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7,802,222 | 1 | 11 |
1. A method of automating the application of constraints to one or more design objects in a circuit design created using an electronic design automation tool, wherein the one or more design objects represent physical circuit objects in a circuit being designed using the electronic design automation tool, comprising: providing a template type in a computer system; wherein the template type includes a selectable template type identifier in produced in a computer user interface display of the computer system to identify the template type, wherein template type includes template instance generation code stored in a computer readable storage device of the computer system to run a template instance generation process, wherein template type further includes template instance validation code stored in the computer readable storage to run a template instance validation process; receiving by the computer system, a user selection of the template type identifier; in response to the received user selection of the template type identifier, invoking the template instance generation code to run the template instance generation process on the computer system, to produce a template instance, wherein the produced template instance identifies a constraint set that includes multiple constraints and that identifies associations between the multiple constraints in the constraint set and the one or more design objects to store the produced template instance in the memory device, and to create an association in the memory device between the produced template instance and the template type.
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1. A method of automating the application of constraints to one or more design objects in a circuit design created using an electronic design automation tool, wherein the one or more design objects represent physical circuit objects in a circuit being designed using the electronic design automation tool, comprising: providing a template type in a computer system; wherein the template type includes a selectable template type identifier in produced in a computer user interface display of the computer system to identify the template type, wherein template type includes template instance generation code stored in a computer readable storage device of the computer system to run a template instance generation process, wherein template type further includes template instance validation code stored in the computer readable storage to run a template instance validation process; receiving by the computer system, a user selection of the template type identifier; in response to the received user selection of the template type identifier, invoking the template instance generation code to run the template instance generation process on the computer system, to produce a template instance, wherein the produced template instance identifies a constraint set that includes multiple constraints and that identifies associations between the multiple constraints in the constraint set and the one or more design objects to store the produced template instance in the memory device, and to create an association in the memory device between the produced template instance and the template type. 11. The method of claim 1 , further including: displaying on a computer system display a template instance representation corresponding to the template instance.
| 0.817873 |
10,019,259 | 9 | 14 |
9. A method, comprising: receiving and extensibility library that includes one or more transformation directives that specify a business semantic preserving transform that transforms at least one source application component of a source application into one or more transformed application components of a transformed application, the business semantic preserving transform as specified by the one or more transformation directives of the extensibility library causes an execution of transformed code of the one or more transformed application components of the transformed application in a new execution scenario to produce an identical semantic effect as an execution of source code of the at least one source application component of the source application in an old execution scenario; registering the extensibility library to a transformation application such that the transformation application accesses the one or more transformation directives; receiving the source application having one or more original architectural classes; performing, via the transformation application, code separation by identifying one or more candidate components in the source application through querying an abstract syntax tree (AST) that includes transformation directives from the extensibility library, and mapping the one or more candidate components in the source application to a target architecture of the transformed application that includes one or more transformed architectural classes that abstract away a physical configuration of underlying hardware; and transforming, via the transformation application, the source application into the transformed application using the business semantic preserving transform, the business semantic preserving transform changing one or more original architectural classes of the source application into the one or more transformed architectural classes of the transformed application, the one or more original architectural classes having at least one architectural difference from the one or more transformed architectural classes.
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9. A method, comprising: receiving and extensibility library that includes one or more transformation directives that specify a business semantic preserving transform that transforms at least one source application component of a source application into one or more transformed application components of a transformed application, the business semantic preserving transform as specified by the one or more transformation directives of the extensibility library causes an execution of transformed code of the one or more transformed application components of the transformed application in a new execution scenario to produce an identical semantic effect as an execution of source code of the at least one source application component of the source application in an old execution scenario; registering the extensibility library to a transformation application such that the transformation application accesses the one or more transformation directives; receiving the source application having one or more original architectural classes; performing, via the transformation application, code separation by identifying one or more candidate components in the source application through querying an abstract syntax tree (AST) that includes transformation directives from the extensibility library, and mapping the one or more candidate components in the source application to a target architecture of the transformed application that includes one or more transformed architectural classes that abstract away a physical configuration of underlying hardware; and transforming, via the transformation application, the source application into the transformed application using the business semantic preserving transform, the business semantic preserving transform changing one or more original architectural classes of the source application into the one or more transformed architectural classes of the transformed application, the one or more original architectural classes having at least one architectural difference from the one or more transformed architectural classes. 14. The method of claim 9 , wherein the at least one architectural difference resulted from a transformation of a source application component of the source application having a first grammar into a transformed application component of the transformed application having a second grammar, wherein the first grammar and the second grammar are associated with different programming paradigms.
