructions to recursively optimize the set of information environment components to create a set of optimized information environment components that meets the inherent constraints and key performance indicators (KPIs) of the candidate industry type; fifth program instructions to define a hierarchy of KPIs for the candidate industry type; sixth program instructions to predict access patterns to the optimized information environment components based on the hierarchy of KPIs of the candidate industry type, wherein higher ranked KPIs take precedence over relatively lower ranked KPIs in determining access priorities for accessing components in the set of optimized information environment components, and seventh program instructions to construct and deploy a physical information environment that comprises the set of optimized information environment components; and wherein the first, second, third, fourth, fifth, sixth, and seventh program instructions are stored on the computer readable storage media for execution by the CPU via the computer readable memory. | 9. A computer system comprising: a central processing unit (CPU), a computer readable memory, and a computer readable storage media; first program instructions to select a candidate industry type to describe a customer's operations, wherein the candidate industry type has specific information environment requirements; second program instructions to select a set of information environment components that is specific to the candidate industry type; third program instructions to identify inherent constraints on the set of information environment components, wherein the inherent constraints are for the candidate industry type and the set of information environment components; fourth program instructions to recursively optimize the set of information environment components to create a set of optimized information environment components that meets the inherent constraints and key performance indicators (KPIs) of the candidate industry type; fifth program instructions to define a hierarchy of KPIs for the candidate industry type; sixth program instructions to predict access patterns to the optimized information environment components based on the hierarchy of KPIs of the candidate industry type, wherein higher ranked KPIs take precedence over relatively lower ranked KPIs in determining access priorities for accessing components in the set of optimized information environment components, and seventh program instructions to construct and deploy a physical information environment that comprises the set of optimized information environment components; and wherein the first, second, third, fourth, fifth, sixth, and seventh program instructions are stored on the computer readable storage media for execution by the CPU via the computer readable memory. 12. The computer system of claim 9 , wherein the inherent constraints for the candidate industry type comprise a turnaround speed requirement, wherein the turnaround speed requirement defines how quickly a request for data must be returned for that candidate industry type. | 0.519366 |
8,656,374 | 1 | 9 | 1. A method for implementation by one or more data processors comprising: receiving, by at least one data processor, a first data record schema and a second data record schema, wherein the first data record schema differs from the second data record schema in at least one of endianness, character encoding and localization; converting, by at least one data processor, the first data record schema into a first representation and the second data record schema into a second representation, wherein the first representation and the second representation have a common encoding or syntax that is independent of the at least one of endianness, character encoding and localization; creating, by at least one data processor, a standardized data record schema based on the first representation and the second representation; receiving, by at least one data processor, a parameterized location of a data record corresponding to a Common Business Oriented Language (COBOL) copybook and information about the data record format; and processing, by at least one data processor, the data record based on the standardized data record schema; wherein the standardized data record schema is created by (i) generating a hierarchical structure of field definitions for the standardized data record schema corresponding to the COBOL copybook, and (ii) attaching information extracted from a COBOL data definition contained in the COBOL copybook to a corresponding field definition contained in the standardized data record schema. | 1. A method for implementation by one or more data processors comprising: receiving, by at least one data processor, a first data record schema and a second data record schema, wherein the first data record schema differs from the second data record schema in at least one of endianness, character encoding and localization; converting, by at least one data processor, the first data record schema into a first representation and the second data record schema into a second representation, wherein the first representation and the second representation have a common encoding or syntax that is independent of the at least one of endianness, character encoding and localization; creating, by at least one data processor, a standardized data record schema based on the first representation and the second representation; receiving, by at least one data processor, a parameterized location of a data record corresponding to a Common Business Oriented Language (COBOL) copybook and information about the data record format; and processing, by at least one data processor, the data record based on the standardized data record schema; wherein the standardized data record schema is created by (i) generating a hierarchical structure of field definitions for the standardized data record schema corresponding to the COBOL copybook, and (ii) attaching information extracted from a COBOL data definition contained in the COBOL copybook to a corresponding field definition contained in the standardized data record schema. 9. The method of claim 1 , wherein the parsing comprises determining that the COBOL copybook contains a nested data record schema, and wherein the creating comprises providing an option to select a type of the standardized data record schema from an expanded data record schema and a collapsed data record schema. | 0.5 |
