patent_num
int64 3.93M
10.2M
| claim_num1
int64 1
519
| claim_num2
int64 2
520
| sentence1
stringlengths 40
15.9k
| sentence2
stringlengths 88
20k
| label
float64 0.5
0.99
|
|---|---|---|---|---|---|
9,864,973 | 11 | 12 | 11. A method for collaboratively editing a media project using a plurality of different media processing applications, the method comprising: in response to a first user using a first media processing application of the plurality of different media processing applications to edit a first element of the media project, generating and storing in shared storage: metadata native to the first media processing application of the plurality of different media processing applications corresponding to a current state of the first element of the media project represented in a data model of the first media processing application; data describing the edit to the first element of the media project performed by the first user using the first media processing application; and an updated canonical representation of the current state of the media project wherein the updated canonical representation is based on a subset of media project data models of the plurality of different media processing applications; and in response to a second user using a second media processing application of the plurality of different media processing applications to edit a second element of the media project, generating and storing in the shared storage: metadata native to the second media processing application corresponding to a current state of the second element of the media project represented in a data model of the second media processing application; data describing the edit to the second element of the media project performed by the second user using the second media processing application; and a further updated canonical representation of the current state of the media project, wherein the further updated canonical representation is based on an updated subset of media project data models of the plurality of different media processing applications; and wherein each of the plurality of different media processing applications is able to read the updated and the further updated canonical representations of the current state of the media project. | 11. A method for collaboratively editing a media project using a plurality of different media processing applications, the method comprising: in response to a first user using a first media processing application of the plurality of different media processing applications to edit a first element of the media project, generating and storing in shared storage: metadata native to the first media processing application of the plurality of different media processing applications corresponding to a current state of the first element of the media project represented in a data model of the first media processing application; data describing the edit to the first element of the media project performed by the first user using the first media processing application; and an updated canonical representation of the current state of the media project wherein the updated canonical representation is based on a subset of media project data models of the plurality of different media processing applications; and in response to a second user using a second media processing application of the plurality of different media processing applications to edit a second element of the media project, generating and storing in the shared storage: metadata native to the second media processing application corresponding to a current state of the second element of the media project represented in a data model of the second media processing application; data describing the edit to the second element of the media project performed by the second user using the second media processing application; and a further updated canonical representation of the current state of the media project, wherein the further updated canonical representation is based on an updated subset of media project data models of the plurality of different media processing applications; and wherein each of the plurality of different media processing applications is able to read the updated and the further updated canonical representations of the current state of the media project. 12. The method of claim 11 , wherein the shared storage further includes media files containing source media upon which the media project is based. | 0.762136 |
8,630,841 | 1 | 5 | 1. A method of verifying a compound word, comprising: receiving an input signal indicative of a textual input; accessing a rule including multiple segment identifiers and an indication of a relationship between the multiple segment identifiers; generating a rule structure that is based at least in part on the rule, the rule structure including multiple nodes that represent the multiple segment identifiers and are connected by links that represent the relationship between the multiple segment identifiers; accessing a lexical entry including an indication of the multiple segment identifiers; utilizing a computer processor that is a component of a computer to apply the rule structure to the textual input to determine whether the textual input is a valid compound word that includes segments included in the lexical entry and indirectly identified by the multiple segment identifiers, wherein applying the rule structure comprises traversing a traversal path in the rule structure and identifying segments corresponding to the nodes along the traversal path, the segments being identified based on the segment identifiers represented by each node; and providing an output signal indicative of whether the textual input is a compound word. | 1. A method of verifying a compound word, comprising: receiving an input signal indicative of a textual input; accessing a rule including multiple segment identifiers and an indication of a relationship between the multiple segment identifiers; generating a rule structure that is based at least in part on the rule, the rule structure including multiple nodes that represent the multiple segment identifiers and are connected by links that represent the relationship between the multiple segment identifiers; accessing a lexical entry including an indication of the multiple segment identifiers; utilizing a computer processor that is a component of a computer to apply the rule structure to the textual input to determine whether the textual input is a valid compound word that includes segments included in the lexical entry and indirectly identified by the multiple segment identifiers, wherein applying the rule structure comprises traversing a traversal path in the rule structure and identifying segments corresponding to the nodes along the traversal path, the segments being identified based on the segment identifiers represented by each node; and providing an output signal indicative of whether the textual input is a compound word. 5. The method of claim 1 , and further comprising: accessing a plurality of lexical entries from the lexical data store, each of the lexical entries including a segment field and a metadata field. | 0.811538 |
7,797,274 | 1 | 7 | 1. A method for providing a collaborative editing model for online content, comprising: receiving user-created online content comprising a first public-facing version of the content; receiving a set of suggested edits to the online content from a plurality of users, where each suggested edit in the set relates to the first public-facing version; providing the set of suggested edits to an authorized editor of the online content, wherein the editor is visually notified of differences between the first public-facing version of the content and the suggested edits and is notified of conflicts existing between two or more suggested edits where a conflict exists if a first suggested edit cannot co-exist with a second suggested edit; receiving input from the editor resolving conflicts and accepting or rejecting suggested edits in the set; modifying the first public-facing version of the content based on the set of suggested edits and the input from the editor to generate a second public-facing version of the content; carrying over suggested edits from the set that were not accepted nor rejected and that are not in conflict with the second public-facing version of the content for future editor input accepting or rejecting the carried over suggested edits in relation to the second public-facing version of the content; providing a marked-up view of the online content showing differences between the first public-facing version of the online content and a previous public-facing version of the online content; receiving comments appended to one or more of the suggested edits; providing the comments to the editor; and recording a reply from the editor to at least one of the comments. | 1. A method for providing a collaborative editing model for online content, comprising: receiving user-created online content comprising a first public-facing version of the content; receiving a set of suggested edits to the online content from a plurality of users, where each suggested edit in the set relates to the first public-facing version; providing the set of suggested edits to an authorized editor of the online content, wherein the editor is visually notified of differences between the first public-facing version of the content and the suggested edits and is notified of conflicts existing between two or more suggested edits where a conflict exists if a first suggested edit cannot co-exist with a second suggested edit; receiving input from the editor resolving conflicts and accepting or rejecting suggested edits in the set; modifying the first public-facing version of the content based on the set of suggested edits and the input from the editor to generate a second public-facing version of the content; carrying over suggested edits from the set that were not accepted nor rejected and that are not in conflict with the second public-facing version of the content for future editor input accepting or rejecting the carried over suggested edits in relation to the second public-facing version of the content; providing a marked-up view of the online content showing differences between the first public-facing version of the online content and a previous public-facing version of the online content; receiving comments appended to one or more of the suggested edits; providing the comments to the editor; and recording a reply from the editor to at least one of the comments. 7. The method of claim 1 , further comprising: detecting a conflict between a first suggested edit and a second suggested edit; notifying the editor of the conflict; and receiving additional input from the editor providing a resolution to the conflict. | 0.5625 |
9,237,297 | 1 | 6 | 1. A method of reproducing a story at a user terminal, the method comprising the steps of: (A) giving a user access to an electronic copy of a video story, said video story having a plurality of parallel video streams which correspond to a plurality of parallel story timelines to said video story, respectively, wherein each of said parallel video streams includes a respective plurality of video segments which are located in a storage medium, and are in chronological order, said plurality of video segments are represented by a plurality of icons, each along one of a plurality of visual timelines on an on-screen interface, ones of said icons each along a respective one of said plurality of visual timelines which corresponds to a respective one of the parallel video streams; (B) receiving from the user a user requested first selection of a first one of the video segments from one of the parallel video streams; (C) reproducing packets of video data corresponding to the user requested first selection of the first one of the video/story segments to play the user requested first selection at the user terminal; (D) receiving from the user a user requested second selection of a second one of the video segments from another of said parallel video streams, wherein the second one of the video segments is not limited to being chronologically after and chronologically at the same time as the one of the video segments, and wherein each of said parallel video streams corresponds to a respective audio sequence prior to step (A); (E) reproducing packets of video data corresponding to the user requested second one of the video segments to play the user requested second selection at the user terminal, and indicating on the on-screen interface which of the video segments have been reproduced; (F) reproducing packets of video data corresponding to a third one of the video segments from any of the parallel video streams to play the third one of the video story segments at the user terminal; wherein said third one of said video segments is later in chronological time in said video story than said first one and said second one of said video segments, and steps (D) and (E) occur after steps (B) and (C) and before step (F); and wherein said first one of said video segments with its corresponding audio sequence from said one of said parallel video streams and another of said video segments with its corresponding audio sequence from said another of said parallel video streams occur at the same time in said video story and are respectively different in both video and audio content. | 1. A method of reproducing a story at a user terminal, the method comprising the steps of: (A) giving a user access to an electronic copy of a video story, said video story having a plurality of parallel video streams which correspond to a plurality of parallel story timelines to said video story, respectively, wherein each of said parallel video streams includes a respective plurality of video segments which are located in a storage medium, and are in chronological order, said plurality of video segments are represented by a plurality of icons, each along one of a plurality of visual timelines on an on-screen interface, ones of said icons each along a respective one of said plurality of visual timelines which corresponds to a respective one of the parallel video streams; (B) receiving from the user a user requested first selection of a first one of the video segments from one of the parallel video streams; (C) reproducing packets of video data corresponding to the user requested first selection of the first one of the video/story segments to play the user requested first selection at the user terminal; (D) receiving from the user a user requested second selection of a second one of the video segments from another of said parallel video streams, wherein the second one of the video segments is not limited to being chronologically after and chronologically at the same time as the one of the video segments, and wherein each of said parallel video streams corresponds to a respective audio sequence prior to step (A); (E) reproducing packets of video data corresponding to the user requested second one of the video segments to play the user requested second selection at the user terminal, and indicating on the on-screen interface which of the video segments have been reproduced; (F) reproducing packets of video data corresponding to a third one of the video segments from any of the parallel video streams to play the third one of the video story segments at the user terminal; wherein said third one of said video segments is later in chronological time in said video story than said first one and said second one of said video segments, and steps (D) and (E) occur after steps (B) and (C) and before step (F); and wherein said first one of said video segments with its corresponding audio sequence from said one of said parallel video streams and another of said video segments with its corresponding audio sequence from said another of said parallel video streams occur at the same time in said video story and are respectively different in both video and audio content. 6. The method of claim 1 , wherein each of said parallel video streams corresponds to a respective time period. | 0.77621 |
9,081,799 | 1 | 2 | 1. A computer-implemented method comprising: obtaining an image of a rendered document that includes text; determining a two dimensional geometric shape based at least on a first location in the image of the rendered document of a first space between a first pair of consecutive words, a second location in the image of the rendered document of a second space between a second pair of consecutive words, and a third location in the image of the rendered document of a third space between a third pair of consecutive words, wherein the first space, the second space, and the third space are not all included on a same line of text in the rendered document; generating a document signature based on the two dimensional geometric shape; and generating a query for an electronic document that is a counterpart to the rendered document, based at least on the document signature. | 1. A computer-implemented method comprising: obtaining an image of a rendered document that includes text; determining a two dimensional geometric shape based at least on a first location in the image of the rendered document of a first space between a first pair of consecutive words, a second location in the image of the rendered document of a second space between a second pair of consecutive words, and a third location in the image of the rendered document of a third space between a third pair of consecutive words, wherein the first space, the second space, and the third space are not all included on a same line of text in the rendered document; generating a document signature based on the two dimensional geometric shape; and generating a query for an electronic document that is a counterpart to the rendered document, based at least on the document signature. 2. The method of claim 1 , wherein the first location, the second location, and the third location each comprise an absolute location in the image and determining the two dimensional geometric shape comprises determining the two dimensional geometric shape using the absolute locations. | 0.683628 |
7,643,907 | 5 | 6 | 5. The method of claim 1 , wherein the first selected segments includes default data and wherein the method further comprises receiving input data and changing the default data to the input data. | 5. The method of claim 1 , wherein the first selected segments includes default data and wherein the method further comprises receiving input data and changing the default data to the input data. 6. The method of claim 5 , wherein the status of the first selected segments further includes whether the default data has been changed to the input data. | 0.5 |
9,436,760 | 17 | 31 | 17. A system, comprising: one or more processors; memory storing instructions that when executed by at least some of the processors effectuate operations comprising: obtaining a weighted semantic graph of semantic similarity between unstructured text in documents within an analyzed corpus, wherein weights of the semantic graph are inferred by unsupervised learning of the weights by one or more computers, and wherein the semantic graph comprises: more than 1000 nodes, each corresponding to at least one respective document within the analyzed corpus; and more than 2000 weighted edges, each weighted edge linking two of the nodes and having a score indicating an amount of semantic similarity between documents corresponding to the two linked nodes; obtaining access to an external corpus having at least some other documents with unstructured text about entities mentioned in the analyzed corpus, the other documents not being within the analyzed corpus; for each of at least 20 evaluation nodes among the nodes of the graph, by one or more processors, scoring semantic similarity between documents in the analyzed corpus and documents in the external corpus selected as being associated with adjacent nodes to the respective evaluation node, wherein scoring semantic similarity comprises: determining the adjacent node in the graph based on the adjacent node sharing an edge with the respective evaluation node; selecting one or more documents from the external corpus based on the selected documents being associated with the adjacent node; determining n-gram weights of a plurality of n-grams in text of the document corresponding to the adjacent node based on the weight of the edge linking the respective evaluation node to the adjacent node in the semantic graph; and determining one or more exogenous semantic similarity scores between the documents selected from the external corpus and the respective evaluation node, the exogenous semantic similarity scores being determined based on the determined n-gram weights and the presence of corresponding n-grams in the respective documents selected from the external corpus; and determining, by one or more processors, a measure of quality of at least some of the weighted edges of the semantic graph based on the exogenous semantic similarity scores. | 17. A system, comprising: one or more processors; memory storing instructions that when executed by at least some of the processors effectuate operations comprising: obtaining a weighted semantic graph of semantic similarity between unstructured text in documents within an analyzed corpus, wherein weights of the semantic graph are inferred by unsupervised learning of the weights by one or more computers, and wherein the semantic graph comprises: more than 1000 nodes, each corresponding to at least one respective document within the analyzed corpus; and more than 2000 weighted edges, each weighted edge linking two of the nodes and having a score indicating an amount of semantic similarity between documents corresponding to the two linked nodes; obtaining access to an external corpus having at least some other documents with unstructured text about entities mentioned in the analyzed corpus, the other documents not being within the analyzed corpus; for each of at least 20 evaluation nodes among the nodes of the graph, by one or more processors, scoring semantic similarity between documents in the analyzed corpus and documents in the external corpus selected as being associated with adjacent nodes to the respective evaluation node, wherein scoring semantic similarity comprises: determining the adjacent node in the graph based on the adjacent node sharing an edge with the respective evaluation node; selecting one or more documents from the external corpus based on the selected documents being associated with the adjacent node; determining n-gram weights of a plurality of n-grams in text of the document corresponding to the adjacent node based on the weight of the edge linking the respective evaluation node to the adjacent node in the semantic graph; and determining one or more exogenous semantic similarity scores between the documents selected from the external corpus and the respective evaluation node, the exogenous semantic similarity scores being determined based on the determined n-gram weights and the presence of corresponding n-grams in the respective documents selected from the external corpus; and determining, by one or more processors, a measure of quality of at least some of the weighted edges of the semantic graph based on the exogenous semantic similarity scores. 31. The system of claim 17 , the operations comprising: multiplying an n-gram matrix representing the occurrence of n-grams in documents in the analyzed corpus by a semantic similarity matrix of the edge weights by performing operations comprising: obtaining a sparse matrix representation of the n-gram matrix in which vectors within the matrix are represented as respective indices identifying the locations on the respective vectors having non-zero values; dividing the semantic similarity matrix into tiles, each tile comprising a plurality of adjacent values from a plurality of rows and a plurality of columns in the semantic similarity matrix; and for each of at least a plurality of the tiles: loading the respective tile from a first level of a memory hierarchy to a second level of a memory hierarchy that has faster access times for a processor that the first level of the memory hierarchy; multiplying the respective tile by at least part of the n-gram matrix to produce a product; and updating an existing value in a resultant matrix by adding the product to the existing value. | 0.749311 |
8,510,287 | 8 | 9 | 8. A computer-readable medium bearing computer executable instructions which, when executed on a computer system having at least a processor and a memory, carry out a method for responding to a search query with annotated entities, comprising the method of: obtaining a set of search results information from a search results retrieval component responsive to receiving a search query from a computer user; identifying a plurality of recommended entities within the search results information and obtaining a corresponding plurality of annotations corresponding to each of the plurality of recommended entities from an annotation component and assigning an affinity value to each of the plurality of annotations, the annotation component being distinct from the search results retrieval component, wherein each annotation for each recommended entity comprises a set of annotation relationships between the user and the recommended entity, each annotation relationship describing a basis for which the recommended entity is relevant to the user, and wherein the affinity values between the user and a recommended entity are determined according to one or more probability density functions; selecting a threshold number of annotation relationships of the plurality of annotations having the highest affinity value between the user and the corresponding recommended entity; generating a search results page, wherein the generated search results page includes a portion of the search results information including at least one of the recommended entities and, for each of the recommended entities in the search results page, annotating the recommended entity by a user actionable indicator proximate to the recommended entity, wherein the user actionable indicator is configured to provide the annotation comprising the selected threshold number of annotation relationships corresponding to the recommended entity upon activation; and returning the generated search results page responsive to the search query from the user. | 8. A computer-readable medium bearing computer executable instructions which, when executed on a computer system having at least a processor and a memory, carry out a method for responding to a search query with annotated entities, comprising the method of: obtaining a set of search results information from a search results retrieval component responsive to receiving a search query from a computer user; identifying a plurality of recommended entities within the search results information and obtaining a corresponding plurality of annotations corresponding to each of the plurality of recommended entities from an annotation component and assigning an affinity value to each of the plurality of annotations, the annotation component being distinct from the search results retrieval component, wherein each annotation for each recommended entity comprises a set of annotation relationships between the user and the recommended entity, each annotation relationship describing a basis for which the recommended entity is relevant to the user, and wherein the affinity values between the user and a recommended entity are determined according to one or more probability density functions; selecting a threshold number of annotation relationships of the plurality of annotations having the highest affinity value between the user and the corresponding recommended entity; generating a search results page, wherein the generated search results page includes a portion of the search results information including at least one of the recommended entities and, for each of the recommended entities in the search results page, annotating the recommended entity by a user actionable indicator proximate to the recommended entity, wherein the user actionable indicator is configured to provide the annotation comprising the selected threshold number of annotation relationships corresponding to the recommended entity upon activation; and returning the generated search results page responsive to the search query from the user. 9. The computer-readable medium of claim 8 , wherein a relationship of the set of annotation relationships describes a positive affinity between the user and the recommended entity. | 0.719814 |
8,117,198 | 1 | 9 | 1. A computer-implemented method for generating a task-enhanced search engine index, the method comprising: determining associations between user tasks and resources accessed by a user while performing various tasks, wherein determining the associations includes determining associations for a particular task in connection with a plurality of resources identified for use in the context of the particular task, and wherein the associations determined for the particular task are determined according to task-related information provided based on use of the plurality of resources in the context of the particular task; filtering the plurality of resources, as filtered resources, for the particular task according to the associations determined for the particular task; storing, as stored associations, the associations determined for the particular task with respect to the filtered resources for each of the user tasks; computing from the stored associations task-related metadata for each of the resources; storing the computed task-related metadata in a search engine index; predicting a predicted task using the computer task-related metadata stored in the search engine index, wherein the predicted task is different from the particular task, and wherein predicting the predicted task includes determining that performing the particular task indicates a probability of subsequently performing the predicted task according to the computer-task related metadata; and generating search results in response to a search query by the user, the search results being generated according to the predicted task and the search engine index. | 1. A computer-implemented method for generating a task-enhanced search engine index, the method comprising: determining associations between user tasks and resources accessed by a user while performing various tasks, wherein determining the associations includes determining associations for a particular task in connection with a plurality of resources identified for use in the context of the particular task, and wherein the associations determined for the particular task are determined according to task-related information provided based on use of the plurality of resources in the context of the particular task; filtering the plurality of resources, as filtered resources, for the particular task according to the associations determined for the particular task; storing, as stored associations, the associations determined for the particular task with respect to the filtered resources for each of the user tasks; computing from the stored associations task-related metadata for each of the resources; storing the computed task-related metadata in a search engine index; predicting a predicted task using the computer task-related metadata stored in the search engine index, wherein the predicted task is different from the particular task, and wherein predicting the predicted task includes determining that performing the particular task indicates a probability of subsequently performing the predicted task according to the computer-task related metadata; and generating search results in response to a search query by the user, the search results being generated according to the predicted task and the search engine index. 9. The method of claim 1 wherein computing the task-related metadata and storing the computed task-related metadata in the search engine index are repeated periodically in order to update the search engine index. | 0.567347 |