| 0.597107 |
7,865,452 | 1 | 11 |
1. A computer-implemented method comprising steps of: receiving one or more answer submissions at an online answer submission system that accepts, from multiple users, answers to questions submitted to the online answer submission system by users other than those that submitted the one or more answer submissions; processing a set of previously scored training submissions, thereby training a machine learning mechanism to score, automatically, a plurality of submissions that are submitted by users of a system; scoring a particular submission of said plurality of submissions automatically using the machine learning mechanism, thereby producing a score; and performing, relative to the particular submission, an action that is determined based on said score; wherein said previously scored training submissions are also answers to questions submitted to the online answer submission system; wherein said steps are performed by one or more computing devices.
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1. A computer-implemented method comprising steps of: receiving one or more answer submissions at an online answer submission system that accepts, from multiple users, answers to questions submitted to the online answer submission system by users other than those that submitted the one or more answer submissions; processing a set of previously scored training submissions, thereby training a machine learning mechanism to score, automatically, a plurality of submissions that are submitted by users of a system; scoring a particular submission of said plurality of submissions automatically using the machine learning mechanism, thereby producing a score; and performing, relative to the particular submission, an action that is determined based on said score; wherein said previously scored training submissions are also answers to questions submitted to the online answer submission system; wherein said steps are performed by one or more computing devices. 11. The method of claim 1 , wherein training the machine learning mechanism comprises training the machine learning mechanism based on web-based sources of information, wherein the web-based sources of information comprise at least one of: (a) information that is extracted from an analysis of search results that result from a web search query, (b) a number of trusted web sites within a specified set of search results, (c) information produced by a lexical analysis of text that corresponds to a specified set of search results, and (d) information produced from an expansion of a submission with lexical information that originates from a specified set of search results.
| 0.550599 |
7,827,028 | 12 | 16 |
12. The method of claim 11 , wherein said generating (g) comprises: (h) receiving, at a parser training module, a destination language treebank having parse trees of a plurality of destination language sentences, the parse trees of said destination language sentences having nodes labeled with the syntactic labels; (i) generating, using the destination language treebank, a destination language parsing model, including parameters for ranking candidate parse trees for a destination language sentence; (j) receiving, at the parsing module, a second plurality of destination language sentences from a parallel corpus, the parallel corpus including the second plurality of destination language sentences and their respective source language equivalents; (k) applying the destination language parsing model to the second plurality of destination language sentences to generate a ranked list of candidate parse trees for each sentence of the second plurality of destination language sentences; (l) transforming the candidate parse trees by applying, with a tree transformer, a rule set associated with linguistic characteristics of the source and destination languages; (m) assigning, with a role labeler, a linguistic role label to nodes of the candidate parse trees, the role label corresponding to the linguistic role of a node within its respective parse tree; (n) extracting grammar constraints from portions of each candidate parse tree; (o) estimating the translingual parsing model using the extracted grammar constraints, and source language sentences of the parallel corpus, the translingual parsing model including parameters sufficient to rank candidate parses, wherein the parameters relate elements of the candidate parses including source language words, destination language words, syntactic labels, and role labels.
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12. The method of claim 11 , wherein said generating (g) comprises: (h) receiving, at a parser training module, a destination language treebank having parse trees of a plurality of destination language sentences, the parse trees of said destination language sentences having nodes labeled with the syntactic labels; (i) generating, using the destination language treebank, a destination language parsing model, including parameters for ranking candidate parse trees for a destination language sentence; (j) receiving, at the parsing module, a second plurality of destination language sentences from a parallel corpus, the parallel corpus including the second plurality of destination language sentences and their respective source language equivalents; (k) applying the destination language parsing model to the second plurality of destination language sentences to generate a ranked list of candidate parse trees for each sentence of the second plurality of destination language sentences; (l) transforming the candidate parse trees by applying, with a tree transformer, a rule set associated with linguistic characteristics of the source and destination languages; (m) assigning, with a role labeler, a linguistic role label to nodes of the candidate parse trees, the role label corresponding to the linguistic role of a node within its respective parse tree; (n) extracting grammar constraints from portions of each candidate parse tree; (o) estimating the translingual parsing model using the extracted grammar constraints, and source language sentences of the parallel corpus, the translingual parsing model including parameters sufficient to rank candidate parses, wherein the parameters relate elements of the candidate parses including source language words, destination language words, syntactic labels, and role labels. 16. The method of claim 12 , wherein said estimating (o) is effected using a CKY algorithm.