8,412,514 | 13 | 20 | 13. A system for responding to an inquiry comprising: a web miner configured to retrieve a first web page and a second web page, the first web page associated with a first layout and the second web page associated with a second layout and to compare the first layout and the second layout and to generate stored question-answer pairs based on the comparing the first layout and the second layout; an interface configured to receive the inquiry via a network; provide a plurality of responses receive a selection of a particular response from among the plurality of responses; a database comprising stored question-answer pairs; a feature extractor module for extracting features of the stored question-answer pairs from the database and labeling each question-answer pair with meta-level features to define relationships among stored question-answer pairs, the meta-level features of a particular stored question-answer pair based on the meta-level features of a previous related stored question-answer pair and a first related stored question-answer pair; and a processor configured to: clarify the inquiry based on follow-up procedures including asking follow-up questions and analyzing the inquiry to determine the plurality of responses to the inquiry based on the stored question answer pairs, the relationships among the stored question answer pairs, and the follow-up procedures; and store a measure of the eligibility of the particular response to be a response to the inquiry based on the selection. | 13. A system for responding to an inquiry comprising: a web miner configured to retrieve a first web page and a second web page, the first web page associated with a first layout and the second web page associated with a second layout and to compare the first layout and the second layout and to generate stored question-answer pairs based on the comparing the first layout and the second layout; an interface configured to receive the inquiry via a network; provide a plurality of responses receive a selection of a particular response from among the plurality of responses; a database comprising stored question-answer pairs; a feature extractor module for extracting features of the stored question-answer pairs from the database and labeling each question-answer pair with meta-level features to define relationships among stored question-answer pairs, the meta-level features of a particular stored question-answer pair based on the meta-level features of a previous related stored question-answer pair and a first related stored question-answer pair; and a processor configured to: clarify the inquiry based on follow-up procedures including asking follow-up questions and analyzing the inquiry to determine the plurality of responses to the inquiry based on the stored question answer pairs, the relationships among the stored question answer pairs, and the follow-up procedures; and store a measure of the eligibility of the particular response to be a response to the inquiry based on the selection. 20. The system of claim 13 wherein the web miner detects question-answer pair templates on a web page. | 0.808271 |
7,974,938 | 11 | 19 | 11. A system for storing and using a set of observations of real-world events and actions, the system comprising: a cluster generator for generating clusters based on the set of observations, each cluster representing correlations between events and actions in a subset of observations in the set of observations; and a graph processor coupled to the cluster generator for processing the clusters into a first linked graph based on a set of rules to identify correlations between the events and actions, the first linked graph tagged to indicate the correlations between the events and the actions; wherein the cluster generator presupposes an event as reversible unless an observation contradicting reversibility of the event is obtained, and wherein the graph processor tags an edge associated with the irreversible event as having uncertain correlations with actions that are irreversible. | 11. A system for storing and using a set of observations of real-world events and actions, the system comprising: a cluster generator for generating clusters based on the set of observations, each cluster representing correlations between events and actions in a subset of observations in the set of observations; and a graph processor coupled to the cluster generator for processing the clusters into a first linked graph based on a set of rules to identify correlations between the events and actions, the first linked graph tagged to indicate the correlations between the events and the actions; wherein the cluster generator presupposes an event as reversible unless an observation contradicting reversibility of the event is obtained, and wherein the graph processor tags an edge associated with the irreversible event as having uncertain correlations with actions that are irreversible. 19. The system of claim 11 , wherein the graph processor generates a second linked graph for another set of observations by modifying or expanding the first linked graph. | 0.823651 |
4,541,069 | 5 | 6 | 5. The device of claim 1, wherein said memory means stores the words of the first language in groups based on the first letter of such words. | 5. The device of claim 1, wherein said memory means stores the words of the first language in groups based on the first letter of such words. 6. The device of claim 5, further comprising means responsive to entry of the first word by the input means for providing an address of the initial word of the group of words, the first letter of which is the same as that of the entered first word. | 0.5 |
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