7,590,934 | 1 | 5 | 1. A computer-readable storage medium encoded with data for processing by a data processing system, said data comprising: a meta-document for tracking and storing all information pertaining to actions performed by an application program on a document comprising document information during its entire lifetime, comprising a file structure including: an object conveying document information, processing information, and metadata for indexing and retrieving the processing information; wherein all of which are stored on the meta-document and retrievable from the meta-document; wherein the processing information comprises all information pertaining to each time the meta-document is processed by the application program being executed by the data processing system and any results of the processing during the entire life of the meta-document, the processing information being stored on the meta-document each time the meta-document is processed and being retrievable from the meta-document; wherein the metadata comprises all associated metadata pertaining to each time the meta-document is processed by the application program being executed by the data processing system during the entire life of the meta-document, the metadata being stored on the meta-document each time the meta-document is processed and being retrievable from the meta-document; wherein the meta-document is transmitted to a source and parsed at the source for extracting stored processing information and metadata; wherein processing information is stored pertaining to transmitting and parsing at the source and associated metadata stored on the meta-document; wherein the meta-document further comprises a first instruction, embedded on the object, responsive to processing of the meta-document, for generating and storing processing information and associated metadata on the meta-document, wherein the parsing is performed by the first instruction; and wherein the meta-document further comprises a second instruction, embedded on the object, for parsing and extracting selected processing information stored on the meta-document, parsing the meta-document for extracting the selected processing information and associated metadata, and distributing the extracted selected processing information to the source. | 1. A computer-readable storage medium encoded with data for processing by a data processing system, said data comprising: a meta-document for tracking and storing all information pertaining to actions performed by an application program on a document comprising document information during its entire lifetime, comprising a file structure including: an object conveying document information, processing information, and metadata for indexing and retrieving the processing information; wherein all of which are stored on the meta-document and retrievable from the meta-document; wherein the processing information comprises all information pertaining to each time the meta-document is processed by the application program being executed by the data processing system and any results of the processing during the entire life of the meta-document, the processing information being stored on the meta-document each time the meta-document is processed and being retrievable from the meta-document; wherein the metadata comprises all associated metadata pertaining to each time the meta-document is processed by the application program being executed by the data processing system during the entire life of the meta-document, the metadata being stored on the meta-document each time the meta-document is processed and being retrievable from the meta-document; wherein the meta-document is transmitted to a source and parsed at the source for extracting stored processing information and metadata; wherein processing information is stored pertaining to transmitting and parsing at the source and associated metadata stored on the meta-document; wherein the meta-document further comprises a first instruction, embedded on the object, responsive to processing of the meta-document, for generating and storing processing information and associated metadata on the meta-document, wherein the parsing is performed by the first instruction; and wherein the meta-document further comprises a second instruction, embedded on the object, for parsing and extracting selected processing information stored on the meta-document, parsing the meta-document for extracting the selected processing information and associated metadata, and distributing the extracted selected processing information to the source. 5. The meta-document of claim 1 , wherein the application program is embedded on the meta-document. | 0.65625 |
8,351,649 | 9 | 11 | 9. The method of claim 3 , further comprising: using a Bayesian framework to combine outputs of the generative tracker and discriminative tracker; and generating object information corresponding to the object based on the combined outputs, wherein tracking the object comprises using the object information. | 9. The method of claim 3 , further comprising: using a Bayesian framework to combine outputs of the generative tracker and discriminative tracker; and generating object information corresponding to the object based on the combined outputs, wherein tracking the object comprises using the object information. 11. The method of claim 9 , wherein the object information comprises position, size, and rotation information. | 0.723618 |
9,208,440 | 12 | 16 | 12. The method according to claim 11 wherein developing the syntactical model includes representing the events of a hypothetical scenario as independent clauses, each of which has only one verb, and represents an event including sets of entities. | 12. The method according to claim 11 wherein developing the syntactical model includes representing the events of a hypothetical scenario as independent clauses, each of which has only one verb, and represents an event including sets of entities. 16. The method according to claim 12 wherein the entity is an object of an action. | 0.643478 |
9,552,555 | 7 | 12 | 7. A system for recommending content items, the system comprising: a hardware processor that: determines a plurality of accessed content items associated with a user, wherein each of the plurality of accessed content items is associated with a plurality of topics; generates a user interest model of interactions between the plurality of topics and the plurality of accessed content items, wherein the user interest model (i) determines a plurality of related topics associated with the plurality of topics from the plurality of accessed content items, (ii) generates user interest information associated with the user using at least a portion of the plurality of related topics, (iii) determines similarities between the user interest information associated with the user and user interest information of other users including the at least a portion of the plurality of related topics associated with the user, and (iv) determines a conjunction of between the similarities and the plurality of accessed content items; applies the model to determine, for a plurality of content items, a probability that the user selects a content item from the plurality of content items for presentation; ranks the plurality of content items based on the determined probabilities; and selects at least one of the plurality of content items to recommend to the user based on the ranked plurality of content items. | 7. A system for recommending content items, the system comprising: a hardware processor that: determines a plurality of accessed content items associated with a user, wherein each of the plurality of accessed content items is associated with a plurality of topics; generates a user interest model of interactions between the plurality of topics and the plurality of accessed content items, wherein the user interest model (i) determines a plurality of related topics associated with the plurality of topics from the plurality of accessed content items, (ii) generates user interest information associated with the user using at least a portion of the plurality of related topics, (iii) determines similarities between the user interest information associated with the user and user interest information of other users including the at least a portion of the plurality of related topics associated with the user, and (iv) determines a conjunction of between the similarities and the plurality of accessed content items; applies the model to determine, for a plurality of content items, a probability that the user selects a content item from the plurality of content items for presentation; ranks the plurality of content items based on the determined probabilities; and selects at least one of the plurality of content items to recommend to the user based on the ranked plurality of content items. 12. The system of claim 7 , wherein the processor is further configured to: generate a decision tree, wherein a portion of the decision tree identifies which of the user interest information of other users is similar to the user interest information of the user; determine a subset of the plurality of topics based on the decision tree; and determine a conjunction that models interaction between the subset of the plurality of topics and the plurality of content items. | 0.631661 |
7,490,034 | 26 | 31 | 26. A computer-implemented method for obtaining word information by accessing a lexicon that is adapted for use in a plurality of different natural language processing tasks, wherein the lexicon is adapted to be used with a text analyzer in a language processing system, and wherein the lexicon stores word information pertaining to a plurality of words, the lexicon comprising: a word list section storing the plurality of words; sets of data sections, wherein each set of data sections corresponds with an individual word in the word list section, each data section among a set of data sections storing different selected information about the corresponding word in the word list; and an indices section storing a plurality of pointers apart from the sets of data sections, wherein each plurality of pointers corresponds with an individual word and comprises a first set of pointers associated with a natural language processing task and a second set of pointers associated with a different natural language processing task, wherein the first set of pointers is different from the second set of pointers, each of the sets of pointers pointing to data in a data section, the method comprising: accessing the word list section as a function of said word to ascertain a pointer identification for the indices section; using the pointer identification to obtain one of the first or second sets of pointers in the indices section based on the natural language processing task to be performed; using one of the first or second sets of pointers to obtain information from only some data sections of the set of data sections, the only some data sections having information about said word necessary to perform the natural language processing task. | 26. A computer-implemented method for obtaining word information by accessing a lexicon that is adapted for use in a plurality of different natural language processing tasks, wherein the lexicon is adapted to be used with a text analyzer in a language processing system, and wherein the lexicon stores word information pertaining to a plurality of words, the lexicon comprising: a word list section storing the plurality of words; sets of data sections, wherein each set of data sections corresponds with an individual word in the word list section, each data section among a set of data sections storing different selected information about the corresponding word in the word list; and an indices section storing a plurality of pointers apart from the sets of data sections, wherein each plurality of pointers corresponds with an individual word and comprises a first set of pointers associated with a natural language processing task and a second set of pointers associated with a different natural language processing task, wherein the first set of pointers is different from the second set of pointers, each of the sets of pointers pointing to data in a data section, the method comprising: accessing the word list section as a function of said word to ascertain a pointer identification for the indices section; using the pointer identification to obtain one of the first or second sets of pointers in the indices section based on the natural language processing task to be performed; using one of the first or second sets of pointers to obtain information from only some data sections of the set of data sections, the only some data sections having information about said word necessary to perform the natural language processing task. 31. The computer-implemented method of claim 26 , and further comprising selectively accessing the data sections based on the identified pointers, wherein selectively accessing comprises only accessing data sections associated with an identified pointer. | 0.730932 |
4,761,815 | 1 | 4 | 1. Speech recognition apparatus comprising: means for converting audio speech into electroninc signals; means for diverting the incoming speech up along a time line into an array of sequential word states based on the content of the speech, each word state having a time period; means for classifying each word state as one of a plurality of classifications based on the content of the speech during the corresponding time period; means for determining the duration of the time period corresponding to each word state within the array of incoming word states and for using the determined durations to provide an array of durational values corresponding to the word states of the incoming word state array; means for providing a plurality of stored templates representing the vocabulary of the speech recognition apparatus; each template being comprised of two arrays; the first array being a sequence of stored word states each state being classified as one of said plurality of classifications; the second array being a sequence of values indicating the duration of a corresponding stored word state; first comparing means for comparing the classifications of incoming word states with said first array of each of said templates to locate matching states; second comparing means for comparing the duration of each incoming word state with the duration of the corresponding stored word state only where the classifications of the word states match; and means responsive to both of said comparing means for determining which of said templates is the closest match to said array of incoming word states and said array of durational values. | 1. Speech recognition apparatus comprising: means for converting audio speech into electroninc signals; means for diverting the incoming speech up along a time line into an array of sequential word states based on the content of the speech, each word state having a time period; means for classifying each word state as one of a plurality of classifications based on the content of the speech during the corresponding time period; means for determining the duration of the time period corresponding to each word state within the array of incoming word states and for using the determined durations to provide an array of durational values corresponding to the word states of the incoming word state array; means for providing a plurality of stored templates representing the vocabulary of the speech recognition apparatus; each template being comprised of two arrays; the first array being a sequence of stored word states each state being classified as one of said plurality of classifications; the second array being a sequence of values indicating the duration of a corresponding stored word state; first comparing means for comparing the classifications of incoming word states with said first array of each of said templates to locate matching states; second comparing means for comparing the duration of each incoming word state with the duration of the corresponding stored word state only where the classifications of the word states match; and means responsive to both of said comparing means for determining which of said templates is the closest match to said array of incoming word states and said array of durational values. 4. Speech recognition apparatus as defined in claim 1 wherein said dividing and classifying means includes: means for determining the frequency of the incoming speech from said electronic signals; means for dividing the incoming speech signals into equal time portions; means for classifying each time portion as fricative-like, vowel-like, or silence based on the average frequency of the incoming speech signals during the time portion; means for designating a group of time portions as an incoming state when a predetermined number of proximately located time portions have the same classifications, and for classifying the state in accordance with the predominate classification of the time portions which make up the state. | 0.5 |
6,151,021 | 49 | 59 | 49. A computer readable storage medium having program code stored thereon, wherein the program code is arranged so that, when the program code is executed by a computer, a note is displayed, the note has an options area, a notation area, and a grab area, and the notation area and the grab area are different areas of the note and so that, when the options area is activated, a list of options is displayed, wherein the options area contains a nonmove option. | 49. A computer readable storage medium having program code stored thereon, wherein the program code is arranged so that, when the program code is executed by a computer, a note is displayed, the note has an options area, a notation area, and a grab area, and the notation area and the grab area are different areas of the note and so that, when the options area is activated, a list of options is displayed, wherein the options area contains a nonmove option. 59. The computer readable storage medium of claim 49 wherein the options area contains a duplicate option. | 0.576 |
7,653,594 | 1 | 21 | 1. A system, comprising: a database; a computer system having read and write access to said database; and wherein said database stores a first plurality of records including a first record for a first consumer; wherein said first record stores: (1) CID data (consumer identification data) indicating a first consumer CID for said first consumer; (2) transaction data in a set of transaction class fields indicating items transacted by said first consumer during a first prior time period, including a first transaction class field indicating items transacted by said first consumer in a first transaction class during said first prior time period; (3) first correlated class predictive data in a first correlated class predictive data field indicating at least one of a ranking, a probability, and a prediction that said first consumer will transact in a first correlated class during a correlated time period, and wherein said correlated time period is subsequent in time to said first prior time period; wherein said computer system stores data defining a predictive model function, said predictive model function is defined at least in part by values representing statistical correlations between the existence of transactions in at least one transaction class for transactions that occurred during at least one second prior time period and transactions in at least one transaction class that occurred during at least one third time period, wherein said at least one third time period is subsequent in time to said at least one second time period, said statistical correlations derived from a second plurality of consumer records; and wherein said computer system is structured to (1) apply said predictive model function to transaction data in said first record for transactions in said first record that occurred during said first prior time period, to result in said first correlated class predictive data and (2) store said first correlated class predictive data in said first correlated class predictive data field of said first record. | 1. A system, comprising: a database; a computer system having read and write access to said database; and wherein said database stores a first plurality of records including a first record for a first consumer; wherein said first record stores: (1) CID data (consumer identification data) indicating a first consumer CID for said first consumer; (2) transaction data in a set of transaction class fields indicating items transacted by said first consumer during a first prior time period, including a first transaction class field indicating items transacted by said first consumer in a first transaction class during said first prior time period; (3) first correlated class predictive data in a first correlated class predictive data field indicating at least one of a ranking, a probability, and a prediction that said first consumer will transact in a first correlated class during a correlated time period, and wherein said correlated time period is subsequent in time to said first prior time period; wherein said computer system stores data defining a predictive model function, said predictive model function is defined at least in part by values representing statistical correlations between the existence of transactions in at least one transaction class for transactions that occurred during at least one second prior time period and transactions in at least one transaction class that occurred during at least one third time period, wherein said at least one third time period is subsequent in time to said at least one second time period, said statistical correlations derived from a second plurality of consumer records; and wherein said computer system is structured to (1) apply said predictive model function to transaction data in said first record for transactions in said first record that occurred during said first prior time period, to result in said first correlated class predictive data and (2) store said first correlated class predictive data in said first correlated class predictive data field of said first record. 21. The system of claim 1 further comprising a terminal for presenting transaction incentive to said first consumer. | 0.83844 |
9,454,563 | 17 | 18 | 17. A system comprising: one or more storage devices including data responsive to search queries; one or more processors in communication with the one or more storage devices, the one or more processors: receiving one or more search criteria that was specified by a user; in response to receiving the one or more search criteria, causing a search engine to generate search results from among the data that corresponds to a search query that is based on the one or more search criteria, performing a fact check of the search results by comparing each search result from among the search results to respective information from one or more sources to determine a factual accuracy of said each search result, generating fact checked search results by adjusting the search results in accordance with the fact check, and causing to be displayed the fact checked search results in response to receiving the search query. | 17. A system comprising: one or more storage devices including data responsive to search queries; one or more processors in communication with the one or more storage devices, the one or more processors: receiving one or more search criteria that was specified by a user; in response to receiving the one or more search criteria, causing a search engine to generate search results from among the data that corresponds to a search query that is based on the one or more search criteria, performing a fact check of the search results by comparing each search result from among the search results to respective information from one or more sources to determine a factual accuracy of said each search result, generating fact checked search results by adjusting the search results in accordance with the fact check, and causing to be displayed the fact checked search results in response to receiving the search query. 18. The system of claim 17 , wherein the one or more processors parses each search result of the search results into a respective parsed search result, and performing the fact check of the search results comprises comparing each parsed search result to respective information from the one or more sources to determine a factual accuracy of said each parsed search result. | 0.504011 |
10,055,394 | 1 | 8 | 1. A method comprising: presenting a document in a graphical user interface of a document editing application on a display of a first user device associated with a first user; receiving, at the first user device, first information describing a first user modification to the document; in response to receiving the first information, changing the presentation of the document on the first user device to reflect the first user modification to the document; receiving, from a second user device, second information describing a second user modification to the document; in response to receiving the second information, changing the presentation of the document on the first user device to reflect the second user modification to the document; in accordance with an automatic determination that the first user modification to the document corresponds to the first user, forgoing presenting an animation associated with the first user modification to the document; and in accordance with an automatic determination that the second user modification to the document corresponds to a second user different from the first user, presenting an animation associated with the second user modification to the document. | 1. A method comprising: presenting a document in a graphical user interface of a document editing application on a display of a first user device associated with a first user; receiving, at the first user device, first information describing a first user modification to the document; in response to receiving the first information, changing the presentation of the document on the first user device to reflect the first user modification to the document; receiving, from a second user device, second information describing a second user modification to the document; in response to receiving the second information, changing the presentation of the document on the first user device to reflect the second user modification to the document; in accordance with an automatic determination that the first user modification to the document corresponds to the first user, forgoing presenting an animation associated with the first user modification to the document; and in accordance with an automatic determination that the second user modification to the document corresponds to a second user different from the first user, presenting an animation associated with the second user modification to the document. 8. The method of claim 1 , wherein the second user modification comprises adding a graphical element, and the animation associated with the second user modification comprises an expansion animation. | 0.641304 |
9,152,979 | 17 | 18 | 17. An ideograph insertion system, comprising: a processor configured to execute processor executable instructions; a communications device coupled to the processor and configured to transmit information from a computer network to the processor and transmit information from the processor to the computer network; and a non-transitory processor readable medium coupled to the processor and storing processor executable instructions that when executed cause the processor to: receive application data from a user device via a communications network, the application data being indicative of text entered into a messaging application by a user via the user device; analyze the application data for one or more indicator, the one or more indicator being at least a portion of the text entered into the application, the one or more indicator having one or more meaning; retrieve one or more selected ideograph from a database populated with ideographs received from and associated with one or more advertisers, the one or more selected ideograph being indicative of a graphical representation of the one or more meanings of the one or more indicator; transmit the one or more selected ideograph to the user device via the communications network; and charge a fee to at least one advertiser associated with the one or more selected ideograph, wherein the one or more selected ideograph is retrieved and transmitted to the user device without immediate user action beyond entering the text into the application. | 17. An ideograph insertion system, comprising: a processor configured to execute processor executable instructions; a communications device coupled to the processor and configured to transmit information from a computer network to the processor and transmit information from the processor to the computer network; and a non-transitory processor readable medium coupled to the processor and storing processor executable instructions that when executed cause the processor to: receive application data from a user device via a communications network, the application data being indicative of text entered into a messaging application by a user via the user device; analyze the application data for one or more indicator, the one or more indicator being at least a portion of the text entered into the application, the one or more indicator having one or more meaning; retrieve one or more selected ideograph from a database populated with ideographs received from and associated with one or more advertisers, the one or more selected ideograph being indicative of a graphical representation of the one or more meanings of the one or more indicator; transmit the one or more selected ideograph to the user device via the communications network; and charge a fee to at least one advertiser associated with the one or more selected ideograph, wherein the one or more selected ideograph is retrieved and transmitted to the user device without immediate user action beyond entering the text into the application. 18. The ideograph insertion system of claim 17 , wherein the one or more selected ideograph is one or more selected branded visual content. | 0.620219 |
8,694,303 | 18 | 19 | 18. The system of claim 11 , wherein the translation parameters associated with the candidate translation units comprise one or more of the following: a candidate translation space policy, a scoring function, a BLEU score, a weight vector, and a loss function. | 18. The system of claim 11 , wherein the translation parameters associated with the candidate translation units comprise one or more of the following: a candidate translation space policy, a scoring function, a BLEU score, a weight vector, and a loss function. 19. The system of claim 18 , wherein the translation parameters are ranked so that candidate translation units having higher relevancy for a correct translation of the source units are associated with one or more of the following: a minimized loss function, a highest weight vector, and a best candidate translation space policy. | 0.5 |
8,219,615 | 2 | 10 | 2. The computer program product of claim 1 , wherein the computer program product is configured such that: each of the plurality of n-tuples includes only the first text representing the stock ticker symbol and the second text representing the company name corresponding to the stock ticker symbol, the indicating comprises displaying both the first text representing the stock ticker symbol and the second text representing the company name corresponding to the stock ticker symbol in the at least one of the plurality of n-tuples such that the first text precedes the second text, the indicating further comprises displaying multiple n-tuples in a vertically-oriented list directly beneath the stock-related field, the indicating further comprises visually emphasizing a subset of text in at least one of the text strings of each of the multiple n-tuples in the vertically-oriented list with the subset of text matching the at least portion of characters typed so far, and further comprising: computer code for allowing receipt of selection input initiated by the user selecting one of the multiple n-tuples from the vertically-oriented list; and computer code for using at least one of the text strings from the selected one of the multiple n-tuples in connection with the stock-related field. | 2. The computer program product of claim 1 , wherein the computer program product is configured such that: each of the plurality of n-tuples includes only the first text representing the stock ticker symbol and the second text representing the company name corresponding to the stock ticker symbol, the indicating comprises displaying both the first text representing the stock ticker symbol and the second text representing the company name corresponding to the stock ticker symbol in the at least one of the plurality of n-tuples such that the first text precedes the second text, the indicating further comprises displaying multiple n-tuples in a vertically-oriented list directly beneath the stock-related field, the indicating further comprises visually emphasizing a subset of text in at least one of the text strings of each of the multiple n-tuples in the vertically-oriented list with the subset of text matching the at least portion of characters typed so far, and further comprising: computer code for allowing receipt of selection input initiated by the user selecting one of the multiple n-tuples from the vertically-oriented list; and computer code for using at least one of the text strings from the selected one of the multiple n-tuples in connection with the stock-related field. 10. The computer program product of claim 2 , wherein the computer program product is configured such that the dynamically determining comprises determining whether the at least portion of characters typed so far match leading text in the one or more text strings in the at least one of the plurality of n-tuples. | 0.785027 |