| 0.677305 |
9,454,759 | 10 | 11 |
10. A system for maintaining consumer privacy in behavioral scoring, comprising: a first computing system; and a second computing system, wherein the first computing system includes a receiver configured to receive a plurality of account identifiers, and, for each account identifier, a corresponding first encrypted account identifier and corresponding disguised set of consumer characteristics from a second computing system, wherein the first encrypted account identifier is a produced via encryption of the corresponding account identifier using a first one-way encryption, and wherein the receiver of the first computing system is configured to encrypt each account identifier into a second encrypted account identifier using a second one-way encryption upon receipt, and a plurality of transaction data entries, wherein each transaction data entry includes data related to a payment transaction including at least a second encrypted account identifier and transaction data; and a processor configured to generate an algorithm configured to calculate a behavior prediction score corresponding to the behavior prediction request using disguised consumer characteristic values, wherein the generated algorithm is based on at least the transaction data included in each received transaction data entry and the disguised set of consumer characteristics mapped to the second encrypted account identifier included in the respective transaction data entry, and the first computing system does not receive any unencrypted account identifiers, any undisguised consumer characteristics, or any personally identifiable information.
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10. A system for maintaining consumer privacy in behavioral scoring, comprising: a first computing system; and a second computing system, wherein the first computing system includes a receiver configured to receive a plurality of account identifiers, and, for each account identifier, a corresponding first encrypted account identifier and corresponding disguised set of consumer characteristics from a second computing system, wherein the first encrypted account identifier is a produced via encryption of the corresponding account identifier using a first one-way encryption, and wherein the receiver of the first computing system is configured to encrypt each account identifier into a second encrypted account identifier using a second one-way encryption upon receipt, and a plurality of transaction data entries, wherein each transaction data entry includes data related to a payment transaction including at least a second encrypted account identifier and transaction data; and a processor configured to generate an algorithm configured to calculate a behavior prediction score corresponding to the behavior prediction request using disguised consumer characteristic values, wherein the generated algorithm is based on at least the transaction data included in each received transaction data entry and the disguised set of consumer characteristics mapped to the second encrypted account identifier included in the respective transaction data entry, and the first computing system does not receive any unencrypted account identifiers, any undisguised consumer characteristics, or any personally identifiable information. 11. The system of claim 10 , wherein the processor of the first computing system is further configured to calculate a behavior prediction score for each first encrypted account identifier by application of the corresponding disguised set of consumer characteristics to the generated algorithm; and first computing system further includes a transmitter configured to transmit at least the calculated behavior prediction score for each first encrypted account identifier and the corresponding first encrypted account identifier.
| 0.5 |
7,809,566 | 1 | 2 |
1. A method for use in automatic speech recognition comprising: recognizing a user input as a correction hypothesis containing a plurality of recognition elements; performing a non-deterministic alignment of at least a portion of the correction hypothesis with an earlier recognition hypothesis containing a plurality of recognition elements such that the recognition elements in the aligned portion of the correction hypothesis are determined to most likely correspond to a range of recognition elements in the earlier recognition hypothesis; replacing the elements in the range of recognition elements in the earlier recognition hypothesis with the recognition elements in the aligned portion of the correction hypothesis.
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1. A method for use in automatic speech recognition comprising: recognizing a user input as a correction hypothesis containing a plurality of recognition elements; performing a non-deterministic alignment of at least a portion of the correction hypothesis with an earlier recognition hypothesis containing a plurality of recognition elements such that the recognition elements in the aligned portion of the correction hypothesis are determined to most likely correspond to a range of recognition elements in the earlier recognition hypothesis; replacing the elements in the range of recognition elements in the earlier recognition hypothesis with the recognition elements in the aligned portion of the correction hypothesis. 2. A method according to claim 1 , wherein the non-deterministic alignment favors alignments corresponding to recognition elements in the earlier recognition hypothesis that have a higher probability of being misrecognized.
| 0.5 |
9,401,159 | 11 | 16 |
11. In a mobile terminal, a method comprising: receiving a selection of a first control action associated with a first application stored in the mobile terminal; providing a plurality of choices associated with the first control action; receiving input that identifies a word or a phrase to be used as a voice command corresponding to the first control action, wherein the word or phrase is selected from the plurality of choices; associating the identified word or phrase as corresponding to the first control action; receiving voice input from a user; identifying the voice input as corresponding to the identified word or phrase; and performing the first control action associated with the first application based on the identified voice input.
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11. In a mobile terminal, a method comprising: receiving a selection of a first control action associated with a first application stored in the mobile terminal; providing a plurality of choices associated with the first control action; receiving input that identifies a word or a phrase to be used as a voice command corresponding to the first control action, wherein the word or phrase is selected from the plurality of choices; associating the identified word or phrase as corresponding to the first control action; receiving voice input from a user; identifying the voice input as corresponding to the identified word or phrase; and performing the first control action associated with the first application based on the identified voice input. 16. The method of claim 11 , further comprising: receiving, from the user via a voice input, a second word or phrase corresponding to a second control action associated with the first application; associating the second word or phrase with the second control action; performing speech recognition to identify the second word or phrase; verifying with the user whether the identified second word or phrase is correct; and associating the second word or phrase with the second control action in response to verifying that the identified second word or phrase is correct.
| 0.5 |
8,635,127 | 1 | 2 |
1. A computerized method for identifying tax documents to customize a taxpayer interview in a tax preparation program comprising: presenting to a computer user on a computer screen from said tax preparation program images of a plurality of sample tax documents corresponding to said taxpayer's actual tax documents comprising reported tax data; determining in said tax preparation program said computer user's selection of at least one of said plurality of sample tax documents relevant to said taxpayer's tax situation; in response to said computer user's selection of said at least one sample tax document, customizing a tax preparation interview process according to said at least one sample tax document wherein said tax preparation program: (1) presents at least one data prompt to said computer user for entering tax data, said data prompt selected for said computer user according to said at least one of said plurality of tax documents selected by said computer user; (2) receives said computer user's response to said at least one data prompt for entering tax data; and in response to receiving said computer user's response, prepares a tax return using said tax preparation program and said response to said at least one data prompt for entering tax data.
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1. A computerized method for identifying tax documents to customize a taxpayer interview in a tax preparation program comprising: presenting to a computer user on a computer screen from said tax preparation program images of a plurality of sample tax documents corresponding to said taxpayer's actual tax documents comprising reported tax data; determining in said tax preparation program said computer user's selection of at least one of said plurality of sample tax documents relevant to said taxpayer's tax situation; in response to said computer user's selection of said at least one sample tax document, customizing a tax preparation interview process according to said at least one sample tax document wherein said tax preparation program: (1) presents at least one data prompt to said computer user for entering tax data, said data prompt selected for said computer user according to said at least one of said plurality of tax documents selected by said computer user; (2) receives said computer user's response to said at least one data prompt for entering tax data; and in response to receiving said computer user's response, prepares a tax return using said tax preparation program and said response to said at least one data prompt for entering tax data. 2. The method of claim 1 wherein determining said computer user's selection of at least one of said plurality of sample tax documents comprises determining which of said plurality of sample tax documents said computer user has dragged and dropped in a section of said computer screen.
| 0.5 |
9,397,830 | 7 | 13 |
7. The method of claim 1 , further comprising: generating conditional decrypt criteria; and inserting the conditional decrypt criteria into a preamble, said preamble located in an encrypted block of the plurality of encrypted blocks.
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7. The method of claim 1 , further comprising: generating conditional decrypt criteria; and inserting the conditional decrypt criteria into a preamble, said preamble located in an encrypted block of the plurality of encrypted blocks. 13. The method of claim 7 , wherein generating conditional decrypt criteria includes an entity specific identifier.
| 0.577206 |
9,661,105 | 1 | 6 |
1. A virtual assistant system, comprising an assistant server configured to: receive from a mobile device a first semantic atom corresponding to an input command for one or more external services, the assistant server configured to generate a language command specific to each of the one or more external services corresponding to the first semantic atom; and transmit a second semantic atom comprising the generated language command back to the mobile device to enable the mobile device to directly control the one or more external services using a wireless connection.
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1. A virtual assistant system, comprising an assistant server configured to: receive from a mobile device a first semantic atom corresponding to an input command for one or more external services, the assistant server configured to generate a language command specific to each of the one or more external services corresponding to the first semantic atom; and transmit a second semantic atom comprising the generated language command back to the mobile device to enable the mobile device to directly control the one or more external services using a wireless connection. 6. The virtual assistant system of claim 1 , wherein the assistant server is further configured to delete the first semantic atom after generating the language command when the first semantic atom is determined to no longer be required for further information transmissions thereof with other services or the mobile device.
| 0.547619 |
9,697,824 | 1 | 5 |
1. A computer-implemented method for facilitating a user to control a driving apparatus through a voice command, the method comprising: receiving, by a processor, a user voice input; determining, by the processor, a language dialect the user voice input is associated with a language dialect; translating, by the processor, the voice input to a standard voice pattern based on the language dialect associated with the user voice input; based on the standard voice pattern, determining, by the processor, a control command corresponding to the user voice input for maneuvering the driving apparatus; and effectuating, by the processor, execution of the control command to control the driving apparatus.