9,130,651 | 1 | 8 | 1. An energy harvesting communication device configured with on chip signal booster apparatus; comprising: at least a communication apparatus; at least an antenna apparatus in association with an input and/or output device; at least a processor in communication with at least one of: said at least a communication apparatus; said at least an antenna apparatus; a charge platform in association with at least charging circuit; at least an interactive media in association with at least an interactive interface; an output device; and at least a sensor apparatus embedded in silicon substrate and etched and/or fused in nano-fiber and/or microfiber material, said at least a sensor apparatus in association with said at least a communication apparatus to provide at least one of an effective energy harvesting medium, communication clarity, communication medium, detection selectivity medium, and detection platform. | 1. An energy harvesting communication device configured with on chip signal booster apparatus; comprising: at least a communication apparatus; at least an antenna apparatus in association with an input and/or output device; at least a processor in communication with at least one of: said at least a communication apparatus; said at least an antenna apparatus; a charge platform in association with at least charging circuit; at least an interactive media in association with at least an interactive interface; an output device; and at least a sensor apparatus embedded in silicon substrate and etched and/or fused in nano-fiber and/or microfiber material, said at least a sensor apparatus in association with said at least a communication apparatus to provide at least one of an effective energy harvesting medium, communication clarity, communication medium, detection selectivity medium, and detection platform. 8. The energy harvesting communication device of claim 1 , wherein said processor further comprises at least one of a CMOS processor, a CMOS memory, a CMOS sensor, each configured with at least one of: at least one antenna apparatus; at least a meta-material; at least a substrate; at least a substrate nano-fiber; said antenna apparatus being opened at least at one end and shorted at least at a second end; and wherein said antenna apparatus further comprises CMOS intra-chip antennas comprising at least one of: a radio frequency transceiver; an RF transmitter; an IR transmitter; a transducer; an IR transceiver. | 0.82716 |
7,730,162 | 1 | 2 | 1. A method of generating an application specific interface object from a markup language message representing said application specific interface object, said method comprising: determining an identity of said application specific interface object as specified by said markup language message, wherein the markup language message includes attributes and corresponding data of objects in serialized form used by the application specific interface object; parsing serialized form of said markup language message to identify objects, associated attributes, and corresponding data of said application specific interface object represented by said markup language message; and identifying integration instructions encapsulated within said application specific interface object for associating said corresponding data with said attributes to generate said objects; including said identified objects and associated parameters within an object table to generate said application specific interface object according to said integration instructions; comparing elements of said markup language message to determine a data type of said objects; and determining whether said objects represent a subclass by identifying sub-elements associated with said compared elements representing objects. | 1. A method of generating an application specific interface object from a markup language message representing said application specific interface object, said method comprising: determining an identity of said application specific interface object as specified by said markup language message, wherein the markup language message includes attributes and corresponding data of objects in serialized form used by the application specific interface object; parsing serialized form of said markup language message to identify objects, associated attributes, and corresponding data of said application specific interface object represented by said markup language message; and identifying integration instructions encapsulated within said application specific interface object for associating said corresponding data with said attributes to generate said objects; including said identified objects and associated parameters within an object table to generate said application specific interface object according to said integration instructions; comparing elements of said markup language message to determine a data type of said objects; and determining whether said objects represent a subclass by identifying sub-elements associated with said compared elements representing objects. 2. The method of claim 1 , wherein said parsing step further comprising: if said application specific interface object is determined to be a subclass, recursively processing said sub-elements; and representing said sub-elements as another object in said object table. | 0.5 |
9,298,871 | 17 | 21 | 17. A system for performing translations of source code for parameterized cells, comprising: a processor that executes sets of instructions; and a memory storing programmable code, wherein the programmable code includes the sets of instructions which, when executed by the processor, cause the processor to at least: identify a first source code in a first programming language for a parameterized cell; determine a language construct in the first programming language for the parameterized cell; identify a mapping for the language construct in the first programming language to translate the language construct into an equivalent construct in a second programming language; translate, with the mapping, the language construct in the first programming language to the equivalent construct in the second programming language to generate the second source code, which comprises a translated version of the first source code, for the parameterized cell in the second programming language by tokenizing the first source code that breaks the first source code into multiple, smaller elements for the parameterized cell; and a parameterized cell translation module that is configured to include or function in conjunction with the processor to: verify correctness of the mapping by determining whether a first layout and a second layout of the parameterized cell are geometrically identical by performing an exclusive OR operation between the first layout corresponding to the language construct and the second layout corresponding to the equivalent construct, wherein the correctness of the mapping is verified to determine whether the language construct in the first source code written in the first programming language and the equivalent construct in a second source code written in the second programming language are semantically correct. | 17. A system for performing translations of source code for parameterized cells, comprising: a processor that executes sets of instructions; and a memory storing programmable code, wherein the programmable code includes the sets of instructions which, when executed by the processor, cause the processor to at least: identify a first source code in a first programming language for a parameterized cell; determine a language construct in the first programming language for the parameterized cell; identify a mapping for the language construct in the first programming language to translate the language construct into an equivalent construct in a second programming language; translate, with the mapping, the language construct in the first programming language to the equivalent construct in the second programming language to generate the second source code, which comprises a translated version of the first source code, for the parameterized cell in the second programming language by tokenizing the first source code that breaks the first source code into multiple, smaller elements for the parameterized cell; and a parameterized cell translation module that is configured to include or function in conjunction with the processor to: verify correctness of the mapping by determining whether a first layout and a second layout of the parameterized cell are geometrically identical by performing an exclusive OR operation between the first layout corresponding to the language construct and the second layout corresponding to the equivalent construct, wherein the correctness of the mapping is verified to determine whether the language construct in the first source code written in the first programming language and the equivalent construct in a second source code written in the second programming language are semantically correct. 21. The system of claim 17 , in which the mapping corresponds to equivalent syntax, functions and classes between the first programming language and the second programming language. | 0.669708 |
8,522,203 | 2 | 4 | 2. The method of claim 1 , wherein the script includes one or more headers, wherein at least one of the headers includes an edit verification value, the method further including storing a first edit verification value for at least one of the headers in memory when the script is created, wherein the determining whether the script has been edited outside of the script generator comprises comparing the stored edit verification value with a second edit verification value for a corresponding at least one of the headers in the script. | 2. The method of claim 1 , wherein the script includes one or more headers, wherein at least one of the headers includes an edit verification value, the method further including storing a first edit verification value for at least one of the headers in memory when the script is created, wherein the determining whether the script has been edited outside of the script generator comprises comparing the stored edit verification value with a second edit verification value for a corresponding at least one of the headers in the script. 4. The method of claim 2 , wherein the modifying of the management flag comprises deleting the management flag from a block of the header. | 0.5 |
8,842,881 | 11 | 13 | 11. A method, comprising: detecting a target with a first plurality of detectors, wherein the first plurality of detectors comprises a first appearance-based detector and a first silhouette-based detector; detecting the target with a second plurality of detectors, wherein the second plurality of detectors comprises a first appearance-based detector and a second silhouette-based detector; generating a first plurality of feature cues with the first plurality of detectors; generating a second plurality of feature cues with the second plurality of detectors; fusing the first plurality of feature cues and the second plurality of feature cues to create a set of target hypotheses; tracking the target based on the set of target hypotheses and a scene context analysis; and updating the tracking of the target based on a motion model; wherein the tracking is used to compensate for a motion of a radiation source tracked by a radiation imaging device. | 11. A method, comprising: detecting a target with a first plurality of detectors, wherein the first plurality of detectors comprises a first appearance-based detector and a first silhouette-based detector; detecting the target with a second plurality of detectors, wherein the second plurality of detectors comprises a first appearance-based detector and a second silhouette-based detector; generating a first plurality of feature cues with the first plurality of detectors; generating a second plurality of feature cues with the second plurality of detectors; fusing the first plurality of feature cues and the second plurality of feature cues to create a set of target hypotheses; tracking the target based on the set of target hypotheses and a scene context analysis; and updating the tracking of the target based on a motion model; wherein the tracking is used to compensate for a motion of a radiation source tracked by a radiation imaging device. 13. The method of claim 11 , wherein the first and second appearance-based detectors are video cameras and the first and second silhouette-based detectors are video cameras. | 0.820911 |
8,166,388 | 1 | 11 | 1. A method for overlaying ink on a document, the method comprising the steps of: displaying the document in a first window, the first window having a handle; generating a transparent second window over the first window; attaching the second window to the handle of the first window; collecting ink data in the second window; and rendering the ink data as rendered ink in the second window. | 1. A method for overlaying ink on a document, the method comprising the steps of: displaying the document in a first window, the first window having a handle; generating a transparent second window over the first window; attaching the second window to the handle of the first window; collecting ink data in the second window; and rendering the ink data as rendered ink in the second window. 11. The method of claim 1 , further including setting a property that is associated with the second window and that represents the handle of the first window. | 0.814118 |
8,996,403 | 1 | 2 | 1. A computer-implemented method comprising: receiving a plurality of keywords from a source, the source being from the group: a search query and a product listing; associating a plurality of levels of dimension data with each of the plurality of keywords, the plurality of levels of dimension data including information indicative of a propensity of a keyword of the plurality of keywords to belong to a particular product category in a product category hierarchy, the plurality of levels of dimension data including keyword clustering dimension data, the keyword clustering dimension data including information indicative of an affinity that a keyword of the plurality of keywords has with a particular keyword cluster of a plurality of pre-defined keyword clusters, the plurality of levels of dimension data including keyword traffic dimension data, the keyword traffic dimension data including information indicative of a predicted revenue per click level for a keyword of the plurality of keywords, the keyword traffic dimension data including information indicative of a value related to confirmed registered users; storing the processed plurality of keywords and dimension data in a keyword database; automatically selecting at least one of the stored plurality of keywords to be trafficked using trafficking criteria for the plurality of levels of dimension data, the trafficking criteria being specified by a user; automatically selecting at least one of a plurality of trafficked keywords to be untrafficked based on pruning criteria for at least one of the plurality of levels of dimension data, the pruning criteria being specified by the user; automatically generating a report, the report comprising the selected at least one of the plurality of trafficked keywords and a corresponding loss in revenue for each one of the selected at least one of the plurality of trafficked keywords, the corresponding loss in revenue being based on the corresponding one of the selected at least one of the plurality of trafficked keywords being removed from being trafficked; causing the report to be displayed to the user; and automatically removing at least a portion of the selected at least one of the plurality of trafficked keywords from being trafficked on a search engine based on the selection of the at least one of the plurality of trafficked words to be untrafficked, the removing of the selected at least one of the plurality of trafficked keywords being performed in response to user input corresponding to the report, the user input being used to determine the at least a portion of the selected at least one of the plurality of trafficked keywords to remove, and the user input comprising an indication of an approval to remove the selected at least one of the plurality of trafficked keywords of the report or an indication of one or more of the selected at least one of the plurality of trafficked keywords to exclude from removal. | 1. A computer-implemented method comprising: receiving a plurality of keywords from a source, the source being from the group: a search query and a product listing; associating a plurality of levels of dimension data with each of the plurality of keywords, the plurality of levels of dimension data including information indicative of a propensity of a keyword of the plurality of keywords to belong to a particular product category in a product category hierarchy, the plurality of levels of dimension data including keyword clustering dimension data, the keyword clustering dimension data including information indicative of an affinity that a keyword of the plurality of keywords has with a particular keyword cluster of a plurality of pre-defined keyword clusters, the plurality of levels of dimension data including keyword traffic dimension data, the keyword traffic dimension data including information indicative of a predicted revenue per click level for a keyword of the plurality of keywords, the keyword traffic dimension data including information indicative of a value related to confirmed registered users; storing the processed plurality of keywords and dimension data in a keyword database; automatically selecting at least one of the stored plurality of keywords to be trafficked using trafficking criteria for the plurality of levels of dimension data, the trafficking criteria being specified by a user; automatically selecting at least one of a plurality of trafficked keywords to be untrafficked based on pruning criteria for at least one of the plurality of levels of dimension data, the pruning criteria being specified by the user; automatically generating a report, the report comprising the selected at least one of the plurality of trafficked keywords and a corresponding loss in revenue for each one of the selected at least one of the plurality of trafficked keywords, the corresponding loss in revenue being based on the corresponding one of the selected at least one of the plurality of trafficked keywords being removed from being trafficked; causing the report to be displayed to the user; and automatically removing at least a portion of the selected at least one of the plurality of trafficked keywords from being trafficked on a search engine based on the selection of the at least one of the plurality of trafficked words to be untrafficked, the removing of the selected at least one of the plurality of trafficked keywords being performed in response to user input corresponding to the report, the user input being used to determine the at least a portion of the selected at least one of the plurality of trafficked keywords to remove, and the user input comprising an indication of an approval to remove the selected at least one of the plurality of trafficked keywords of the report or an indication of one or more of the selected at least one of the plurality of trafficked keywords to exclude from removal. 2. The method as claimed in claim 1 further comprising vetting the plurality of keywords against a blacklist. | 0.657233 |
8,645,991 | 5 | 6 | 5. The method of claim 1 , further comprising: receiving a first signal from the second user in response to the at least one item of supplemental content. | 5. The method of claim 1 , further comprising: receiving a first signal from the second user in response to the at least one item of supplemental content. 6. The method of claim 5 , further comprising: logging the first signal. | 0.815385 |
4,807,181 | 5 | 6 | 5. The word-processing apparatus of claim 4, wherein at least one of said root character sequences includes word separator, said second format is a compressed code format having variable numbers of bits for the different characters, some characters with bit-length in excess of a byte, and said word separator has a discrete code in said first and second formats, and further including converting means from said second code format to said first code format operable prior to replacement of a word in said display. | 5. The word-processing apparatus of claim 4, wherein at least one of said root character sequences includes word separator, said second format is a compressed code format having variable numbers of bits for the different characters, some characters with bit-length in excess of a byte, and said word separator has a discrete code in said first and second formats, and further including converting means from said second code format to said first code format operable prior to replacement of a word in said display. 6. The word-processing apparatus of claim 5, wherein said first code format requires less than all bits of said byte to distinguish said characters, at least one bit being in excess of the code format requirement, and said converting means for said second code format is enabled in response to a predetermined state of said one bit in excess. | 0.5 |
8,676,731 | 5 | 21 | 5. In an automated data extraction environment, a non-transitory computer readable storage medium having instructions that when executed by a processor responsive to the instructions, perform a method of generating a confidence attribute comprising: scanning a set of data items, each data item corresponding to a control data value; generating, in a series of transformations, a recognized value from the scanned data item, the series of transformations defining a combined sequence from an input stream of data form to a final data value to convert the scanned data item to the recognized value, each of the transformations in the series converting the data item to a different form of the recognized value; determining, for each of the transformations, a component confidence, the component confidence indicative of a likelihood of accurate conversion from an input form to the different form; combining each of the component confidences determined for a particular data item in a weighted manner using a non-linear statistical model to generate a final confidence; and comparing, in a learning mode, the recognized value from a final transformation of the data item to the corresponding control data value, the recognized value defining a business relevant data value, the final confidence being indicative of the data value being a true representation of the original value in the input data stream. | 5. In an automated data extraction environment, a non-transitory computer readable storage medium having instructions that when executed by a processor responsive to the instructions, perform a method of generating a confidence attribute comprising: scanning a set of data items, each data item corresponding to a control data value; generating, in a series of transformations, a recognized value from the scanned data item, the series of transformations defining a combined sequence from an input stream of data form to a final data value to convert the scanned data item to the recognized value, each of the transformations in the series converting the data item to a different form of the recognized value; determining, for each of the transformations, a component confidence, the component confidence indicative of a likelihood of accurate conversion from an input form to the different form; combining each of the component confidences determined for a particular data item in a weighted manner using a non-linear statistical model to generate a final confidence; and comparing, in a learning mode, the recognized value from a final transformation of the data item to the corresponding control data value, the recognized value defining a business relevant data value, the final confidence being indicative of the data value being a true representation of the original value in the input data stream. 21. The method of claim 5 further comprising: identifying, in an automated data collection stream, transformations of data between data representation forms, each of the transformations subject to a likelihood of accurate transformation between the data representation forms; receiving a training data set; retrieving a control set of data representative of an accurate translation of the data through each of the transformations; and developing a confidence model for specifying, for each translation of data, a relative weight indicative of an accuracy for that transformation. | 0.712798 |
8,819,055 | 1 | 5 | 1. A system for managing a logical people group (LPG), comprising: a computer, including a computer memory and processor; a user directory which includes a first plurality of attribute values for each of a plurality of users and the user directory maps the plurality of users to a plurality of groups; an attribute directory, separate from the user directory, which includes a second plurality of attribute values for each of the plurality of users, which second plurality of attribute values are not recorded in said user directory and the attribute directory maps a plurality of business attributes to the plurality of users; a security layer, separate from the user directory and attribute directory, which includes a third plurality of attribute values for each of the plurality of users, which third plurality of attribute values are not recorded in said user directory or attribute directory and the security layer includes a plurality of roles, wherein each role is associated with an application and includes one or more users from the plurality of users; a query module including a query cache executing on the computer, operable to receive a complex query wherein the complex query includes a first parameter operable on the first plurality of attribute values, a second defined parameter operable on the second plurality of attribute values, and a third parameter operable on the third plurality of attribute values; wherein, in response to the complex query, the query module, searches the user directory and identifies a first subset of the plurality users having attribute values satisfying the first parameter, searches the attribute directory and identifies a second subset of the plurality of users having attribute values satisfying the second parameter, searches the security layer and identifies a third subset of the plurality users having attribute values satisfying the third parameter, compares the first subset, the second subset, and the third subset, returns an LPG comprising a logical group of a plurality of users present in all of the first subset, the second subset, and the third subset, wherein the LPG is defined by the complex query, and stores the LPG in a query cache which stores, for a user-definable period, LPGs based on previously received complex queries. | 1. A system for managing a logical people group (LPG), comprising: a computer, including a computer memory and processor; a user directory which includes a first plurality of attribute values for each of a plurality of users and the user directory maps the plurality of users to a plurality of groups; an attribute directory, separate from the user directory, which includes a second plurality of attribute values for each of the plurality of users, which second plurality of attribute values are not recorded in said user directory and the attribute directory maps a plurality of business attributes to the plurality of users; a security layer, separate from the user directory and attribute directory, which includes a third plurality of attribute values for each of the plurality of users, which third plurality of attribute values are not recorded in said user directory or attribute directory and the security layer includes a plurality of roles, wherein each role is associated with an application and includes one or more users from the plurality of users; a query module including a query cache executing on the computer, operable to receive a complex query wherein the complex query includes a first parameter operable on the first plurality of attribute values, a second defined parameter operable on the second plurality of attribute values, and a third parameter operable on the third plurality of attribute values; wherein, in response to the complex query, the query module, searches the user directory and identifies a first subset of the plurality users having attribute values satisfying the first parameter, searches the attribute directory and identifies a second subset of the plurality of users having attribute values satisfying the second parameter, searches the security layer and identifies a third subset of the plurality users having attribute values satisfying the third parameter, compares the first subset, the second subset, and the third subset, returns an LPG comprising a logical group of a plurality of users present in all of the first subset, the second subset, and the third subset, wherein the LPG is defined by the complex query, and stores the LPG in a query cache which stores, for a user-definable period, LPGs based on previously received complex queries. 5. The system of claim 1 wherein the complex query includes at least one parameter operable on a role attribute. | 0.647799 |
9,633,123 | 10 | 11 | 10. The method of claim 9 , wherein each of the at least one query constraints include a set of column query patterns having a two component tuple. | 10. The method of claim 9 , wherein each of the at least one query constraints include a set of column query patterns having a two component tuple. 11. The method of claim 10 , wherein the query satisfies the at least one query constraints when each of the set of column query patterns in the least one query constraint is satisfied. | 0.5 |