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1. A computer-implemented method for facilitating a user to control a driving apparatus through a voice command, the method comprising: receiving, by a processor, a user voice input; determining, by the processor, a language dialect the user voice input is associated with a language dialect; translating, by the processor, the voice input to a standard voice pattern based on the language dialect associated with the user voice input; based on the standard voice pattern, determining, by the processor, a control command corresponding to the user voice input for maneuvering the driving apparatus; and effectuating, by the processor, execution of the control command to control the driving apparatus. 5. The computer-implemented method of claim 1 , wherein determining the user voice input is associated with the language dialect includes: retrieving one or more reference patterns representing predetermined voice inputs in the language dialect; obtaining characteristic vectors for the reference patterns; obtaining a characteristic vector for the user voice input; for each characteristic vector for the reference patterns, determining a similarity between the characteristic vector for the reference pattern and the characteristic vector for the user voice input; and determining the reference pattern having a highest similarity to the user voice input as the matching reference pattern.
| 0.5 |
9,104,769 | 9 | 18 |
9. A method comprising: sending, over a network, from a server system, to a first source, one or more requests to determine a latency or speed associated with the first source; receiving, from the first source, at the server system, one or more replies to the one or more requests; determining, by the server system, based on the one or more replies, the latency or speed associated with the first source; receiving, from a client, at the server system that is operated by a first party, a first client request for first data; in response to receiving the first client request, generating a plurality of requests, each of which requests a different set of data from the first source that is (1) remote relative to the server system and (2) is operated by a second party that is different than the first party; wherein generating the plurality of requests comprises, based on the latency or speed, determining, by the server system, a size of multiple requests of the plurality of requests that are sent to the first source; sending the plurality of requests over the network from the server system to the first source; after sending the plurality of requests from the server system to the first source, receiving a plurality of responses from the first source; sending, from the server system, to the client, data from the plurality of responses; wherein the method is performed by one or more computing devices.
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9. A method comprising: sending, over a network, from a server system, to a first source, one or more requests to determine a latency or speed associated with the first source; receiving, from the first source, at the server system, one or more replies to the one or more requests; determining, by the server system, based on the one or more replies, the latency or speed associated with the first source; receiving, from a client, at the server system that is operated by a first party, a first client request for first data; in response to receiving the first client request, generating a plurality of requests, each of which requests a different set of data from the first source that is (1) remote relative to the server system and (2) is operated by a second party that is different than the first party; wherein generating the plurality of requests comprises, based on the latency or speed, determining, by the server system, a size of multiple requests of the plurality of requests that are sent to the first source; sending the plurality of requests over the network from the server system to the first source; after sending the plurality of requests from the server system to the first source, receiving a plurality of responses from the first source; sending, from the server system, to the client, data from the plurality of responses; wherein the method is performed by one or more computing devices. 18. One or more non-transitory storage media carrying instructions which, when executed by one or more processors, cause performance of the method recited in claim 9 .
| 0.729773 |
7,716,233 | 7 | 16 |
7. A computer readable storage medium, comprising executable instructions to: receive an abstract query specifying first and second levels in a multiple source hierarchical dimension, wherein the first level references a primary first data source, and the second level references a primary second data source; generate a first data source specific query specifying the first level for the first data source; execute the first data source specific query against the first data source to produce results for the first level, wherein the results for the first level include a value for the first level; generate a context expression for the second data source, wherein the context expression relates the first level to the second level; generate a second data source specific query specifying the second level and including the context expression for the second data source; execute the second data source specific query against the second data source to produce results for the second level; and return the results for the first and second levels.
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7. A computer readable storage medium, comprising executable instructions to: receive an abstract query specifying first and second levels in a multiple source hierarchical dimension, wherein the first level references a primary first data source, and the second level references a primary second data source; generate a first data source specific query specifying the first level for the first data source; execute the first data source specific query against the first data source to produce results for the first level, wherein the results for the first level include a value for the first level; generate a context expression for the second data source, wherein the context expression relates the first level to the second level; generate a second data source specific query specifying the second level and including the context expression for the second data source; execute the second data source specific query against the second data source to produce results for the second level; and return the results for the first and second levels. 16. The computer readable storage medium according to claim 7 , wherein the abstract query, the first data source specific query and the second data source specific query additionally specify at least one of a measure and an attribute.
| 0.5 |
9,009,201 | 1 | 3 |
1. A method comprising: receiving, for inserting into a database, structured data and unstructured data, the structured data including data for storing in the database as one or more data records, the unstructured data including an electronic document; recording an insertion event, including inserting a first data record corresponding to the structured data into an event data structure and inserting a second data record corresponding to the unstructured data into the event data structure; determining, using an indexing agent that monitors the event data structure for the insertion event, a combined index for the structured data and unstructured data, wherein determining the combined index comprises: determining, by the indexing agent and storing in a collection data structure, a record for the structured data upon detecting an insertion of the first data record into the event data structure; determining, by the indexing agent and storing in a document data structure, a record for the unstructured data upon detecting an insertion of the second data record into the event data structure, the document data structure comprising a document table and a file table, the document table comprising a file identifier data field for storing a foreign key to the file table, the file table comprising a tenant specific data table for storing information for unstructured data uploaded by the tenant, the tenant being a work group including one or more user computers; and determining the combined index based on the record in the collection data structure and the record in the document data structure; and providing the combined index to a search module for performing a search in the structured data and unstructured data, wherein the method is performed by one or more computers.
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1. A method comprising: receiving, for inserting into a database, structured data and unstructured data, the structured data including data for storing in the database as one or more data records, the unstructured data including an electronic document; recording an insertion event, including inserting a first data record corresponding to the structured data into an event data structure and inserting a second data record corresponding to the unstructured data into the event data structure; determining, using an indexing agent that monitors the event data structure for the insertion event, a combined index for the structured data and unstructured data, wherein determining the combined index comprises: determining, by the indexing agent and storing in a collection data structure, a record for the structured data upon detecting an insertion of the first data record into the event data structure; determining, by the indexing agent and storing in a document data structure, a record for the unstructured data upon detecting an insertion of the second data record into the event data structure, the document data structure comprising a document table and a file table, the document table comprising a file identifier data field for storing a foreign key to the file table, the file table comprising a tenant specific data table for storing information for unstructured data uploaded by the tenant, the tenant being a work group including one or more user computers; and determining the combined index based on the record in the collection data structure and the record in the document data structure; and providing the combined index to a search module for performing a search in the structured data and unstructured data, wherein the method is performed by one or more computers. 3. The method of claim 1 , wherein determining the combined index based on the record in the collection data structure and the record in the document data structure comprises: identifying, by the indexing agent, the unstructured data based on the record in the document data structure; determining an index of the unstructured data based on content of the unstructured data; and determining the combined index based on the index of the unstructured data.
| 0.5 |
8,484,031 | 1 | 4 |
1. A computer-implemented system for processing an interaction, the interaction including an utterance requiring recognition before being usable for further computer-implemented processing, the system comprising: an application configured to provide the utterance; a proxy processing subsystem in communication with the application, the proxy processing subsystem configured to receive the utterance and initiate processing thereof; a recognition decision engine configured to receive from the proxy processing system an utterance for recognition and ancillary information regarding the utterance for recognition, the recognition decision engine selecting, responsive to the ancillary information, one or more recognizers from a first type of recognizer subsystems and a second type of recognizer subsystems; and a results decision engine operably coupled with the one or more recognizers and configured to return to the proxy processing subsystem a recognition result, the results decision engine further automatically updating a statistics database responsive to results of processing by the one or more recognizers.
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1. A computer-implemented system for processing an interaction, the interaction including an utterance requiring recognition before being usable for further computer-implemented processing, the system comprising: an application configured to provide the utterance; a proxy processing subsystem in communication with the application, the proxy processing subsystem configured to receive the utterance and initiate processing thereof; a recognition decision engine configured to receive from the proxy processing system an utterance for recognition and ancillary information regarding the utterance for recognition, the recognition decision engine selecting, responsive to the ancillary information, one or more recognizers from a first type of recognizer subsystems and a second type of recognizer subsystems; and a results decision engine operably coupled with the one or more recognizers and configured to return to the proxy processing subsystem a recognition result, the results decision engine further automatically updating a statistics database responsive to results of processing by the one or more recognizers. 4. The system of claim 1 , wherein the utterance is a computer-stored sound file corresponding to human speech.
| 0.766807 |
7,870,132 | 1 | 2 |
1. A method for constructing a query hierarchy, the method comprising: receiving a first query; generating a candidate query list comprising one or more candidate queries, wherein the one or more candidate queries are determined based upon a relative coverage determined between the first query and each candidate query, the relative coverage between the first query and a candidate query representing a similarity between the content of pages clicked by a user in response to the candidate query and the content of pages clicked by a user in response to the first query determined from click-through data; determining hierarchical relationships between the first query relative and each of the one or more candidate queries comprising the candidate query list, wherein each of the hierarchical relationships comprises one of a parent relationship, a child relationship, or a sibling relationship; and constructing a query hierarchy based on the hierarchical relationships between the first query and each of the one or more candidate queries comprising the candidate query list.