8,229,808 | 1 | 14 | 1. A wireless computing device configured for capture and pre-decision processing of digital data related to a document in a distributed decisioning environment having a set of decisioning filters, the digital data including at least a digital image of the document associated with a financial transaction, the mobile device configured for communication over a wireless communications network with a server of the distributed decisioning environment, the mobile device comprising: a storage for storing a local decisioning filter of the set of decisioning filters, the local decisioning filter including exception criteria, the set of decisioning filters for determining a settlement path for the digital data; a device for converting the document to the digital image and associated data as the digital data; and a processor configured to implement instructions of: apply the local decisioning filter to the digital data to produce pre-decisioned digital data reflecting a first decision result of the pre-decision processing; and transmit the pre-decisioned digital data over the wireless communications network for subsequent receipt by the server; wherein the server is configured to further decision the pre-decisioned digital data using one or more additional decisioning filters of the set of decisioning filters. | 1. A wireless computing device configured for capture and pre-decision processing of digital data related to a document in a distributed decisioning environment having a set of decisioning filters, the digital data including at least a digital image of the document associated with a financial transaction, the mobile device configured for communication over a wireless communications network with a server of the distributed decisioning environment, the mobile device comprising: a storage for storing a local decisioning filter of the set of decisioning filters, the local decisioning filter including exception criteria, the set of decisioning filters for determining a settlement path for the digital data; a device for converting the document to the digital image and associated data as the digital data; and a processor configured to implement instructions of: apply the local decisioning filter to the digital data to produce pre-decisioned digital data reflecting a first decision result of the pre-decision processing; and transmit the pre-decisioned digital data over the wireless communications network for subsequent receipt by the server; wherein the server is configured to further decision the pre-decisioned digital data using one or more additional decisioning filters of the set of decisioning filters. 14. The wireless computing device of claim 1 , wherein the server is configured to confirm the settlement path including routing of the financial transaction to a settlement endpoint by: analyzing the pre-decisioned digital data received to produce a result indicating if further decision processing is needed to determine the settlement path suitable for the pre-decisioned digital data; implementing at least one further processing step based on the result, the further processing step selected from, a) determining the settlement path for the pre-decisioned digital data if no further decision processing is needed by a second decisioning process, and b) applying the second decisioning process to the pre-decisioned digital data by using the one or more additional decisioning filters of the set of decisioning filters; and assign the settlement path to the pre-decisioned digital data for storage in a database. | 0.5 |
10,133,993 | 1 | 9 | 1. A method comprising: performing operations for reducing computing resources used by an online social network by generating a user interface to structure search results corresponding to a plurality of potential experts in a specific skill and to limit presentation of endorsement data of an expert presented in the search results to an inquirer that is within a threshold degree of connection from the expert the operations comprising: receiving, from a device of the inquirer, a search request for the expert in the specific skill; accessing, from a database in the online social network, profile data of members in the online social network, the profile data including the endorsement data; determining the plurality of potential experts from the members in the online social network based on the profile data; accessing social graph data of the plurality of potential experts, the social graph data including degrees of connections between the inquirer and the plurality of potential experts; calculating, using a processor, a ranking value for each member of the plurality of potential experts based on the accessed profile data and the accessed social graph data; verifying the expert from the plurality of potential experts based on the calculated ranking value for each member of the plurality of potential experts, the calculated ranking value of the expert being higher than a predetermined threshold; and including the profile data for the verified expert in the search results for presentation on a display of the device, based on a determination that a degree of connection between the verified expert and the inquirer is within the threshold degree of connection. | 1. A method comprising: performing operations for reducing computing resources used by an online social network by generating a user interface to structure search results corresponding to a plurality of potential experts in a specific skill and to limit presentation of endorsement data of an expert presented in the search results to an inquirer that is within a threshold degree of connection from the expert the operations comprising: receiving, from a device of the inquirer, a search request for the expert in the specific skill; accessing, from a database in the online social network, profile data of members in the online social network, the profile data including the endorsement data; determining the plurality of potential experts from the members in the online social network based on the profile data; accessing social graph data of the plurality of potential experts, the social graph data including degrees of connections between the inquirer and the plurality of potential experts; calculating, using a processor, a ranking value for each member of the plurality of potential experts based on the accessed profile data and the accessed social graph data; verifying the expert from the plurality of potential experts based on the calculated ranking value for each member of the plurality of potential experts, the calculated ranking value of the expert being higher than a predetermined threshold; and including the profile data for the verified expert in the search results for presentation on a display of the device, based on a determination that a degree of connection between the verified expert and the inquirer is within the threshold degree of connection. 9. The method of claim 1 , wherein the member profile data includes a location for each member in the plurality of potential experts, and the verifying of the expert is based on a distance from the inquirer to the location of the expert being below a predetermined threshold. | 0.615922 |
7,685,512 | 8 | 9 | 8. The computer readable medium of claim 6 , wherein generating said XML schema fragment comprises: identifying a first set of local element declarations or element references contained within said complex type definition to which said rendering option is applicable; identifying a second set of local element declarations or element references contained within one or more groups referenced by group references in said complex type definition to which said rendering option is applicable; and for each local element declaration or element reference in said first set and said second set, determining a target namespace of a current local element declaration or of a global element declaration referenced by a current element reference and generating an attribute declaration based on said determining. | 8. The computer readable medium of claim 6 , wherein generating said XML schema fragment comprises: identifying a first set of local element declarations or element references contained within said complex type definition to which said rendering option is applicable; identifying a second set of local element declarations or element references contained within one or more groups referenced by group references in said complex type definition to which said rendering option is applicable; and for each local element declaration or element reference in said first set and said second set, determining a target namespace of a current local element declaration or of a global element declaration referenced by a current element reference and generating an attribute declaration based on said determining. 9. The computer readable medium of claim 8 , wherein said generating said attribute declaration further comprises: if the determined target namespace of the current local element declaration or referenced global element declaration is the same as a target namespace of said complex type definition, or if the determined target namespace of the current local element declaration or referenced global element declaration is null and an “AttributeFormDefault” attribute of an XML schema containing said complex type definition has a value of “unqualified”, generating a local attribute declaration within said complex type definition having a type matching a determined type of said local element declaration or referenced global element declaration. | 0.623108 |
9,940,324 | 1 | 11 | 1. A method for evaluating performance of machine translation, the method comprising: receiving, by a machine translation device, a first document in a source language; translating, by a machine translation device, the first document in the source language to a second document in a target language, based, at least in part, on a first quantity of information, wherein the first quantity of information is obtained through, at least, preprocessing, before the translation, wherein the preprocessing includes, at least, tokenization; subsequent to the translation, performing, by a machine translation device, post processing to obtain a second quantity of information, wherein the post processing includes, at least, tokenization; evaluating, by a machine translation device, the second document in the target language, based, at least, on one or more aspects of the translation, the preprocessing, and the post processing, wherein the evaluation of the second document in the target language, based, at least, on the one or more aspects of the translation further comprises: comparing, by the machine translation device, the one or more aspects of the translation of the first document in the source language to the second document in the target language with a predetermined threshold, based, at least, on the first quantity of information and the second quantity of information; and determining, by the machine translation device, whether the comparison of the one or more aspects of the translation of the first document in the source language to the second document in the target language is greater than the predetermined threshold; and responsive to determining that the comparison of the one or more aspects of the translation of the first document in the source language to the second document in the target language is not greater than the predetermined threshold, translating, by the machine translation device, the first document to the second document, using available models, wherein the translation comprises: determining, by the machine translation device, that no new models are created and that no degradation has taken place on a translation performance; comparing, by the machine translation device, a first model used for a translation that failed to meet the predetermined threshold to a second model used for a translation that meets the predetermined threshold; and determining, by the machine translation device, that the first model and the second model exceeds a percentage of similarity, wherein the percentage of similarity is used to show that the first model and the second model are different and due to the difference, the second model is an improvement on the first model. | 1. A method for evaluating performance of machine translation, the method comprising: receiving, by a machine translation device, a first document in a source language; translating, by a machine translation device, the first document in the source language to a second document in a target language, based, at least in part, on a first quantity of information, wherein the first quantity of information is obtained through, at least, preprocessing, before the translation, wherein the preprocessing includes, at least, tokenization; subsequent to the translation, performing, by a machine translation device, post processing to obtain a second quantity of information, wherein the post processing includes, at least, tokenization; evaluating, by a machine translation device, the second document in the target language, based, at least, on one or more aspects of the translation, the preprocessing, and the post processing, wherein the evaluation of the second document in the target language, based, at least, on the one or more aspects of the translation further comprises: comparing, by the machine translation device, the one or more aspects of the translation of the first document in the source language to the second document in the target language with a predetermined threshold, based, at least, on the first quantity of information and the second quantity of information; and determining, by the machine translation device, whether the comparison of the one or more aspects of the translation of the first document in the source language to the second document in the target language is greater than the predetermined threshold; and responsive to determining that the comparison of the one or more aspects of the translation of the first document in the source language to the second document in the target language is not greater than the predetermined threshold, translating, by the machine translation device, the first document to the second document, using available models, wherein the translation comprises: determining, by the machine translation device, that no new models are created and that no degradation has taken place on a translation performance; comparing, by the machine translation device, a first model used for a translation that failed to meet the predetermined threshold to a second model used for a translation that meets the predetermined threshold; and determining, by the machine translation device, that the first model and the second model exceeds a percentage of similarity, wherein the percentage of similarity is used to show that the first model and the second model are different and due to the difference, the second model is an improvement on the first model. 11. The method of claim 1 , wherein the preprocessing includes, at least, tokenization, sentence alignment, stemming, named entity recognition, and lexical similarity recognition. | 0.819556 |
8,226,416 | 3 | 27 | 3. A non-transitory computer readable medium containing an executable program for recognizing an utterance spoken by a reader, where the program performs the steps of: receiving text comprising one or more words to be read by the reader; generating a grammar for speech recognition, in accordance with the text; inserting at least one reading learner grammar feature into the grammar, wherein the at least one reading learner grammar feature comprises a recognition of at least one reading learner mistake; receiving the utterance; interpreting the utterance in accordance with the grammar; and outputting feedback indicative of reader performance. | 3. A non-transitory computer readable medium containing an executable program for recognizing an utterance spoken by a reader, where the program performs the steps of: receiving text comprising one or more words to be read by the reader; generating a grammar for speech recognition, in accordance with the text; inserting at least one reading learner grammar feature into the grammar, wherein the at least one reading learner grammar feature comprises a recognition of at least one reading learner mistake; receiving the utterance; interpreting the utterance in accordance with the grammar; and outputting feedback indicative of reader performance. 27. The non-transitory computer readable medium of claim 3 , further comprising: storing the feedback. | 0.797619 |
8,195,036 | 8 | 13 | 8. An apparatus for reproducing multimedia image data and text-based subtitle data recorded on a storage medium, the apparatus comprising: a reading unit configured to read text-based subtitle data comprising a style set and dialog information, the style set comprising a plurality of style information, the style information comprising: area information indicating an output area of the subtitles; and font information for designating a font type and a font size, and the dialog information comprising: text information comprising subtitle contents; reference style information indicating one style information, of the plurality of style information, to be applied to the subtitle contents included in the dialog information; partial style information to be applied to a portion of the subtitle contents included in the dialog information; and beginning and ending time information for designating the times of the subtitle contents, and a controller unit configured to: apply the one style information, of the plurality of style information, indicated by the reference style information to the subtitle contents included in the dialog information; and apply the partial style information to the portion of the subtitle contents included in the dialog information. | 8. An apparatus for reproducing multimedia image data and text-based subtitle data recorded on a storage medium, the apparatus comprising: a reading unit configured to read text-based subtitle data comprising a style set and dialog information, the style set comprising a plurality of style information, the style information comprising: area information indicating an output area of the subtitles; and font information for designating a font type and a font size, and the dialog information comprising: text information comprising subtitle contents; reference style information indicating one style information, of the plurality of style information, to be applied to the subtitle contents included in the dialog information; partial style information to be applied to a portion of the subtitle contents included in the dialog information; and beginning and ending time information for designating the times of the subtitle contents, and a controller unit configured to: apply the one style information, of the plurality of style information, indicated by the reference style information to the subtitle contents included in the dialog information; and apply the partial style information to the portion of the subtitle contents included in the dialog information. 13. The apparatus as claimed in claim 8 , wherein each of the plurality of style information comprises a plurality of output style information to be applied to the text information at a same time; and the plurality of output style information of the style information indicated by the reference style information are to be applied to the text information at a same time, and the plurality of output style information of each of remaining ones of the plurality of style information are not to be used. | 0.5 |
8,079,046 | 16 | 17 | 16. A method of predicting items of media content likely to appeal to a user, based on the user's preferences, comprising: rating a new item based on earlier ratings of items by the user that correlate highly with the new item according to a first predictive algorithm when the new item has earlier ratings of items by the user that correlate highly with the new item, the first predictive algorithm comprising a collaborative filtering algorithm; rating the new item, when the new item has not been rated by the first predictive algorithm, according to a second predictive algorithm, the second algorithm comprising a content-based adaptive modeling algorithm, based on ratings of specific features by the user describing the item, wherein at least a portion of the features have previously been explicitly rated by the user; for a new item whose features have not been previously rated by the user, rating the new item based on previous explicit ratings of items by the user, according to the adaptive modeling algorithm, wherein the adaptive modeling algorithm generates implicit ratings of features based on the explicit ratings of items by the user; and allowing the user to correct a rating for an item generated by the first predictive algorithm or the second predictive algorithm in order to obtain generated item ratings more in line with what the user expects; wherein the method is performed by one or more computing devices. | 16. A method of predicting items of media content likely to appeal to a user, based on the user's preferences, comprising: rating a new item based on earlier ratings of items by the user that correlate highly with the new item according to a first predictive algorithm when the new item has earlier ratings of items by the user that correlate highly with the new item, the first predictive algorithm comprising a collaborative filtering algorithm; rating the new item, when the new item has not been rated by the first predictive algorithm, according to a second predictive algorithm, the second algorithm comprising a content-based adaptive modeling algorithm, based on ratings of specific features by the user describing the item, wherein at least a portion of the features have previously been explicitly rated by the user; for a new item whose features have not been previously rated by the user, rating the new item based on previous explicit ratings of items by the user, according to the adaptive modeling algorithm, wherein the adaptive modeling algorithm generates implicit ratings of features based on the explicit ratings of items by the user; and allowing the user to correct a rating for an item generated by the first predictive algorithm or the second predictive algorithm in order to obtain generated item ratings more in line with what the user expects; wherein the method is performed by one or more computing devices. 17. The method of claim 16 , wherein the collaborative filtering algorithm rates items according to correlation factors, so that an item with a high correlation factor to an item rated by the user is similarly rated. | 0.782696 |
9,348,807 | 12 | 13 | 12. The computer program product of claim 8 , wherein the conditional expression component includes a representation of the conditional expression in a hierarchical tree format. | 12. The computer program product of claim 8 , wherein the conditional expression component includes a representation of the conditional expression in a hierarchical tree format. 13. The computer program product of claim 12 , wherein the hierarchical tree format includes conditional expressions that must both be satisfied being depicted as connected by solid lines. | 0.522843 |
8,463,783 | 9 | 12 | 9. A system, comprising: a data store storing advertisement selection data for a set of advertisements, the selection data specifying selections of the advertisements from search results pages for search queries; an advertisement management system comprising one or more processors configured to receive specified text and provide advertisements that are relevant to the specified text based on targeting keywords for the advertisements matching the specified text; and an ad-selection analysis subsystem coupled to the data store and the advertisement management system, the ad-selection analysis subsystem including one or more processors configured to perform operations including: creating clusters of terms and corresponding advertisements based on the advertisement selection data, each of the clusters including multiple corresponding advertisements and each of the corresponding advertisements in each cluster having a term vector that is within a threshold distance of each other term vector for other corresponding advertisements in the cluster, each term vector for a corresponding advertisement specifying the search queries for which the corresponding advertisement was both presented to a user and selected by the user, the term vector also specifying advertiser-designated keywords for the corresponding advertisement that triggered presentations of the corresponding advertisement, wherein at least one of the advertiser-designated keywords is not included in the search queries, and wherein creating the clusters comprises determining cluster vectors for the clusters, each cluster vector for a respective cluster being an aggregate representation of term vectors for each of multiple corresponding advertisements in the respective cluster; computing similarity measures between pairs of the clusters, each similarity measure for a pair of clusters being based on a distance between a cluster vector for a first cluster of the pair and a cluster vector for a second cluster of the pair; receiving a request for data identified as relevant to specified text; in response to the request: identifying, from the clusters, a particular cluster that includes a term matching the specified text; identifying, from the clusters, a co-relevant cluster for the particular cluster, the co-relevant cluster being identified based on the computed similarity measure between the particular cluster and the co-relevant cluster meeting a threshold similarity measure, the co-relevant cluster being a different cluster than the clusters that include the term matching the specified text; and providing data identified as relevant to the specified text, the data specifying at least one additional advertisement that is relevant to the specified text, the additional advertisement being one of the corresponding advertisements from the co-relevant cluster. | 9. A system, comprising: a data store storing advertisement selection data for a set of advertisements, the selection data specifying selections of the advertisements from search results pages for search queries; an advertisement management system comprising one or more processors configured to receive specified text and provide advertisements that are relevant to the specified text based on targeting keywords for the advertisements matching the specified text; and an ad-selection analysis subsystem coupled to the data store and the advertisement management system, the ad-selection analysis subsystem including one or more processors configured to perform operations including: creating clusters of terms and corresponding advertisements based on the advertisement selection data, each of the clusters including multiple corresponding advertisements and each of the corresponding advertisements in each cluster having a term vector that is within a threshold distance of each other term vector for other corresponding advertisements in the cluster, each term vector for a corresponding advertisement specifying the search queries for which the corresponding advertisement was both presented to a user and selected by the user, the term vector also specifying advertiser-designated keywords for the corresponding advertisement that triggered presentations of the corresponding advertisement, wherein at least one of the advertiser-designated keywords is not included in the search queries, and wherein creating the clusters comprises determining cluster vectors for the clusters, each cluster vector for a respective cluster being an aggregate representation of term vectors for each of multiple corresponding advertisements in the respective cluster; computing similarity measures between pairs of the clusters, each similarity measure for a pair of clusters being based on a distance between a cluster vector for a first cluster of the pair and a cluster vector for a second cluster of the pair; receiving a request for data identified as relevant to specified text; in response to the request: identifying, from the clusters, a particular cluster that includes a term matching the specified text; identifying, from the clusters, a co-relevant cluster for the particular cluster, the co-relevant cluster being identified based on the computed similarity measure between the particular cluster and the co-relevant cluster meeting a threshold similarity measure, the co-relevant cluster being a different cluster than the clusters that include the term matching the specified text; and providing data identified as relevant to the specified text, the data specifying at least one additional advertisement that is relevant to the specified text, the additional advertisement being one of the corresponding advertisements from the co-relevant cluster. 12. The system of claim 9 , wherein the ad-selection analysis subsystem is further configured to perform operations including: receiving a request for relevant resource keywords for resource text; identifying, from the clusters, relevant resource keywords for the resource text, the relevant resource keywords including at least one relevant term from a first cluster that includes a term matching the resource text and at least one relevant term from a co-relevant cluster for the first cluster, the co-relevant cluster being identified based on the computed similarity measure between the first cluster and the co-relevant cluster for the first cluster; and providing data specifying the relevant terms in response to the request. | 0.5 |
8,620,659 | 11 | 13 | 11. The system of claim 1 , wherein the one or more processors are configured to: determine a most likely context for the natural language utterance; compare one or more text combinations against one or more grammar expression entries in a context description grammar to identify one or more contexts that completely or partially match the one or more text combinations; provide a relevance score for each of the identified matching contexts; select the matching context having a highest score as the most likely context for the natural language utterance, wherein the domain agent is associated with the selected context; communicate the request to the domain agent associated with the selected context; and generate a response to the request using content gathered as a result of the domain agent processing the request, wherein the response arranges the content in an order based on the relevance scores for the identified matching contexts. | 11. The system of claim 1 , wherein the one or more processors are configured to: determine a most likely context for the natural language utterance; compare one or more text combinations against one or more grammar expression entries in a context description grammar to identify one or more contexts that completely or partially match the one or more text combinations; provide a relevance score for each of the identified matching contexts; select the matching context having a highest score as the most likely context for the natural language utterance, wherein the domain agent is associated with the selected context; communicate the request to the domain agent associated with the selected context; and generate a response to the request using content gathered as a result of the domain agent processing the request, wherein the response arranges the content in an order based on the relevance scores for the identified matching contexts. 13. The system of claim 11 , wherein the one or more processors are configured to: determine a personality based on the identified matching contexts, the domain agent processing the request, or a user profile associated with the user; and format the response based on the personality. | 0.5 |
4,410,958 | 9 | 10 | 9. In a text processing system including a display screen, a memory, and printer the improvement comprising: means for supporting interactive text editing functions; means for displaying on said display screen an outlined miniature full page representation; and means for simultaneously displaying full size text immediately adjacent said miniature full page representation. | 9. In a text processing system including a display screen, a memory, and printer the improvement comprising: means for supporting interactive text editing functions; means for displaying on said display screen an outlined miniature full page representation; and means for simultaneously displaying full size text immediately adjacent said miniature full page representation. 10. The apparatus of claim 9 further including code indicia for determining from user input the page size to be represented and code for making said outlined miniature full page representations proportional thereto. | 0.5 |