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1. A method for constructing a query hierarchy, the method comprising: receiving a first query; generating a candidate query list comprising one or more candidate queries, wherein the one or more candidate queries are determined based upon a relative coverage determined between the first query and each candidate query, the relative coverage between the first query and a candidate query representing a similarity between the content of pages clicked by a user in response to the candidate query and the content of pages clicked by a user in response to the first query determined from click-through data; determining hierarchical relationships between the first query relative and each of the one or more candidate queries comprising the candidate query list, wherein each of the hierarchical relationships comprises one of a parent relationship, a child relationship, or a sibling relationship; and constructing a query hierarchy based on the hierarchical relationships between the first query and each of the one or more candidate queries comprising the candidate query list. 2. The method according to claim 1 , wherein generating the candidate query list further comprises: determining the relative coverage of the first query relative to each of the one or more candidate queries comprising the candidate query list; and determining the relative coverage of each of the one or more candidate queries comprising the candidate query list relative to the first query.
| 0.5 |
9,031,962 | 7 | 8 |
7. The method of claim 1 , wherein the input device includes a set of overloaded keys generating an ambiguous text input.
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7. The method of claim 1 , wherein the input device includes a set of overloaded keys generating an ambiguous text input. 8. The method of claim 7 , wherein the input device is a phone, a mobile computing device, or a remote control device for a television.
| 0.5 |
9,612,032 | 8 | 14 |
8. A method for using a wall-mounted device for sensing and control for one or more systems in a home environment, said method comprising: detecting a user rotation of an outer ring that laterally surrounds a body of said device to form a circular lateral periphery of said device, said body having a circular cross-section, a wall-facing rear surface, and a user-facing front surface, said outer ring being user-rotatable around said body for enabling said user rotation, said device having a user-facing circular display component, said user-facing circular display component and said outer ring forming a user input component, wherein at least one environmental sensing component is disposed within said body of said device, wherein said device includes a communication component configured for providing wired or wireless sensing and/or control-related communications with said one or more systems in the home environment, wherein said device includes a processor in operative communication with said at least one environmental sensing component, said communication component, and said user input component; highlighting, based on said user rotation of said outer ring, respective ones of a circular arrangement of display elements appearing near a periphery of said user-facing circular display component; detecting an inward pressing of said outer ring, said outer ring being inwardly pressable for enabling said inward pressing; identifying as a user selection one of said display elements that is highlighted when said inward pressing of said outer ring is detected, wherein plural respective user selections of different ones of said display elements are identified responsive to repeated user rotations and/or inward pressings of said outer ring; and permitting user access to the control of one or more sensing or control functions of said device if said plural respective user selections corresponds to a password or combination.
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8. A method for using a wall-mounted device for sensing and control for one or more systems in a home environment, said method comprising: detecting a user rotation of an outer ring that laterally surrounds a body of said device to form a circular lateral periphery of said device, said body having a circular cross-section, a wall-facing rear surface, and a user-facing front surface, said outer ring being user-rotatable around said body for enabling said user rotation, said device having a user-facing circular display component, said user-facing circular display component and said outer ring forming a user input component, wherein at least one environmental sensing component is disposed within said body of said device, wherein said device includes a communication component configured for providing wired or wireless sensing and/or control-related communications with said one or more systems in the home environment, wherein said device includes a processor in operative communication with said at least one environmental sensing component, said communication component, and said user input component; highlighting, based on said user rotation of said outer ring, respective ones of a circular arrangement of display elements appearing near a periphery of said user-facing circular display component; detecting an inward pressing of said outer ring, said outer ring being inwardly pressable for enabling said inward pressing; identifying as a user selection one of said display elements that is highlighted when said inward pressing of said outer ring is detected, wherein plural respective user selections of different ones of said display elements are identified responsive to repeated user rotations and/or inward pressings of said outer ring; and permitting user access to the control of one or more sensing or control functions of said device if said plural respective user selections corresponds to a password or combination. 14. The method of claim 8 , wherein user access to the control of said one or more sensing or control functions of said device enables a user to lock or unlock one of said systems in said home environment.
| 0.621771 |
10,055,462 | 2 | 10 |
2. A system comprising: one or more storage devices that store instructions; and one or more computers configured by executing the instructions to perform operations comprising: receiving a first search query associated with an entity reference, wherein the entity reference corresponds to one or more distinct entities; providing a set of results for the first search query, wherein the set of results distinguishes between distinct entities; identifying one or more attributes of at least one entity of the one or more distinct entities based at least in part on the set of results; in response to the first search query, automatically generating a selectable list of one or more additional search queries that are based on but separate from the first search query, wherein the one or more additional search queries comprise combinations of the at least one entity and the one or more attributes; providing the selectable list of the one or more additional search queries for display within a user interface; receiving an input selecting at least one of the one or more additional search queries that are displayed within the user interface; and providing an updated set of results based on the selected one or more additional search queries, wherein the updated set of results comprises at least one result not in the set of results.