9,830,317 | 15 | 21 | 15. A portable, real time voice translation system for use by use by a child of preschool or elementary school age, comprising: a translation system for use on a single unit, portable device having a processor and a memory, the translation system for use by use by a child of preschool or elementary school age and having a content state selector on the front of the device that includes simplified pictorial representations of a plurality of translatable content for easy selection by use by the child of preschool or elementary school age; a computer program that is operable for executing the following functions: accessing a multilanguage database embodied in a non-transitory computer readable storage medium including a plurality of phrase templates associated with a respective plurality of phrases; selecting a source language from a plurality of source languages and a destination language from a plurality of destination languages; inputting a source phrase; transmitting the source phrase to (i) a speech recognition module embodied in a non-transitory computer readable storage medium for converting the spoken source phrase into a recognized source phrase; (ii) a translation engine embodied in a non-transitory computer readable storage medium for translating the recognized source phrase in the selected source language into a destination phrase in a destination language selected from multiple languages, wherein the plurality of phrases in the multilanguage database include the destination phrase; and, (iii) a template look-up engine embodied in a non-transitory computer readable storage medium for finding the phrase template associated with the destination phrase from among the multiple languages; and, outputting the spoken translation in the selected destination language, the outputting pace being adjustable from a real-time translation to a slow and more easily assimilated translation pace for ease of use by the child of preschool or elementary school age, the real-time pace being no slower than 0.010 seconds; and, a graphical user interface on the front of the device that includes a text display of the source phrase and the destination phrase; wherein, the translation system having a total time between the input of the source phrase and output of the destination phrase that is no slower than the 0.010 seconds. | 15. A portable, real time voice translation system for use by use by a child of preschool or elementary school age, comprising: a translation system for use on a single unit, portable device having a processor and a memory, the translation system for use by use by a child of preschool or elementary school age and having a content state selector on the front of the device that includes simplified pictorial representations of a plurality of translatable content for easy selection by use by the child of preschool or elementary school age; a computer program that is operable for executing the following functions: accessing a multilanguage database embodied in a non-transitory computer readable storage medium including a plurality of phrase templates associated with a respective plurality of phrases; selecting a source language from a plurality of source languages and a destination language from a plurality of destination languages; inputting a source phrase; transmitting the source phrase to (i) a speech recognition module embodied in a non-transitory computer readable storage medium for converting the spoken source phrase into a recognized source phrase; (ii) a translation engine embodied in a non-transitory computer readable storage medium for translating the recognized source phrase in the selected source language into a destination phrase in a destination language selected from multiple languages, wherein the plurality of phrases in the multilanguage database include the destination phrase; and, (iii) a template look-up engine embodied in a non-transitory computer readable storage medium for finding the phrase template associated with the destination phrase from among the multiple languages; and, outputting the spoken translation in the selected destination language, the outputting pace being adjustable from a real-time translation to a slow and more easily assimilated translation pace for ease of use by the child of preschool or elementary school age, the real-time pace being no slower than 0.010 seconds; and, a graphical user interface on the front of the device that includes a text display of the source phrase and the destination phrase; wherein, the translation system having a total time between the input of the source phrase and output of the destination phrase that is no slower than the 0.010 seconds. 21. The system of claim 15 , wherein the executing includes accessing a plurality of phrase templates that represent languages that include at least a combination of English, French, and Spanish; English, Japanese, and Mandarin; English, French, and Portuguese; English, Russian, and Mandarin; English, Hindustani, and Japanese; or, English, Arabic, and Russian. | 0.752394 |
4,654,875 | 7 | 13 | 7. A system to achieve automatic language recognition, that comprises: means to receive the language as input in the form of structures or feature sets and to provide electrical signals derived from the structures or feature sets received, which electrical signals are electrical representations of symbols in the language so received; and analysis means connected to receive the electrical signals and operable to achieve automatic recognition of the individual symbols in the input information on the basis of the combination of a. channel characteristics in the form of probabilities of observing the particular symbol given that the true state of nature is each of the different possible letters; b. the probabilities of the particular symbol occurring serially with other recognized symbols that precede the symbol being analyzed; and c. lexical information in the form of acceptable structures represented as a graph structure. | 7. A system to achieve automatic language recognition, that comprises: means to receive the language as input in the form of structures or feature sets and to provide electrical signals derived from the structures or feature sets received, which electrical signals are electrical representations of symbols in the language so received; and analysis means connected to receive the electrical signals and operable to achieve automatic recognition of the individual symbols in the input information on the basis of the combination of a. channel characteristics in the form of probabilities of observing the particular symbol given that the true state of nature is each of the different possible letters; b. the probabilities of the particular symbol occurring serially with other recognized symbols that precede the symbol being analyzed; and c. lexical information in the form of acceptable structures represented as a graph structure. 13. A system according to claim 7 in which said analysis means employs the viterbi algorithm to establish the probability that particular signals are the electrical analog of a particular word and in which the lexical information is in the form of a trie structure that contains a dictionary of acceptable words and which is accessed each time the viterbi algorithm establishes said probability. | 0.5 |
9,116,979 | 1 | 9 | 1. A method implemented by a processor of a profiling system, the method comprising: based on a document corpus containing a plurality of documents, creating a topic set, at least some of the topics in the topic set being organized in a hierarchical structure which includes a plurality of topic levels including an upper topic level and a lower topic level, each topic in the lower topic level being a subtopic of at least one of the topics in the upper topic level; monitoring interest in a plurality of documents for a user to identify one or more documents-of-interest to the user; and based on the monitored interest for the user, creating an interest profile for the user by: determining a measure of topical interest for the user for at least one of the topics at the upper topic level and for a subtopic of that topic, the subtopic being at the lower topic level; and determining, by the processor, a per-topic heuristic feature profile for the user, the per-topic heuristic feature profile indicating non-content related features which affect a user's interest in a document by specifying, for at least one topic of the topic set, an effect of document type on the user's interest in documents associated with the at least one topic, wherein the document type is one or more of: a blog post, a micro-blog post, a comment, or a news article. | 1. A method implemented by a processor of a profiling system, the method comprising: based on a document corpus containing a plurality of documents, creating a topic set, at least some of the topics in the topic set being organized in a hierarchical structure which includes a plurality of topic levels including an upper topic level and a lower topic level, each topic in the lower topic level being a subtopic of at least one of the topics in the upper topic level; monitoring interest in a plurality of documents for a user to identify one or more documents-of-interest to the user; and based on the monitored interest for the user, creating an interest profile for the user by: determining a measure of topical interest for the user for at least one of the topics at the upper topic level and for a subtopic of that topic, the subtopic being at the lower topic level; and determining, by the processor, a per-topic heuristic feature profile for the user, the per-topic heuristic feature profile indicating non-content related features which affect a user's interest in a document by specifying, for at least one topic of the topic set, an effect of document type on the user's interest in documents associated with the at least one topic, wherein the document type is one or more of: a blog post, a micro-blog post, a comment, or a news article. 9. The method of claim 1 , further comprising: determining a measure of significance for the user for at least one of the topics at the upper topic level and for a subtopic of that topic, the subtopic being at the lower topic level. | 0.5 |
10,156,981 | 11 | 12 | 11. A text entry device, comprising: at least one processor; and a computer-readable storage device or memory storing computer-readable instructions that when executed by the at least one processor, cause the text entry device to perform a method of text entry, the instructions comprising: instructions that cause the text entry device to receive first input data comprising one or more words, the first input data being associated with an input scope of the text entry device, instructions that cause the text entry device to receive second input data comprising one or more characters, instructions that cause the text entry device to analyze the second input data and at least a portion of input history data, instructions that cause the text entry device to resize one or more target areas associated with one or more, but less than all, respective keys displayed on the display, wherein the resizing is based on one or more characters previously received in the second input data text, and instructions to provide at least one user-specific suggestion candidate for the second input data on a display coupled to the text entry device. | 11. A text entry device, comprising: at least one processor; and a computer-readable storage device or memory storing computer-readable instructions that when executed by the at least one processor, cause the text entry device to perform a method of text entry, the instructions comprising: instructions that cause the text entry device to receive first input data comprising one or more words, the first input data being associated with an input scope of the text entry device, instructions that cause the text entry device to receive second input data comprising one or more characters, instructions that cause the text entry device to analyze the second input data and at least a portion of input history data, instructions that cause the text entry device to resize one or more target areas associated with one or more, but less than all, respective keys displayed on the display, wherein the resizing is based on one or more characters previously received in the second input data text, and instructions to provide at least one user-specific suggestion candidate for the second input data on a display coupled to the text entry device. 12. The text entry device of claim 11 , wherein the resizing is based on a plurality of characters previously received in the second input data text. | 0.815136 |
8,811,742 | 19 | 20 | 19. The non-transitory computer readable storage medium of claim 17 , wherein the portion of the canonical document is an image segment of the canonical document. | 19. The non-transitory computer readable storage medium of claim 17 , wherein the portion of the canonical document is an image segment of the canonical document. 20. The non-transitory computer readable storage medium of claim 19 , wherein the image segment presented visually matches text and non-text elements of the visual query. | 0.5 |
7,822,625 | 1 | 3 | 1. A method for monitoring a health related condition of a user, comprising the steps of: (A) using a graphical user interface by a health care provider associated with said user to select at least one question about said health related condition of the user and a plurality of possible answers for each question; (B) merging said at least one question and said plurality of possible answers with health data of said user stored in a database accessible by a server, thereby customizing the at least one question by including the health data of said user stored in the database; (C) generating a computer program on said server based on the health data of said user merged with said at least one question and said plurality of possible answers, said computer program comprising commands to convert first electronic data into a digital voice that (i) asks said at least one question about the health related condition of the user and (ii) states said plurality of possible answers; (D) providing to said user a remotely programmable apparatus configured to execute said computer program; (E) transmitting said computer program to said apparatus via a communication network, wherein said apparatus is (i) connectable to said communication network, (ii) located distant from said server, and (iii) programmed to uniquely identify said user; and (F) receiving from said apparatus via said communication network a response for each question selected from said plurality of possible answers. | 1. A method for monitoring a health related condition of a user, comprising the steps of: (A) using a graphical user interface by a health care provider associated with said user to select at least one question about said health related condition of the user and a plurality of possible answers for each question; (B) merging said at least one question and said plurality of possible answers with health data of said user stored in a database accessible by a server, thereby customizing the at least one question by including the health data of said user stored in the database; (C) generating a computer program on said server based on the health data of said user merged with said at least one question and said plurality of possible answers, said computer program comprising commands to convert first electronic data into a digital voice that (i) asks said at least one question about the health related condition of the user and (ii) states said plurality of possible answers; (D) providing to said user a remotely programmable apparatus configured to execute said computer program; (E) transmitting said computer program to said apparatus via a communication network, wherein said apparatus is (i) connectable to said communication network, (ii) located distant from said server, and (iii) programmed to uniquely identify said user; and (F) receiving from said apparatus via said communication network a response for each question selected from said plurality of possible answers. 3. The method according to claim 1 , wherein said response from said user for each question comprises a button activation corresponding to one of said plurality of possible answers. | 0.664815 |
7,685,118 | 15 | 16 | 15. The method of claim 14 , wherein said expert knowledge base is a knowledge base of technical solutions extracted from a natural language document. | 15. The method of claim 14 , wherein said expert knowledge base is a knowledge base of technical solutions extracted from a natural language document. 16. The method of claim 15 , wherein said natural language document comprises at least one of a patent or an article. | 0.738839 |
7,783,972 | 1 | 10 | 1. A computer program product, tangibly stored on a computer-readable medium, for editing a consolidated document in one or more editing sessions of an edit chain in which one or more users edit and save successive versions of the consolidated document, the consolidated document comprising at least one component file converted from a native application format into and stored in a non-native application format different from the native application format, the non-native application format comprising a format accepted by an output processing system, the product comprising: means for storing a marker within the consolidated document, the marker indicating that the document was edited using an ensured workflow system; means for storing identifying information within the consolidated document sufficient to identify the at least one component file that was converted into the consolidated document, the identifying information comprising one or more of: file name, file type, relative path, absolute path, creation date, modification date, and checksum data; means for storing preflight information within the consolidated document, the preflight information identifying a preflight profile and parameters thereof to be used for preflighting the document; means for preflighting the edited document in accordance with the preflight profile; and means for storing an edit log within the consolidated document, the edit log comprising edit information corresponding to a history of edits made to the document in each editing session of the edit chain, the edit information comprising: a listing of one or more edits made during the session, user information relating to the user who conducted the edit, comment data optionally entered by the user relating to the session, and one or more preflight results, if preflighting has been performed. | 1. A computer program product, tangibly stored on a computer-readable medium, for editing a consolidated document in one or more editing sessions of an edit chain in which one or more users edit and save successive versions of the consolidated document, the consolidated document comprising at least one component file converted from a native application format into and stored in a non-native application format different from the native application format, the non-native application format comprising a format accepted by an output processing system, the product comprising: means for storing a marker within the consolidated document, the marker indicating that the document was edited using an ensured workflow system; means for storing identifying information within the consolidated document sufficient to identify the at least one component file that was converted into the consolidated document, the identifying information comprising one or more of: file name, file type, relative path, absolute path, creation date, modification date, and checksum data; means for storing preflight information within the consolidated document, the preflight information identifying a preflight profile and parameters thereof to be used for preflighting the document; means for preflighting the edited document in accordance with the preflight profile; and means for storing an edit log within the consolidated document, the edit log comprising edit information corresponding to a history of edits made to the document in each editing session of the edit chain, the edit information comprising: a listing of one or more edits made during the session, user information relating to the user who conducted the edit, comment data optionally entered by the user relating to the session, and one or more preflight results, if preflighting has been performed. 10. The product of claim 1 further comprising means to display the listing of one or more edits made during the session in an edit log. | 0.841176 |
7,707,210 | 1 | 2 | 1. A multi-dimensional foraging method comprising: generating a working set of documents from a collection of documents having dimensions, wherein a dimension of a document represents a characteristic of the document; selecting panels, each panel corresponding to a selected dimension or combination of dimensions of the documents; generating for each of the selected panels, sets of the documents in the working set, the generated document sets corresponding to the dimension or combination of dimensions of a corresponding panel; generating neighboring document sets from documents other than those of the working set, the neighboring document sets related to the document sets generated from the working set; arranging the document sets having a particular dimension or combination of dimensions in relationship to each other in a same panel; selecting document sets to be included in the working set, wherein those not selected are placed in a non-working set; displaying a recency panel that presents documents within the working set and the non-working set on a time line, wherein the time line indicates a date of creation for each document presented; displaying the panels having the arranged document set on a visual display device, wherein a multi-dimensional view of the working set is provided; generating, within at least some of the panels, a visual retrieval boundary between document sets within the working set and document sets in the non-working set, wherein the visual retrieval boundary distinguishes between document sets within the working set and the non-working set; adding at least one of a visual constraint or inclusion to at least one of the displayed panels; altering the working set and the non-working set and the visual retrieval boundary based on the adding of the at least one visual constraint or inclusion; and displaying the panels with the altered working set and the non-working set and retrieval boundary. | 1. A multi-dimensional foraging method comprising: generating a working set of documents from a collection of documents having dimensions, wherein a dimension of a document represents a characteristic of the document; selecting panels, each panel corresponding to a selected dimension or combination of dimensions of the documents; generating for each of the selected panels, sets of the documents in the working set, the generated document sets corresponding to the dimension or combination of dimensions of a corresponding panel; generating neighboring document sets from documents other than those of the working set, the neighboring document sets related to the document sets generated from the working set; arranging the document sets having a particular dimension or combination of dimensions in relationship to each other in a same panel; selecting document sets to be included in the working set, wherein those not selected are placed in a non-working set; displaying a recency panel that presents documents within the working set and the non-working set on a time line, wherein the time line indicates a date of creation for each document presented; displaying the panels having the arranged document set on a visual display device, wherein a multi-dimensional view of the working set is provided; generating, within at least some of the panels, a visual retrieval boundary between document sets within the working set and document sets in the non-working set, wherein the visual retrieval boundary distinguishes between document sets within the working set and the non-working set; adding at least one of a visual constraint or inclusion to at least one of the displayed panels; altering the working set and the non-working set and the visual retrieval boundary based on the adding of the at least one visual constraint or inclusion; and displaying the panels with the altered working set and the non-working set and retrieval boundary. 2. The method as set forth in claim 1 further including saving the retrieval boundary. | 0.896882 |
10,079,888 | 1 | 13 | 1. A method, comprising: distributing, by at least one computer server, a user interface application to at least one client device over a computer network, the user interface application configured for receiving user input selecting a type of a non-network object from a set of object types each characterized by a different location data type, obtaining corresponding location information for the non-network object according to its characteristic location data type at least in part from user input, sending the corresponding location information to the at least one computer server, and requesting assignment of a unique numeric address for the non-network object; receiving, by the at least one computer server, a new address request with the corresponding location information from the at least one client device generated by the user interface application, for each non-network object; determining, by the at least one computer server, a numeric address for the each non-network object based at least in part on its selected type that is not used as an address for any other object of an identical type; maintaining, by the at least one computer server, each numeric address in association with the corresponding location information for the each non-network object as a record in an on-line data structure holding different numeric addresses each associated with a different one of the corresponding location information; and serving, by the at least one computer server over the computer network, the corresponding location information for the each non-network object selected from the on-line data structure in response to at least one query containing the each numeric address. | 1. A method, comprising: distributing, by at least one computer server, a user interface application to at least one client device over a computer network, the user interface application configured for receiving user input selecting a type of a non-network object from a set of object types each characterized by a different location data type, obtaining corresponding location information for the non-network object according to its characteristic location data type at least in part from user input, sending the corresponding location information to the at least one computer server, and requesting assignment of a unique numeric address for the non-network object; receiving, by the at least one computer server, a new address request with the corresponding location information from the at least one client device generated by the user interface application, for each non-network object; determining, by the at least one computer server, a numeric address for the each non-network object based at least in part on its selected type that is not used as an address for any other object of an identical type; maintaining, by the at least one computer server, each numeric address in association with the corresponding location information for the each non-network object as a record in an on-line data structure holding different numeric addresses each associated with a different one of the corresponding location information; and serving, by the at least one computer server over the computer network, the corresponding location information for the each non-network object selected from the on-line data structure in response to at least one query containing the each numeric address. 13. The method of claim 1 , further comprising coordinating, by the at least one computer server, address assignment of two or more of the each non-network objects based on one or more of a hierarchical relationship or a peer relationship between the two or more of the each non-network objects. | 0.5 |
8,812,531 | 1 | 2 | 1. One or more computer-readable non-transitory storage media embodying software that is operable when executed by one or more computing devices to: derive a concept matrix from a plurality of sample documents using singular value decomposition of a term-document matrix, the concept matrix identifying a latent pattern of word usage in the plurality of sample documents around a concept; derive concept terms by extracting significant terms from search text and inferring relevant terms therefrom in accordance with the concept matrix; and generate a query comprising a search expression having at least one of the derived concept terms. | 1. One or more computer-readable non-transitory storage media embodying software that is operable when executed by one or more computing devices to: derive a concept matrix from a plurality of sample documents using singular value decomposition of a term-document matrix, the concept matrix identifying a latent pattern of word usage in the plurality of sample documents around a concept; derive concept terms by extracting significant terms from search text and inferring relevant terms therefrom in accordance with the concept matrix; and generate a query comprising a search expression having at least one of the derived concept terms. 2. The media of claim 1 , the software further operable when executed by the one or more computing devices to remove noise terms that are unrelated to said significant terms. | 0.516667 |
8,271,506 | 2 | 3 | 2. The method of claim 1 further comprising: generating, using the computing device, an entity information object for each the analysis of the first, second, third and fourth information objects such that each generated entity information object defines a point-of-view (POV) relationship between each of the information objects associated with the first RWE and the second RWE. | 2. The method of claim 1 further comprising: generating, using the computing device, an entity information object for each the analysis of the first, second, third and fourth information objects such that each generated entity information object defines a point-of-view (POV) relationship between each of the information objects associated with the first RWE and the second RWE. 3. The method of claim 2 further comprising storing, using the computing device, each POV relationship independently. | 0.781716 |
9,286,887 | 1 | 6 | 1. A method comprising: creating, via a processor, a correlation table based on speech input from a user, the correlation table comprising alternative character combinations of the speech input and comprising a non-alphanumeric character; generating a selection identifier based on the speech input, the selection identifier corresponding to a data element; comparing the alternative character combinations and the selection identifier to reference identifiers, to yield a matched identifier comprising a reference identifier corresponding to the speech input; and when the matched identifier comprises more than one reference identifier, voice prompting, by a voice prompt device, the user to select which of the more than one reference identifier corresponds to the speech input. | 1. A method comprising: creating, via a processor, a correlation table based on speech input from a user, the correlation table comprising alternative character combinations of the speech input and comprising a non-alphanumeric character; generating a selection identifier based on the speech input, the selection identifier corresponding to a data element; comparing the alternative character combinations and the selection identifier to reference identifiers, to yield a matched identifier comprising a reference identifier corresponding to the speech input; and when the matched identifier comprises more than one reference identifier, voice prompting, by a voice prompt device, the user to select which of the more than one reference identifier corresponds to the speech input. 6. The method of claim 1 , further comprising identifying the user based on the reference identifier. | 0.731383 |
9,430,468 | 1 | 8 | 1. An online journal recommendation system, comprising: one or more editorial computers connected to a multi-node network, said editorial computers comprising one or more program controlled data processors configured to: receive an author-submitted article for publication via said multi-node network; access journal database records, wherein said database records include information associated with previously submitted articles and corresponding author user profiles; create a first fingerprint of a plurality of published articles in a particular journal from the journal database records; create a second fingerprint of the author-submitted article; compare the first fingerprint with the second fingerprint to determine whether the particular journal has articles with a high similarity to the author-submitted article; recommend the particular journal to the author as a potential journal for submission of the author-submitted article when the particular journal has published articles which have a high similarity; and when the author-submitted article is rejected from the particular journal: receive a first input, from the author of the submitted article for publication, comprising a request to initiate a waterfall process for the rejected author-submitted article, provide a first notification of the first input to a receiving journal device, receive a confirmation to proceed from the receiving journal device, transform the rejected author-submitted article into a waterfalled article, transmit data comprising a submission to the receiving journal device, wherein the submission comprises the waterfalled article and metadata, receive a transmission from the receiving journal device, wherein the transmission comprises a rejection of the submission and an option to continue the waterfall process with a second receiving journal, and when an affirmation of the option to continue the waterfall process with the second receiving journal is received: transmit one or more journal recommendations to the author of the submitted article for publication, wherein the one or more journal recommendations comprise one or more potential receiving journals that contain articles having a high similarity with the waterfalled article, and receive a second input from the author of the submitted article for publication comprising a selection of the second receiving journal and forward the waterfalled article to the second selected journal. | 1. An online journal recommendation system, comprising: one or more editorial computers connected to a multi-node network, said editorial computers comprising one or more program controlled data processors configured to: receive an author-submitted article for publication via said multi-node network; access journal database records, wherein said database records include information associated with previously submitted articles and corresponding author user profiles; create a first fingerprint of a plurality of published articles in a particular journal from the journal database records; create a second fingerprint of the author-submitted article; compare the first fingerprint with the second fingerprint to determine whether the particular journal has articles with a high similarity to the author-submitted article; recommend the particular journal to the author as a potential journal for submission of the author-submitted article when the particular journal has published articles which have a high similarity; and when the author-submitted article is rejected from the particular journal: receive a first input, from the author of the submitted article for publication, comprising a request to initiate a waterfall process for the rejected author-submitted article, provide a first notification of the first input to a receiving journal device, receive a confirmation to proceed from the receiving journal device, transform the rejected author-submitted article into a waterfalled article, transmit data comprising a submission to the receiving journal device, wherein the submission comprises the waterfalled article and metadata, receive a transmission from the receiving journal device, wherein the transmission comprises a rejection of the submission and an option to continue the waterfall process with a second receiving journal, and when an affirmation of the option to continue the waterfall process with the second receiving journal is received: transmit one or more journal recommendations to the author of the submitted article for publication, wherein the one or more journal recommendations comprise one or more potential receiving journals that contain articles having a high similarity with the waterfalled article, and receive a second input from the author of the submitted article for publication comprising a selection of the second receiving journal and forward the waterfalled article to the second selected journal. 8. The online journal recommendation system of claim 1 , wherein annotations to the author-submitted article are made in the author-submitted article. | 0.642857 |
9,147,039 | 2 | 4 | 2. The hybrid electronic medical record system of claim 1 wherein the document generator further responds to a user selection of a ranked document to provide an associated predefined report via the database management engine. | 2. The hybrid electronic medical record system of claim 1 wherein the document generator further responds to a user selection of a ranked document to provide an associated predefined report via the database management engine. 4. The hybrid electronic medical record system of claim 2 wherein the associated predefined report includes user-activatable links to structured queries to provide different predefined reports collecting and organizing information from different fields of selected records. | 0.5 |
9,836,305 | 12 | 13 | 12. The computer-implemented method of claim 1 , wherein the determining, by the computing system, the binary representation of the document further comprises: pre-computing, by the computing system, an amount of memory needed for the document, wherein information indicating the amount of memory is stored in the binary representation of the document. | 12. The computer-implemented method of claim 1 , wherein the determining, by the computing system, the binary representation of the document further comprises: pre-computing, by the computing system, an amount of memory needed for the document, wherein information indicating the amount of memory is stored in the binary representation of the document. 13. The computer-implemented method of claim 12 , the method further comprising: allocating, by the computing system, memory prior to executing the parallelized code based at least in part on the information indicating the amount of memory needed for the document. | 0.5 |
9,472,189 | 1 | 7 | 1. A language processing method, comprising: receiving an user utterance as an input sequence of token elements; parsing, using a parsing processor, the input sequence of the token elements using a parsing algorithm in a first mode applying regular production rules on the token elements and on multi-token classifiers for phrases obtained from the token elements, wherein each token element contains a token of an input string and/or at least one corresponding token classifier; controlling, when the first mode parsing does not result in a multi-token phrase encompassing all tokens of the input string, the parsing processor to parse the input sequence using the parsing algorithm in a second mode applying both the regular and artificial production rules, wherein the second mode comprises generating the artificial production rules on the basis of the input sequence and intermediate results of the parsing using the parsing algorithm in the first mode, the intermediate results being based on lexical category of the token of the input string; and matching the parsed input sequence to one of a plurality of predefined machine commands to generate a command based on the input sequence, wherein the parsing algorithm comprises calculating probabilities for all possible subsequences and selecting, for each sequence of token classifiers within the input sequence, the subsequences with a highest probability, and each of the artificial production rules has a probability value lower than any of the regular production rules, the probability value being set such that at most one artificial production rule of the artificial production rules is used when parsing the input sequence using the parsing algorithm in the second mode, and the probability value being increased when the artificial production rule is successfully applied. | 1. A language processing method, comprising: receiving an user utterance as an input sequence of token elements; parsing, using a parsing processor, the input sequence of the token elements using a parsing algorithm in a first mode applying regular production rules on the token elements and on multi-token classifiers for phrases obtained from the token elements, wherein each token element contains a token of an input string and/or at least one corresponding token classifier; controlling, when the first mode parsing does not result in a multi-token phrase encompassing all tokens of the input string, the parsing processor to parse the input sequence using the parsing algorithm in a second mode applying both the regular and artificial production rules, wherein the second mode comprises generating the artificial production rules on the basis of the input sequence and intermediate results of the parsing using the parsing algorithm in the first mode, the intermediate results being based on lexical category of the token of the input string; and matching the parsed input sequence to one of a plurality of predefined machine commands to generate a command based on the input sequence, wherein the parsing algorithm comprises calculating probabilities for all possible subsequences and selecting, for each sequence of token classifiers within the input sequence, the subsequences with a highest probability, and each of the artificial production rules has a probability value lower than any of the regular production rules, the probability value being set such that at most one artificial production rule of the artificial production rules is used when parsing the input sequence using the parsing algorithm in the second mode, and the probability value being increased when the artificial production rule is successfully applied. 7. The method according to claim 1 , comprising managing information about the use of artificial production rules and redefining and using as a regular production rule at least one of the artificial production rules which is applied more often than other artificial production rules. | 0.742727 |
8,209,330 | 13 | 16 | 13. A system, comprising: a first data storage device storing relevance data for an image search result relative to a search query, the image search result referencing an image that has been identified as responsive to the search query based on a relevance score for the image search result, the relevance score being determined independent of a visual similarity of the image to other images that are referenced by other image search results that are identified as responsive to the search query; a second data storage device storing image similarity data that is indicative of a relative visual similarity between the image and the other images; and an image search subsystem comprising one or more processors coupled to each of the first data storage device and the second data storage device to generate an adjustment factor for the image search result relative to the search query based on the relevance data and image similarity data, the adjustment factor representing a quality measure for the image relative to the search query, the image search subsystem being further operable to: determine that the image search result is a co-relevant result, the co-relevant result being a responsive image search result for which: the relevance score meets a specified relevance score threshold; and the image similarity data indicates that the image referenced by the responsive image search result has a least a threshold level of visual similarity to a threshold number of the other images referenced by the image search results; and scaling the adjustment factor for the image search result by an amplification factor in response to determining that the image search result is a co-relevant result. | 13. A system, comprising: a first data storage device storing relevance data for an image search result relative to a search query, the image search result referencing an image that has been identified as responsive to the search query based on a relevance score for the image search result, the relevance score being determined independent of a visual similarity of the image to other images that are referenced by other image search results that are identified as responsive to the search query; a second data storage device storing image similarity data that is indicative of a relative visual similarity between the image and the other images; and an image search subsystem comprising one or more processors coupled to each of the first data storage device and the second data storage device to generate an adjustment factor for the image search result relative to the search query based on the relevance data and image similarity data, the adjustment factor representing a quality measure for the image relative to the search query, the image search subsystem being further operable to: determine that the image search result is a co-relevant result, the co-relevant result being a responsive image search result for which: the relevance score meets a specified relevance score threshold; and the image similarity data indicates that the image referenced by the responsive image search result has a least a threshold level of visual similarity to a threshold number of the other images referenced by the image search results; and scaling the adjustment factor for the image search result by an amplification factor in response to determining that the image search result is a co-relevant result. 16. The system of claim 13 , wherein the relevance data comprises user feedback specifying a relevance of the image to the search query. | 0.803468 |
7,496,496 | 7 | 8 | 7. The method of claim 1 wherein extracting features comprises: extracting a feature indicative of the translation device used. | 7. The method of claim 1 wherein extracting features comprises: extracting a feature indicative of the translation device used. 8. The method of claim 7 wherein extracting features comprises: extracting a feature indicative of an amount of the source string translated with another translation device. | 0.5 |
6,161,130 | 12 | 15 | 12. The method in claim 9 wherein the handcrafted features comprise features correspondingly related to formatting, authoring, delivery or communication attributes that characterize a message as belonging to the first class. | 12. The method in claim 9 wherein the handcrafted features comprise features correspondingly related to formatting, authoring, delivery or communication attributes that characterize a message as belonging to the first class. 15. The method in claim 12 wherein the authoring attributes comprise whether the incoming message contains an address of a single recipient, or contains addresses of plurality of recipients or contains no sender at all, or a time at which the incoming message was transmitted. | 0.5 |
10,032,191 | 36 | 37 | 36. The system of claim 35 : wherein a communication session is established between the sandboxed application and the sandbox reachable service by appending a header of a hypertext transfer protocol to permit the networked device to communicate with the sandboxed application as a permitted origin domain through a CORS algorithm, the header being either one of a origin header when the CORS algorithm is applied and a referrer header in an alternate algorithm, wherein the sandboxed application queries a MAC address of the sandbox reachable service in a common private network, wherein the sandbox reachable service optionally verifies that the sandboxed application is in the common private network, wherein the sandbox reachable service communicates a MAC address of the sandboxed application to the sandboxed application when the common private network is shared, wherein the sandboxed application stores the MAC address of the sandboxed application and a unique identifier derived from the MAC address of the sandboxed application, wherein the sandboxed application communicates the MAC address and the unique identifier to a pairing server of the system, and wherein the script embedded in the at least one of the client device, the supply-side platform, and the data provider integrated with the supply side platform is automatically regenerated when the common private network is shared by the sandboxed application and sandboxed reachable service based on the MAC address of the sandboxed application and the unique identifier communicated to the pairing server. | 36. The system of claim 35 : wherein a communication session is established between the sandboxed application and the sandbox reachable service by appending a header of a hypertext transfer protocol to permit the networked device to communicate with the sandboxed application as a permitted origin domain through a CORS algorithm, the header being either one of a origin header when the CORS algorithm is applied and a referrer header in an alternate algorithm, wherein the sandboxed application queries a MAC address of the sandbox reachable service in a common private network, wherein the sandbox reachable service optionally verifies that the sandboxed application is in the common private network, wherein the sandbox reachable service communicates a MAC address of the sandboxed application to the sandboxed application when the common private network is shared, wherein the sandboxed application stores the MAC address of the sandboxed application and a unique identifier derived from the MAC address of the sandboxed application, wherein the sandboxed application communicates the MAC address and the unique identifier to a pairing server of the system, and wherein the script embedded in the at least one of the client device, the supply-side platform, and the data provider integrated with the supply side platform is automatically regenerated when the common private network is shared by the sandboxed application and sandboxed reachable service based on the MAC address of the sandboxed application and the unique identifier communicated to the pairing server. 37. The system of claim 36 , wherein the client device is configured to: extend the security sandbox with a discovery algorithm and a relay algorithm through a discovery module and a relay module added to the security sandbox, and bypass the pairing server having the discovery algorithm and the relay algorithm when establishing the communication session between the sandboxed application and the sandbox reachable service when the security sandbox is extended with the discovery algorithm and the relay algorithm through the discovery module and the relay module added to the security sandbox. | 0.692347 |
9,530,097 | 1 | 8 | 1. An associative relevancy knowledge profiling system, comprising: at least one user interface module configured to: receive user input that represents a user's intention to create an association between reference sets; a known reference set module configured to provide at least two available known reference sets and to permit selection of at least two said available known reference sets and an association type by a selective user input received by said user interface module, wherein the association type defines a type of association between the at least two said available known reference sets; an association module, implemented by a machine, configured: in response to the at least one user interface module receiving the user input, to select the association type from a plurality of association types, and to create an association between two of said available known reference sets, the association being of the selected association type, to generate a knowledge construct based upon said association and said available known reference sets, and to generate a profile comprising said association between two of said available known reference sets; and a storage module configured to store said profile and to provide said profile for subsequent use. | 1. An associative relevancy knowledge profiling system, comprising: at least one user interface module configured to: receive user input that represents a user's intention to create an association between reference sets; a known reference set module configured to provide at least two available known reference sets and to permit selection of at least two said available known reference sets and an association type by a selective user input received by said user interface module, wherein the association type defines a type of association between the at least two said available known reference sets; an association module, implemented by a machine, configured: in response to the at least one user interface module receiving the user input, to select the association type from a plurality of association types, and to create an association between two of said available known reference sets, the association being of the selected association type, to generate a knowledge construct based upon said association and said available known reference sets, and to generate a profile comprising said association between two of said available known reference sets; and a storage module configured to store said profile and to provide said profile for subsequent use. 8. The system of claim 1 , further comprising a preferencing module configured to modify said profile to impose a viewing preference upon any use of said profile based upon a preference user input received by said user interface module. | 0.636923 |
7,707,561 | 22 | 27 | 22. A computer-readable storage medium tangibly embodying a program of machine-readable instructions executable by a digital processing apparatus to perform an operation to convert data, the operation comprising: parsing a data file having one or more data lines; formatting the data file such that the formatted data file can be translated by a translator, the formatted data file comprising one or more computer language instructions; translating the formatted data file with a translator, the translator translating the formatted data file into one of object code, assembly language, and machine language; outputting the formatted data file as an output file; and packaging the translated output file as a searchable mainframe load module, the searchable mainframe load module compatible with a load library operating on a legacy computer system executing a mainframe operating system, the load library configured to return a file without a complete path name in response to a load command. | 22. A computer-readable storage medium tangibly embodying a program of machine-readable instructions executable by a digital processing apparatus to perform an operation to convert data, the operation comprising: parsing a data file having one or more data lines; formatting the data file such that the formatted data file can be translated by a translator, the formatted data file comprising one or more computer language instructions; translating the formatted data file with a translator, the translator translating the formatted data file into one of object code, assembly language, and machine language; outputting the formatted data file as an output file; and packaging the translated output file as a searchable mainframe load module, the searchable mainframe load module compatible with a load library operating on a legacy computer system executing a mainframe operating system, the load library configured to return a file without a complete path name in response to a load command. 27. The computer-readable storage medium of claim 22 , wherein the translator comprises an assembler for translating the formatted data file into assembly language. | 0.635556 |
7,644,377 | 19 | 23 | 19. A computer-readable storage medium that when executed cause a computer to: provide models of corresponding components, wherein the models contain constraints, wherein the models specify a first relationship in which at least one of the components is composed of at least another one of the components, and wherein the models specify a second relationship among components in addition to the first relationship, wherein the second relationship comprises at least one of a reference relationship and an association relationship; input the models into a design tool; and invoke the design tool to generate a configuration of the system that includes the components, wherein the generated configuration satisfies the constraints contained in the models. | 19. A computer-readable storage medium that when executed cause a computer to: provide models of corresponding components, wherein the models contain constraints, wherein the models specify a first relationship in which at least one of the components is composed of at least another one of the components, and wherein the models specify a second relationship among components in addition to the first relationship, wherein the second relationship comprises at least one of a reference relationship and an association relationship; input the models into a design tool; and invoke the design tool to generate a configuration of the system that includes the components, wherein the generated configuration satisfies the constraints contained in the models. 23. The computer-readable storage medium of claim 19 , wherein a first of the models specifies that a first of the components is composed of a second of the components, and a second of the models specifies that a third of the components is composed of a fourth of the components. | 0.709375 |
7,809,548 | 28 | 29 | 28. The method of claim 22 , wherein determining at least one keyword based on the plurality of text units and the plurality of rankings comprises sorting the graph nodes based upon the plurality of rankings. | 28. The method of claim 22 , wherein determining at least one keyword based on the plurality of text units and the plurality of rankings comprises sorting the graph nodes based upon the plurality of rankings. 29. The method of claim 28 , wherein determining the at least one keyword comprises selecting at least one keyword based upon the ranking of the graph nodes. | 0.5 |
9,298,699 | 8 | 14 | 8. One or more non-transitory computer-readable media maintaining instructions that, when executed by one or more processors, cause the one or more processors to perform operations comprising: determining a character identity in a work; determining a voice for the character identity, wherein the voice is associated with indicators within the work that suggest at least one of: a place that the character identity is from, a tribe of the character identity, a caste of the character identity, a dialect of the character identity, an education level of the character identity, an approximate age of the character identity, an ethnicity of the character identity, an era in which the character identity exists, or a socioeconomic class of the character identity; arranging, into a script, the work such that an indicator of the character identity is followed by a portion of text associated with the character identity; and using the voice to audibly present a portion of the work, wherein the portion is associated with the character identity. | 8. One or more non-transitory computer-readable media maintaining instructions that, when executed by one or more processors, cause the one or more processors to perform operations comprising: determining a character identity in a work; determining a voice for the character identity, wherein the voice is associated with indicators within the work that suggest at least one of: a place that the character identity is from, a tribe of the character identity, a caste of the character identity, a dialect of the character identity, an education level of the character identity, an approximate age of the character identity, an ethnicity of the character identity, an era in which the character identity exists, or a socioeconomic class of the character identity; arranging, into a script, the work such that an indicator of the character identity is followed by a portion of text associated with the character identity; and using the voice to audibly present a portion of the work, wherein the portion is associated with the character identity. 14. The one or more non-transitory computer-readable media of claim 8 , wherein the determining the voice for the character identity includes determining a pitch at which the voice is to be audibly presented. | 0.516279 |
8,717,367 | 1 | 14 | 1. In a system comprising one or more computing devices, a method for automatically generating an audiovisual work, the method comprising: inferentially selecting one or more design animation modules from among a plurality of design animation modules to use to generate an audiovisual work based at least upon all of: (a) one or more first metadata values reflecting one or more detected visual characteristics of at least one of one or more digital visual media items, (b) one or more second metadata values reflecting one or more detected audio characteristics of at least one of one or more digital audio media items and comprising beat timing information pertaining to the at least one digital audio media item, and (c) one or more third metadata values associated with at least one of the plurality of design animation modules and comprising beat timing information pertaining to the at least one design animation module; wherein each design animation module of the plurality of design animation modules is an independent interchangeable unit that can be combined with other design animation modules of the plurality of design animation modules to form different audiovisual works; wherein the one or more selected design animation modules comprises the at least one design animation module; wherein inferentially selecting the at least one design animation module comprises comparing the beat timing information of the one or more second metadata values pertaining to the at least one digital audio media item to the beat timing information of the one or more third metadata values pertaining to the at least one design animation module; wherein the at least one design animation modules comprises a specification of an animation scene; assigning the at least one digital visual media item to the at least one design animation module including incorporating the at least one digital visual media item into the specification of the animation scene; and generating the audiovisual work using the one or more selected design animation modules, the one or more digital visual media items, and the one or more digital audio media items; wherein the method is performed by the one or more computing devices. | 1. In a system comprising one or more computing devices, a method for automatically generating an audiovisual work, the method comprising: inferentially selecting one or more design animation modules from among a plurality of design animation modules to use to generate an audiovisual work based at least upon all of: (a) one or more first metadata values reflecting one or more detected visual characteristics of at least one of one or more digital visual media items, (b) one or more second metadata values reflecting one or more detected audio characteristics of at least one of one or more digital audio media items and comprising beat timing information pertaining to the at least one digital audio media item, and (c) one or more third metadata values associated with at least one of the plurality of design animation modules and comprising beat timing information pertaining to the at least one design animation module; wherein each design animation module of the plurality of design animation modules is an independent interchangeable unit that can be combined with other design animation modules of the plurality of design animation modules to form different audiovisual works; wherein the one or more selected design animation modules comprises the at least one design animation module; wherein inferentially selecting the at least one design animation module comprises comparing the beat timing information of the one or more second metadata values pertaining to the at least one digital audio media item to the beat timing information of the one or more third metadata values pertaining to the at least one design animation module; wherein the at least one design animation modules comprises a specification of an animation scene; assigning the at least one digital visual media item to the at least one design animation module including incorporating the at least one digital visual media item into the specification of the animation scene; and generating the audiovisual work using the one or more selected design animation modules, the one or more digital visual media items, and the one or more digital audio media items; wherein the method is performed by the one or more computing devices. 14. The method of claim 1 , wherein at least one of the one or more digital visual media items is a digital video. | 0.934028 |
7,917,460 | 1 | 9 | 1. A system for generating a decision network from a plurality of computer readable text documents, the system comprising: memory that stores a plurality of computer readable text documents; and a processing unit for accessing the memory and for executing computer executable instructions, the computer executable instructions comprising: a preprocessing portion configured to reduce a given computer readable text document of the plurality of computer readable text documents into one or more text segments; an information extractor configured to: identify predefined hedge words and predefined qualifier words in the one or more text segments relating to one or more words in the one or more text segments, wherein the one or more words comprise at least one of a noun, a pronoun and a verb; and assign a confidence value to each identified hedge word and qualifying word that represent a degree of belief or disbelief for the related one or more words; an evidence classifier configured to associate each of the one or more text segments with one of a plurality of hypotheses; a fusion engine configured to build a decision network from the plurality of hypotheses that define nodes in the decision network, an associated predefined hierarchical structure for the decision network, the identified hedge words and qualifying words, their related one or more words, and the assigned confidence values; a knowledge base that stores information relating to the plurality of hypotheses, the evidence classifier being operative to add a new hypothesis to the knowledge base in response to identification of hedge words and qualifier words in the one or more text segments; and a user interface that displays the generated decision network to a user. | 1. A system for generating a decision network from a plurality of computer readable text documents, the system comprising: memory that stores a plurality of computer readable text documents; and a processing unit for accessing the memory and for executing computer executable instructions, the computer executable instructions comprising: a preprocessing portion configured to reduce a given computer readable text document of the plurality of computer readable text documents into one or more text segments; an information extractor configured to: identify predefined hedge words and predefined qualifier words in the one or more text segments relating to one or more words in the one or more text segments, wherein the one or more words comprise at least one of a noun, a pronoun and a verb; and assign a confidence value to each identified hedge word and qualifying word that represent a degree of belief or disbelief for the related one or more words; an evidence classifier configured to associate each of the one or more text segments with one of a plurality of hypotheses; a fusion engine configured to build a decision network from the plurality of hypotheses that define nodes in the decision network, an associated predefined hierarchical structure for the decision network, the identified hedge words and qualifying words, their related one or more words, and the assigned confidence values; a knowledge base that stores information relating to the plurality of hypotheses, the evidence classifier being operative to add a new hypothesis to the knowledge base in response to identification of hedge words and qualifier words in the one or more text segments; and a user interface that displays the generated decision network to a user. 9. The system of claim 1 , wherein the confidence values are assigned according to a set of predefined confidence values, the predefined confidence values are based on a representation of human perception of the confidence expressed by each of a plurality of recognized hedge words and qualifying words. | 0.549107 |
9,424,841 | 16 | 17 | 16. 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, by a computing device, audio data that corresponds to an utterance; determining that the utterance likely includes a particular, predefined hotword; in response to determining that the utterance likely includes the particular, predefined hotword, determining score that reflects a loudness of the audio data; determining a duration of a delay period, wherein the duration of the delay period is inversely proportional to the loudness of the audio data; activating a mode in which the computing device temporarily listens, for the duration of the delay period, for a predetermined audio signal that indicates that another computing device is commencing speech recognition processing on the audio data; after the duration of the delay period has elapsed without hearing the predetermined audio signal from another computing device, deactivating the mode and transmitting the predetermined audio signal that indicates that the computing device is commencing speech recognition processing on the audio data; and after transmitting the predetermined audio signal, processing at least a portion of the audio data using an automated speech recognizer on the computing device. | 16. 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, by a computing device, audio data that corresponds to an utterance; determining that the utterance likely includes a particular, predefined hotword; in response to determining that the utterance likely includes the particular, predefined hotword, determining score that reflects a loudness of the audio data; determining a duration of a delay period, wherein the duration of the delay period is inversely proportional to the loudness of the audio data; activating a mode in which the computing device temporarily listens, for the duration of the delay period, for a predetermined audio signal that indicates that another computing device is commencing speech recognition processing on the audio data; after the duration of the delay period has elapsed without hearing the predetermined audio signal from another computing device, deactivating the mode and transmitting the predetermined audio signal that indicates that the computing device is commencing speech recognition processing on the audio data; and after transmitting the predetermined audio signal, processing at least a portion of the audio data using an automated speech recognizer on the computing device. 17. The medium of claim 16 , wherein the operations further comprise: receiving, by the computing device, additional audio data that corresponds to an additional utterance; determining that the additional utterance likely includes the particular, predefined hotword; in response to determining that the additional utterance likely includes the particular, predefined hotword, determining a second score that reflects a second loudness of the additional audio data; determining a second duration of a second delay period, wherein the second duration of the second delay period is inversely proportional to the second loudness of the additional audio data; activating the mode in which the computing device temporarily listens, for the second duration of the second delay period, for the predetermined audio signal that indicates that the other computing device is commencing speech recognition processing on the additional audio data; before the second duration of a second delay period has elapsed, receiving the predetermined audio signal that indicates that the other computing device is commencing speech recognition processing on the additional audio data; and in response to receiving the predetermined audio signal; deactivating processing of the additional audio data. | 0.5 |
8,495,742 | 1 | 2 | 1. A method of identifying malicious queries, comprising: applying a set of seed malicious queries to search logs to extract an Internet protocol (IP) address associated with a query in the search logs that matches one of the set of seed malicious queries; creating an expanded malicious query set by applying the IP address to the search logs to identify other queries submitted from the IP address; capturing variations of queries in the expanded malicious query set that are contained in the search logs; extracting an enlarged set of malicious queries from the search logs; and outputting the enlarged set of malicious queries and the IP address. | 1. A method of identifying malicious queries, comprising: applying a set of seed malicious queries to search logs to extract an Internet protocol (IP) address associated with a query in the search logs that matches one of the set of seed malicious queries; creating an expanded malicious query set by applying the IP address to the search logs to identify other queries submitted from the IP address; capturing variations of queries in the expanded malicious query set that are contained in the search logs; extracting an enlarged set of malicious queries from the search logs; and outputting the enlarged set of malicious queries and the IP address. 2. The method of claim 1 , further comprising: generating regular expressions at a regular expression generator; extracting malicious queries from the search logs that match the regular expressions to determine the enlarged set of malicious queries; and outputting the regular expressions. | 0.5 |
9,934,430 | 1 | 5 | 1. A non-transitory computer-readable media having instructions stored thereon, the instructions, when executed by one or more processors, cause the processors to perform operations comprising: training a multi-script handwriting recognition model based on spatially-derived features of a multi-script training corpus, the multi-script training corpus including respective handwriting samples corresponding to characters of one or more scripts; receiving a handwriting input from a user which includes characters from a plurality of scripts; and in response to receiving the handwriting input, providing real-time handwriting recognition output for the handwriting input using the multi-script handwriting recognition model that has been trained on the spatially-derived features of the multi-script training corpus, wherein the real-time handwriting recognition output includes characters from a plurality of scripts. | 1. A non-transitory computer-readable media having instructions stored thereon, the instructions, when executed by one or more processors, cause the processors to perform operations comprising: training a multi-script handwriting recognition model based on spatially-derived features of a multi-script training corpus, the multi-script training corpus including respective handwriting samples corresponding to characters of one or more scripts; receiving a handwriting input from a user which includes characters from a plurality of scripts; and in response to receiving the handwriting input, providing real-time handwriting recognition output for the handwriting input using the multi-script handwriting recognition model that has been trained on the spatially-derived features of the multi-script training corpus, wherein the real-time handwriting recognition output includes characters from a plurality of scripts. 5. The media of claim 1 , wherein the one or more scripts include one or more Chinese characters, Arabic script, and Latin script. | 0.882671 |
8,886,624 | 9 | 10 | 9. The system of claim 1 , wherein the processor further comprises: an indicator module configured to generate at least one indicator by indicating a property and an association of the associated keyword or the extended keyword provided by the search module; an indicator selecting module configured to select an indicator from the at least one indicator as a weight indicator, the selection is based on a purpose of usage of the associated keyword or the extended keyword; a weight correcting module configured to apply a weight to the weight indicator in order to change an indicator value of the weight indicator; and a simulation module configured to assess the keyword based on the weight indicator and the indicator, wherein the search module is configured to provide a keyword based on the assessment of the keyword. | 9. The system of claim 1 , wherein the processor further comprises: an indicator module configured to generate at least one indicator by indicating a property and an association of the associated keyword or the extended keyword provided by the search module; an indicator selecting module configured to select an indicator from the at least one indicator as a weight indicator, the selection is based on a purpose of usage of the associated keyword or the extended keyword; a weight correcting module configured to apply a weight to the weight indicator in order to change an indicator value of the weight indicator; and a simulation module configured to assess the keyword based on the weight indicator and the indicator, wherein the search module is configured to provide a keyword based on the assessment of the keyword. 10. The system of claim 9 , wherein the indicator selecting module is configured to identify an objective corresponding to the purpose of usage of the keyword, set a directive that includes a set of conditions for identifying the purpose of usage of the keyword based on the objective, and select a directive indicator from the at least one indicator based on the directive. | 0.661232 |
7,519,589 | 116 | 124 | 116. The method of claim 115 , further comprising: defining a findings folder containing only items whose content is determined by a user to relate to a specific topic, and to providing a definitive conclusion regarding the specific topic. | 116. The method of claim 115 , further comprising: defining a findings folder containing only items whose content is determined by a user to relate to a specific topic, and to providing a definitive conclusion regarding the specific topic. 124. The method of claim 116 , further comprising: profiling the findings folder based on the attributes of the items in the findings folder. | 0.711066 |
8,180,788 | 15 | 18 | 15. The image search server of claim 12 , the program of instructions further comprising an image listing module. | 15. The image search server of claim 12 , the program of instructions further comprising an image listing module. 18. The image search server of claim 15 , wherein the image listing module is configured to segregate images selected into image pages comprising first few of the selected images using the search string and first few of the selected images using the search image. | 0.5 |
9,767,091 | 6 | 11 | 6. A computer implemented method, comprising: receiving input from a client device, the user input comprising an incomplete natural language expression, wherein the incomplete natural language expression is a string of characters with one or more missing characters; extracting one or more n-grams from the incomplete natural language expression; analyzing the extracted one or more n-grams to determine a set of possible domains; assigning a confidence level to each possible domain of the set of possible domains, wherein the confidence level for at least one possible domain exceeds a predetermined confidence threshold; predicting an intent of a user associated with the at least one possible domain; initiating at least one domain application for performing the predicted intent; predicting at least one slot for executing at least one function; and receiving a selection, wherein the received selection indicates confirmation that the at least one function reflects an actual intent of the user; training a natural language analysis component based on at least the received selection indicating confirmation and the contextual information. | 6. A computer implemented method, comprising: receiving input from a client device, the user input comprising an incomplete natural language expression, wherein the incomplete natural language expression is a string of characters with one or more missing characters; extracting one or more n-grams from the incomplete natural language expression; analyzing the extracted one or more n-grams to determine a set of possible domains; assigning a confidence level to each possible domain of the set of possible domains, wherein the confidence level for at least one possible domain exceeds a predetermined confidence threshold; predicting an intent of a user associated with the at least one possible domain; initiating at least one domain application for performing the predicted intent; predicting at least one slot for executing at least one function; and receiving a selection, wherein the received selection indicates confirmation that the at least one function reflects an actual intent of the user; training a natural language analysis component based on at least the received selection indicating confirmation and the contextual information. 11. The method of claim 6 , wherein the set of possible domains includes a calendar domain, an alarm domain, and a travel domain. | 0.724359 |
7,831,607 | 1 | 5 | 1. An integrated circuit chip comprising programmable intelligent search memory for content search wherein said programmable intelligent search memory performs regular expression based search and wherein said regular expression comprises an interval symbol, said interval symbol comprises a symbol and further comprising a) a lower limit; b) an upper limit; or c) a combination thereof; the programmable intelligent search memory using one or more regular expressions, said one or more regular expressions comprising one or more symbols or characters, said one or more regular expressions further comprising one or more of said interval symbols, said one or more regular expressions converted into one or more finite state automata representing the functionality of said one or more regular expressions for programming in said programmable intelligent search memory, said one or more finite state automata comprising a plurality of states, said plurality of states derived from said one or more symbols or characters or said one or more interval symbols of said one or more regular expressions, said content comprising one or more input symbols provided as input to said programmable intelligent search memory, said programmable intelligent search memory comprising at least one of each of: a. a symbol memory circuit to store said one or more symbols; b. an interval symbol memory circuit to store said one or more interval symbols; c. an interval symbol evaluation circuit coupled to said interval symbol memory circuit to evaluate a match of said one or more interval symbols stored in said interval symbol memory circuit with said one or more symbols of said content; d. a symbol evaluation circuit coupled to said symbol memory circuit to evaluate a match of said one or more symbols stored in said symbol memory circuit with said one or more symbols of said content; e. a state dependent vector (SDV) memory circuit to store state transition controls for said one or more finite state automata; f. a current state vector memory (CSV) circuit to store said plurality of states; and g. a state transition circuit coupled to said symbol evaluation circuit, said interval symbol evaluation circuit, said current state vector memory circuit and said state dependent vector memory circuit to perform state transition from one or more first states to one or more second states of said plurality of states of said one or more finite state automata. | 1. An integrated circuit chip comprising programmable intelligent search memory for content search wherein said programmable intelligent search memory performs regular expression based search and wherein said regular expression comprises an interval symbol, said interval symbol comprises a symbol and further comprising a) a lower limit; b) an upper limit; or c) a combination thereof; the programmable intelligent search memory using one or more regular expressions, said one or more regular expressions comprising one or more symbols or characters, said one or more regular expressions further comprising one or more of said interval symbols, said one or more regular expressions converted into one or more finite state automata representing the functionality of said one or more regular expressions for programming in said programmable intelligent search memory, said one or more finite state automata comprising a plurality of states, said plurality of states derived from said one or more symbols or characters or said one or more interval symbols of said one or more regular expressions, said content comprising one or more input symbols provided as input to said programmable intelligent search memory, said programmable intelligent search memory comprising at least one of each of: a. a symbol memory circuit to store said one or more symbols; b. an interval symbol memory circuit to store said one or more interval symbols; c. an interval symbol evaluation circuit coupled to said interval symbol memory circuit to evaluate a match of said one or more interval symbols stored in said interval symbol memory circuit with said one or more symbols of said content; d. a symbol evaluation circuit coupled to said symbol memory circuit to evaluate a match of said one or more symbols stored in said symbol memory circuit with said one or more symbols of said content; e. a state dependent vector (SDV) memory circuit to store state transition controls for said one or more finite state automata; f. a current state vector memory (CSV) circuit to store said plurality of states; and g. a state transition circuit coupled to said symbol evaluation circuit, said interval symbol evaluation circuit, said current state vector memory circuit and said state dependent vector memory circuit to perform state transition from one or more first states to one or more second states of said plurality of states of said one or more finite state automata. 5. The integrated circuit chip of claim 1 further comprising circuits to couple said programmable intelligent search memory to at least one functional block or circuit or a combination, said at least one functional block or circuit or combination comprises a microprocessor, multi-core processor, network processor, graphics processor, switch processor, microcontroller, TCP Offload Engine, network packet classification engine, protocol processor, regular expression processor, security processor, content search processor, network attached storage processor, storage area network processor, wireless processor, mainframe computer, grid computer, server, workstation, personal computer, laptop, handheld device, cellular phone, wired or wireless networked device, switch, router, gateway, chipset, unified threat management device, and the like or any derivatives thereof or any combination thereof. | 0.679943 |
9,313,232 | 1 | 8 | 1. One or more non-transitory machine-readable storage media that includes code for execution and when executed by a processor is operable to perform operations comprising: receiving a plurality of parameters based, at least in part, on metadata information obtained from data mining one or more databases, at least one database containing tags associated with objects, wherein the data mining is to apply one or more filters to the tags of the at least one database to obtain at least a portion of the metadata information; creating a rule associated with at least some of the parameters; and incorporating the rule into a security policy to be used by a capture system, wherein the security policy is to control network communications captured by the capture system. | 1. One or more non-transitory machine-readable storage media that includes code for execution and when executed by a processor is operable to perform operations comprising: receiving a plurality of parameters based, at least in part, on metadata information obtained from data mining one or more databases, at least one database containing tags associated with objects, wherein the data mining is to apply one or more filters to the tags of the at least one database to obtain at least a portion of the metadata information; creating a rule associated with at least some of the parameters; and incorporating the rule into a security policy to be used by a capture system, wherein the security policy is to control network communications captured by the capture system. 8. The one or more non-transitory machine-readable storage media of claim 1 , the operations further comprising: running the rule against one or more databases to determine if the rule provides a targeted result. | 0.659164 |
9,190,063 | 13 | 24 | 13. A method of performing recognition of a speech utterance from a user with a distributed client-server system comprising: receiving user speech data from a client device in streaming packets through a network interface of a network server system employing an application level Internet based protocol overlaid on transmission control protocol (TCP) such that said streaming packets are processed as they are received, said speech data resulting from a first set of speech recognition operations being performed on the speech utterance by a client device; recognizing the speech utterance as well as a natural language used in said speech utterance using processing routines executing at said network server system which implement a second set of speech recognition operations, wherein recognizing includes converting the speech utterance into text using a Hidden Markov Modeling technique; sending text corresponding to the speech utterance to a natural language engine and a database engine; performing linguistic processing of the text at the natural language engine, wherein linguistic processing of the text includes tokenizing the text, tagging one or more tokens, grouping the tagged tokens and storing one or more noun phrases associated with the text; transferring the one or more noun phrases to the database engine for construction of an SQL query; providing a response to the user in a same natural language as was recognized; automatically adjusting said second set of speech recognition operations based on an automated evaluation of resources available at the network server system and/or the client device; and automatically adjusting said first set of speech recognition operations based on an automated evaluation of resources available at the client device. | 13. A method of performing recognition of a speech utterance from a user with a distributed client-server system comprising: receiving user speech data from a client device in streaming packets through a network interface of a network server system employing an application level Internet based protocol overlaid on transmission control protocol (TCP) such that said streaming packets are processed as they are received, said speech data resulting from a first set of speech recognition operations being performed on the speech utterance by a client device; recognizing the speech utterance as well as a natural language used in said speech utterance using processing routines executing at said network server system which implement a second set of speech recognition operations, wherein recognizing includes converting the speech utterance into text using a Hidden Markov Modeling technique; sending text corresponding to the speech utterance to a natural language engine and a database engine; performing linguistic processing of the text at the natural language engine, wherein linguistic processing of the text includes tokenizing the text, tagging one or more tokens, grouping the tagged tokens and storing one or more noun phrases associated with the text; transferring the one or more noun phrases to the database engine for construction of an SQL query; providing a response to the user in a same natural language as was recognized; automatically adjusting said second set of speech recognition operations based on an automated evaluation of resources available at the network server system and/or the client device; and automatically adjusting said first set of speech recognition operations based on an automated evaluation of resources available at the client device. 24. The method of claim 13 wherein said first set of speech recognition operations includes detecting silence in said speech utterance. | 0.806034 |
9,495,542 | 10 | 12 | 10. A non-transient computer program product comprising computer code for execution on a host computer processor, the computer code for implementing a method of scanning an item of target code prepared from a program written in a programming language having a security specification, the method employing a model checking system, the code comprising: computer code for receiving the item of target code, the target code comprising an item of executable binary code; code for preparing a data structure corresponding to the item target code by parsing the target code to extract executable elements; code for providing a language definition file corresponding to the programming language, the language definition file comprising rules defining the format of instructions in the programming language; code for providing a static model corresponding to the programming language, the static model comprising rules defined from the security specification of the programming language; code for creating a composite model of the target code by supplementing the data structure with information from the language definition file, and with information from the static model, the composite model having a format for processing by the model checking system; code for segmenting the composite model into a plurality of segments including at least a first segment; and code for changing a distribution of instructions among the segments by, for each segment after the first segment, assessing at least the first instruction in each segment, and moving the at least first instruction to an immediately preceding segment if that at least first instruction is not a transfer instruction; code for providing the composite model to the model checker; code for engaging the model checker to analyze the composite model, the code for engaging the model checker comprising: code for analyzing each of the plurality of segments individually; and code for analyzing boundaries of the segments; the model checker producing a result; and code for generating an output based on the result produced by the model checker, the output indicating a measure of whether the model checker identified an indication that the target contains malware. | 10. A non-transient computer program product comprising computer code for execution on a host computer processor, the computer code for implementing a method of scanning an item of target code prepared from a program written in a programming language having a security specification, the method employing a model checking system, the code comprising: computer code for receiving the item of target code, the target code comprising an item of executable binary code; code for preparing a data structure corresponding to the item target code by parsing the target code to extract executable elements; code for providing a language definition file corresponding to the programming language, the language definition file comprising rules defining the format of instructions in the programming language; code for providing a static model corresponding to the programming language, the static model comprising rules defined from the security specification of the programming language; code for creating a composite model of the target code by supplementing the data structure with information from the language definition file, and with information from the static model, the composite model having a format for processing by the model checking system; code for segmenting the composite model into a plurality of segments including at least a first segment; and code for changing a distribution of instructions among the segments by, for each segment after the first segment, assessing at least the first instruction in each segment, and moving the at least first instruction to an immediately preceding segment if that at least first instruction is not a transfer instruction; code for providing the composite model to the model checker; code for engaging the model checker to analyze the composite model, the code for engaging the model checker comprising: code for analyzing each of the plurality of segments individually; and code for analyzing boundaries of the segments; the model checker producing a result; and code for generating an output based on the result produced by the model checker, the output indicating a measure of whether the model checker identified an indication that the target contains malware. 12. The non-transient computer program product according to claim 10 , wherein code for segmenting the composite model into a plurality of segments comprises: code for segmenting the composite model into a plurality of segments according to language rule, and wherein code for providing the composite model to the model checker comprises code for providing the plurality of segments to a plurality of model checking systems. | 0.758817 |
9,460,708 | 1 | 4 | 1. A system, comprising: a language model generated from language model seed text, the language model seed text comprising first entries that correctly utilize a first word and second entries that correctly utilize a second word, wherein the second word shares a pronunciation with the first word and has a different spelling than the first word; a dictionary of available data substitutions, the available data substitutions including a substitution of the second word for the first word; a transducer configured to process speech recognition data utilizing the language model and the dictionary, wherein, to process the speech recognition data, the transducer is further configured to: establish probabilities including a first probability of a first alternative that replaces an occurrence of the first word in the speech recognition data with the second word, and a second probability of a second alternative that leaves the occurrence of the first word in the speech recognition data without modification, the probabilities being established based on a third word that appears in sequence with the occurrence of the first word in the speech recognition data; and when the first probability exceeds the second probability, applying the first alternative by replacing the occurrence of the first word in the speech recognition data with the second word that shares the pronunciation with the first word and has a different spelling than the first word; and a computing device configured to execute at least the transducer. | 1. A system, comprising: a language model generated from language model seed text, the language model seed text comprising first entries that correctly utilize a first word and second entries that correctly utilize a second word, wherein the second word shares a pronunciation with the first word and has a different spelling than the first word; a dictionary of available data substitutions, the available data substitutions including a substitution of the second word for the first word; a transducer configured to process speech recognition data utilizing the language model and the dictionary, wherein, to process the speech recognition data, the transducer is further configured to: establish probabilities including a first probability of a first alternative that replaces an occurrence of the first word in the speech recognition data with the second word, and a second probability of a second alternative that leaves the occurrence of the first word in the speech recognition data without modification, the probabilities being established based on a third word that appears in sequence with the occurrence of the first word in the speech recognition data; and when the first probability exceeds the second probability, applying the first alternative by replacing the occurrence of the first word in the speech recognition data with the second word that shares the pronunciation with the first word and has a different spelling than the first word; and a computing device configured to execute at least the transducer. 4. The system of claim 1 , wherein the first word is “Right” and the second word is “Rite”. | 0.963831 |
8,504,923 | 17 | 19 | 17. The non-transitory computer readable memory of claim 16 , wherein the event engine is further to: register a function to call to notify the application of the occurrence of the event; and call the registered function to notify the application of the occurrence of the event. | 17. The non-transitory computer readable memory of claim 16 , wherein the event engine is further to: register a function to call to notify the application of the occurrence of the event; and call the registered function to notify the application of the occurrence of the event. 19. The non-transitory computer readable memory of claim 17 , wherein the event engine is further to: receive a request from the application to be notified of the occurrence of any events. | 0.608333 |
10,148,660 | 13 | 18 | 13. A method for delivering author specific content, comprising: crawling open content across multiple online resources to identify content that appears to have been generated by a specific author; building an author database that stores an identification of a first author along with a string provided by a content site having content generated by the first author, the string identifying a key that encrypts the first author's identification and the Universal Resource Locator (URL) of the content site; receiving input from a user that registers the user and identifies a specific author that the user wants to follow; and generating an activity stream based on the user input to notify the registered user of content from the multiple online resources generated by the specific author. | 13. A method for delivering author specific content, comprising: crawling open content across multiple online resources to identify content that appears to have been generated by a specific author; building an author database that stores an identification of a first author along with a string provided by a content site having content generated by the first author, the string identifying a key that encrypts the first author's identification and the Universal Resource Locator (URL) of the content site; receiving input from a user that registers the user and identifies a specific author that the user wants to follow; and generating an activity stream based on the user input to notify the registered user of content from the multiple online resources generated by the specific author. 18. The method of claim 13 , further comprising: registering authors with the author database; and accepting input from registered authors to identify their respective content. | 0.696552 |
8,265,925 | 1 | 2 | 1. A method for textual exploration and discovery comprising: annotating, with a processor, Subject-Verb-Object Structures (SVOS) in a grammatically encoded electronic text; and wherein said SVOS are used to identify semantic facets termed “Agent”, “Process” and “Object”, i.e. APOS, in a text span, and wherein said semantic facets “Agent”, “Process” and “Object” are provided as index entries in respective window panes on a display unit as contacts point to said electronic text. | 1. A method for textual exploration and discovery comprising: annotating, with a processor, Subject-Verb-Object Structures (SVOS) in a grammatically encoded electronic text; and wherein said SVOS are used to identify semantic facets termed “Agent”, “Process” and “Object”, i.e. APOS, in a text span, and wherein said semantic facets “Agent”, “Process” and “Object” are provided as index entries in respective window panes on a display unit as contacts point to said electronic text. 2. A method in accordance with claim 1 , wherein three separate window panes are provided on the display unit. | 0.899083 |
8,352,468 | 14 | 15 | 14. A computer-implemented method, comprising: learning a preference of a user of a multifunctional device in record selection from search results, the learning comprising: keeping track of records previously selected by the user from search results generated in response to prior search queries entered by the user, the keeping track comprising: storing the records previously selected by the user in a top hit database, which comprises a weight column and a query column having a one-letter resolution; and updating the query column based on a first letter of each of the prior user search queries; associating the selected records with their respective prior user search queries; and presenting, via the multifunctional device, a plurality of records found in the multifunctional device in response to a current search query submitted by the user, the current user search query including an alphanumerical string comprising a first character, the plurality of records presented in an order based on the learned preference of the user. | 14. A computer-implemented method, comprising: learning a preference of a user of a multifunctional device in record selection from search results, the learning comprising: keeping track of records previously selected by the user from search results generated in response to prior search queries entered by the user, the keeping track comprising: storing the records previously selected by the user in a top hit database, which comprises a weight column and a query column having a one-letter resolution; and updating the query column based on a first letter of each of the prior user search queries; associating the selected records with their respective prior user search queries; and presenting, via the multifunctional device, a plurality of records found in the multifunctional device in response to a current search query submitted by the user, the current user search query including an alphanumerical string comprising a first character, the plurality of records presented in an order based on the learned preference of the user. 15. The method of claim 14 , wherein presenting the plurality of records found using the current user search query in the order based on the learned preference of the user comprises: first presenting a record of the plurality of records found that has been most frequently selected by the user when the user has previously entered search queries having a first letter that matches the first character. | 0.5 |
10,095,689 | 2 | 4 | 2. A system for automated ontology building, comprising: a contextual token recognizer for creating, from text, representing at least one of date and time; a dependency graph calculator for calculating a dependency graph across the contextual tokens using at least one parse tree obtained by parsing the text; an instance generator for generating concept instance candidates and parent-child relationships based on pattern matching and transformation of the at least one parse tree; a concept candidate identifier for grouping concept instance candidates into concept candidates using concept candidate keys, the concept candidate keys being a sequence of triplets; a concept candidate based tree former for arranging the concept candidates into a tree having tree nodes and creating predicate-based relationships between the tree nodes based on patterns and predicates identified in the text; a node scorer and sorter for scoring and sorting the tree nodes; a tree rebalancer for performing an analysis of the tree nodes and rebalancing the tree based on the analysis to provide an ontology based on the text and formed as an output graph comprising a plurality of nodes; and a user interface for editing the ontology by selectively choosing from a plurality of options including adding a new node to the output graph, removing an existing node from the output graph, moving one of the plurality of nodes or a sub-graph across a parent-child hierarchy in the output graph, creating a new relation across the plurality of nodes, and removing an existing relation edges from the output graph. | 2. A system for automated ontology building, comprising: a contextual token recognizer for creating, from text, representing at least one of date and time; a dependency graph calculator for calculating a dependency graph across the contextual tokens using at least one parse tree obtained by parsing the text; an instance generator for generating concept instance candidates and parent-child relationships based on pattern matching and transformation of the at least one parse tree; a concept candidate identifier for grouping concept instance candidates into concept candidates using concept candidate keys, the concept candidate keys being a sequence of triplets; a concept candidate based tree former for arranging the concept candidates into a tree having tree nodes and creating predicate-based relationships between the tree nodes based on patterns and predicates identified in the text; a node scorer and sorter for scoring and sorting the tree nodes; a tree rebalancer for performing an analysis of the tree nodes and rebalancing the tree based on the analysis to provide an ontology based on the text and formed as an output graph comprising a plurality of nodes; and a user interface for editing the ontology by selectively choosing from a plurality of options including adding a new node to the output graph, removing an existing node from the output graph, moving one of the plurality of nodes or a sub-graph across a parent-child hierarchy in the output graph, creating a new relation across the plurality of nodes, and removing an existing relation edges from the output graph. 4. The system of claim 2 , wherein the concept instance candidates are grouped responsive to a configurable equality expression between the text and at least one lemma. | 0.820896 |
8,161,066 | 2 | 4 | 2. A method for creating a semantic object to represent a target referent of an object type, the semantic object being stored on a computer readable medium, the method, comprising, creating the semantic object of a semantic object type to represent the target referent of the object type, the semantic object having multiple meta-tags that are each associable with metadata; wherein, the semantic object of the semantic object type is suitable to represent the object type of the target referent; associating a meta-tag of the multiple meta-tags with the metadata; wherein the meta-tag or the metadata is definable using an ontology; and extracting a portion of the content from the target referent for inclusion in the semantic object; subsequently determining that the target referent has been revised, updating metadata associated with one or more meta-taps of the multiple meta-taps of the semantic object based on the revision; wherein the extraction is part of a data mining performed on selected resources including the ontology or the Internet. sharing, with another user, the semantic object having included therein the portion of the content extracted from the target referent. | 2. A method for creating a semantic object to represent a target referent of an object type, the semantic object being stored on a computer readable medium, the method, comprising, creating the semantic object of a semantic object type to represent the target referent of the object type, the semantic object having multiple meta-tags that are each associable with metadata; wherein, the semantic object of the semantic object type is suitable to represent the object type of the target referent; associating a meta-tag of the multiple meta-tags with the metadata; wherein the meta-tag or the metadata is definable using an ontology; and extracting a portion of the content from the target referent for inclusion in the semantic object; subsequently determining that the target referent has been revised, updating metadata associated with one or more meta-taps of the multiple meta-taps of the semantic object based on the revision; wherein the extraction is part of a data mining performed on selected resources including the ontology or the Internet. sharing, with another user, the semantic object having included therein the portion of the content extracted from the target referent. 4. The method of claim 2 , wherein, the metadata is updated based on a revision of the target referent performed via a web browser. | 0.878026 |
9,201,865 | 8 | 10 | 8. The method of claim 1 , comprising identifying options available from a third party computer for the user and requesting from the third party computer available options that match the user request. | 8. The method of claim 1 , comprising identifying options available from a third party computer for the user and requesting from the third party computer available options that match the user request. 10. The method of claim 8 , comprising presenting available time or location options to the user to confirm. | 0.55 |
9,213,766 | 11 | 12 | 11. The method of claim 1 further comprising recommending sources for researching the questionable information after the alert of questionable information is provided. | 11. The method of claim 1 further comprising recommending sources for researching the questionable information after the alert of questionable information is provided. 12. The method of claim 11 wherein the sources recommended are based on the one or more keywords detected and user information. | 0.5 |
8,856,075 | 17 | 18 | 17. A system of sharing in one or more network(s), comprising: a non-transitory computer readable memory of a central server; one or more requestor(s) or sharing user adapted to map or request sharing of one or more selected contents from one or more network(s) at a central server, to determine one or more target users with the said request including share selected one or more contents from one or more network(s) with or via one or more selected communication channels or services, applications or modules, connections or relations from set of requestor(s) or sharing user(s) related subscribed communication channels or services, installed applications or modules and connected or related users; wherein the central server is configured to receive, store and manage each registered user's one or more profile(s), IN and OUT permission and relational connections or dynamic relationships, to receive, store and processes one or more shared contents with metadata; wherein the central server is further configured to match the target users with the requestor(s) or sharing user(s) from one or more network(s), and wherein the matching of the target users with the requestor(s) or sharing user(s) is based on one or more selections, IN and OUT preferences, profiles and connections of requestor(s) or sharing user(s), to provide or synchronize or update said one or more shared contents of said sharing user from one or more network(s) to said determined one or more target users; and one or more target users adapted to receive one or more shared contents from sharing user(s). | 17. A system of sharing in one or more network(s), comprising: a non-transitory computer readable memory of a central server; one or more requestor(s) or sharing user adapted to map or request sharing of one or more selected contents from one or more network(s) at a central server, to determine one or more target users with the said request including share selected one or more contents from one or more network(s) with or via one or more selected communication channels or services, applications or modules, connections or relations from set of requestor(s) or sharing user(s) related subscribed communication channels or services, installed applications or modules and connected or related users; wherein the central server is configured to receive, store and manage each registered user's one or more profile(s), IN and OUT permission and relational connections or dynamic relationships, to receive, store and processes one or more shared contents with metadata; wherein the central server is further configured to match the target users with the requestor(s) or sharing user(s) from one or more network(s), and wherein the matching of the target users with the requestor(s) or sharing user(s) is based on one or more selections, IN and OUT preferences, profiles and connections of requestor(s) or sharing user(s), to provide or synchronize or update said one or more shared contents of said sharing user from one or more network(s) to said determined one or more target users; and one or more target users adapted to receive one or more shared contents from sharing user(s). 18. The system as claimed in claim 17 , wherein said relational connections or dynamic relationships comprising one or more related or connected known, unknown like minded, referred, subscribed and matched users. | 0.809353 |
10,163,074 | 1 | 2 | 1. A system comprising: a processor configured to: receive a digital communication including text in a body of the communication corresponding to a schedulable event; identify the schedulable event in the text of the communication body, by comparison of words in the text to predefined words designated as identifying schedulable events; query a user to determine if the schedulable event should be scheduled; and responsive to user confirmation to the query, schedule the schedulable event. | 1. A system comprising: a processor configured to: receive a digital communication including text in a body of the communication corresponding to a schedulable event; identify the schedulable event in the text of the communication body, by comparison of words in the text to predefined words designated as identifying schedulable events; query a user to determine if the schedulable event should be scheduled; and responsive to user confirmation to the query, schedule the schedulable event. 2. The system of claim 1 wherein the digital communication is a text message. | 0.557471 |
5,513,298 | 15 | 16 | 15. The method of claim 14 further comprising the step of: generating a series of vector quantization values representative of the speech input. | 15. The method of claim 14 further comprising the step of: generating a series of vector quantization values representative of the speech input. 16. The method of claim 15 further comprising the step of: operating a selection device to instantaneously switch the system from one context to another. | 0.5 |
5,555,367 | 11 | 12 | 11. A system according to claim 10, wherein the a series of transformation performed in the performing means includes operations such as retain, duplicate, merge, constrain, and restrict. | 11. A system according to claim 10, wherein the a series of transformation performed in the performing means includes operations such as retain, duplicate, merge, constrain, and restrict. 12. A system according to claim 11, wherein the retain operation retains classes and associations that are to be included in the specification of the query. | 0.54386 |
8,182,270 | 1 | 25 | 1. A method for providing a dynamic continual improvement educational environment that is tailored to an individual learner, the method comprising: using a user interface and a graphical design technique to design an educational path that is selectively adaptive to educational performances of learners, wherein the adaptive educational path comprises dynamic educational content and a plurality of object oriented educational activities for presentation to the learners, wherein the dynamic educational content is separate and independent from the plurality of object oriented educational activities, wherein the design technique automatically produces computer readable instructions relating to the dynamic educational content, and wherein aspects of the educational content are associated in a relational order even when an aspect of the educational content is moved, and further using the user interface and graphical design technique to design a collaborative activity for multiple learners, wherein the collaborative activity is initiated after an establishment of pre-determined criteria; providing a portion of the adaptive educational path for presentation of at least a portion of the educational content to a particular learner; obtaining and automatically analyzing learner performance data of the particular learner, wherein the learner performance data is obtained and analyzed by a computer system; using a computer processor and a computer readable medium encoded with object oriented computer executable code to automatically and adaptively customize the educational path to an educational performance of the particular learner, wherein the customizing of the educational path to the educational performance of the particular learner comprises: using the learner performance data that was obtained and analyzed by the computer system to identify which portions of the educational content are to be presented to the particular learner, wherein the identified portions include a type and difficulty of the educational content that is to be selectively presented to the particular learner; using the learner performance data that was obtained and analyzed by the computer system to selectively determine a frequency of exposure of the identified portions of the educational content to the particular learner; using the learner performance data that was obtained and analyzed by the computer system to prioritize the identified portions of the educational content that are to be presented to the particular learner; using the learner performance data that was obtained and analyzed by the computer system to selectively match the identified and prioritized portions of the educational content with the identified educational activities for presentation to the particular learner; and selectively prioritizing the individually matched educational content and corresponding educational activities for presentation to the particular learner based upon the learner performance data that was obtained and analyzed by the computer system, wherein the prioritization comprises modifying the presentation order of the individually matched educational content and corresponding educational activities based upon the learner performance data that was obtained and analyzed; and providing portions of the educational content for iterative presentation to the learner over an extended period of time based on at least some of the learner performance data that was obtained and analyzed by the computer system to maintain the learner's understanding of the educational content. | 1. A method for providing a dynamic continual improvement educational environment that is tailored to an individual learner, the method comprising: using a user interface and a graphical design technique to design an educational path that is selectively adaptive to educational performances of learners, wherein the adaptive educational path comprises dynamic educational content and a plurality of object oriented educational activities for presentation to the learners, wherein the dynamic educational content is separate and independent from the plurality of object oriented educational activities, wherein the design technique automatically produces computer readable instructions relating to the dynamic educational content, and wherein aspects of the educational content are associated in a relational order even when an aspect of the educational content is moved, and further using the user interface and graphical design technique to design a collaborative activity for multiple learners, wherein the collaborative activity is initiated after an establishment of pre-determined criteria; providing a portion of the adaptive educational path for presentation of at least a portion of the educational content to a particular learner; obtaining and automatically analyzing learner performance data of the particular learner, wherein the learner performance data is obtained and analyzed by a computer system; using a computer processor and a computer readable medium encoded with object oriented computer executable code to automatically and adaptively customize the educational path to an educational performance of the particular learner, wherein the customizing of the educational path to the educational performance of the particular learner comprises: using the learner performance data that was obtained and analyzed by the computer system to identify which portions of the educational content are to be presented to the particular learner, wherein the identified portions include a type and difficulty of the educational content that is to be selectively presented to the particular learner; using the learner performance data that was obtained and analyzed by the computer system to selectively determine a frequency of exposure of the identified portions of the educational content to the particular learner; using the learner performance data that was obtained and analyzed by the computer system to prioritize the identified portions of the educational content that are to be presented to the particular learner; using the learner performance data that was obtained and analyzed by the computer system to selectively match the identified and prioritized portions of the educational content with the identified educational activities for presentation to the particular learner; and selectively prioritizing the individually matched educational content and corresponding educational activities for presentation to the particular learner based upon the learner performance data that was obtained and analyzed by the computer system, wherein the prioritization comprises modifying the presentation order of the individually matched educational content and corresponding educational activities based upon the learner performance data that was obtained and analyzed; and providing portions of the educational content for iterative presentation to the learner over an extended period of time based on at least some of the learner performance data that was obtained and analyzed by the computer system to maintain the learner's understanding of the educational content. 25. A method as recited in claim 1 , wherein the step for providing portions of the educational content for iterative presentation comprises automatically providing positive feedback to the particular learner as aspects of the educational content are learned. | 0.842649 |
9,753,912 | 11 | 13 | 11. A method for processing speech, comprising: receiving speech input; automatically processing the received speech input with at least one automated processor, to parse the speech input, in accordance with a plurality of available command contexts, associated with a plurality of different applications which accept respective commands, and if a correspondence of previously received speech input to any single command is incomplete for execution by any of the plurality of different applications, automatically prompting a human user for further speech input, with a prompt adapted to solicit information from the human user to reduce ambiguity or increase completeness with respect to respective commands accepted by the available command contexts; automatically disregarding previously receive speech input if an abort, fail or cancel condition is detected by the at least one automated processor in further speech input; and based on a result of the automatically processing, selectively processing the command with the at least one automated command processor. | 11. A method for processing speech, comprising: receiving speech input; automatically processing the received speech input with at least one automated processor, to parse the speech input, in accordance with a plurality of available command contexts, associated with a plurality of different applications which accept respective commands, and if a correspondence of previously received speech input to any single command is incomplete for execution by any of the plurality of different applications, automatically prompting a human user for further speech input, with a prompt adapted to solicit information from the human user to reduce ambiguity or increase completeness with respect to respective commands accepted by the available command contexts; automatically disregarding previously receive speech input if an abort, fail or cancel condition is detected by the at least one automated processor in further speech input; and based on a result of the automatically processing, selectively processing the command with the at least one automated command processor. 13. The method according to claim 11 , wherein each of the available command contexts has at least one entry in an associated command dictionary, and each entry in the associated command dictionary has an associated command grammar, and the determining if the correspondence of the previously received speech input to any single command is incomplete for execution, comprises associating portions of the parsed speech input with the respective command grammars. | 0.5 |
10,083,691 | 13 | 15 | 13. A method according to claim 11 , wherein the verification by the human transcriber comprises one of confirming one or more of the transcribed values and assigning a different transcribed value to one or more of the questionable utterances in the sample. | 13. A method according to claim 11 , wherein the verification by the human transcriber comprises one of confirming one or more of the transcribed values and assigning a different transcribed value to one or more of the questionable utterances in the sample. 15. A method according to claim 13 , further comprising: comparing the confirmed transcribed values and the different transcribed values of the questionable utterances in the sample; and when the confirmed transcribed values and the different transcribed values differ, providing the questionable utterances in the pool that are not included in the sample to a different human reviewer. | 0.507653 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.