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2. A system comprising: one or more storage devices that store instructions; and one or more computers configured by executing the instructions to perform operations comprising: receiving a first search query associated with an entity reference, wherein the entity reference corresponds to one or more distinct entities; providing a set of results for the first search query, wherein the set of results distinguishes between distinct entities; identifying one or more attributes of at least one entity of the one or more distinct entities based at least in part on the set of results; in response to the first search query, automatically generating a selectable list of one or more additional search queries that are based on but separate from the first search query, wherein the one or more additional search queries comprise combinations of the at least one entity and the one or more attributes; providing the selectable list of the one or more additional search queries for display within a user interface; receiving an input selecting at least one of the one or more additional search queries that are displayed within the user interface; and providing an updated set of results based on the selected one or more additional search queries, wherein the updated set of results comprises at least one result not in the set of results. 10. The system of claim 2 , wherein the one or more computers are further configured to perform operations comprising: ranking the identified one or more attributes; wherein automatically generating the selectable list comprises automatically generating the one or more additional search queries based on the first search query, the at least one entity, the one or more attributes, and the ranking.
| 0.790747 |
9,400,769 | 1 | 3 |
1. A method for generating a document, comprising: receiving a first configuration of the document that includes a set of content items; generating, using a processor, alignment data representing a measure for an alignment of the content items in the first configuration, the alignment data including a plurality of alignment lines; and determining, using the alignment data, whether the content items are aligned with one another with an acceptable alignment, wherein the content items are determined to be aligned with one another with an acceptable alignment when a distance between any two adjacent alignment lines of the plurality of alignment lines is less than a first defined value or greater than a second defined value; wherein generating the alignment data comprises: using the processor to determine a set of collinear points for the first configuration defined by the edges of the content items; and using the determined set of points to generate the plurality of alignment lines for the first configuration; wherein the first defined value is 2 mm and the second defined value is 5 mm.
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1. A method for generating a document, comprising: receiving a first configuration of the document that includes a set of content items; generating, using a processor, alignment data representing a measure for an alignment of the content items in the first configuration, the alignment data including a plurality of alignment lines; and determining, using the alignment data, whether the content items are aligned with one another with an acceptable alignment, wherein the content items are determined to be aligned with one another with an acceptable alignment when a distance between any two adjacent alignment lines of the plurality of alignment lines is less than a first defined value or greater than a second defined value; wherein generating the alignment data comprises: using the processor to determine a set of collinear points for the first configuration defined by the edges of the content items; and using the determined set of points to generate the plurality of alignment lines for the first configuration; wherein the first defined value is 2 mm and the second defined value is 5 mm. 3. The method as claimed in claim 1 , further comprising: using the processor to generate regularity data representing a measure for the disposition of content items in the first configuration; and using the regularity data to determine if the content items are positioned relative to one another in a way which is commensurate with a predefined style definition for the document.
| 0.5 |
8,103,534 | 1 | 6 |
1. A computer-implemented method of managing supplier intelligence, comprising: collecting procurement data regarding a procurement process from a plurality of data sources, the procurement data including information regarding a plurality of business divisions of a business entity; for each business division, generating a set of spend formulas for determining spending associated with that business division; generating a set of supplier intelligence business rules based on a variety of business parameters, each supplier intelligence business rule interrelating at least one spend formula associated with a first one of the business divisions with at least one spend formula associated with a second one of the business divisions; performing, using a computer system, an automatic analysis of at least a portion of the procurement data based on one or more of the set of supplier intelligence business rules to determine the financial effects of a decision made by the first business division on the second business division; and automatically generating a visual output indicating the results of the automatic analysis of the at least a portion of the procurement data.
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1. A computer-implemented method of managing supplier intelligence, comprising: collecting procurement data regarding a procurement process from a plurality of data sources, the procurement data including information regarding a plurality of business divisions of a business entity; for each business division, generating a set of spend formulas for determining spending associated with that business division; generating a set of supplier intelligence business rules based on a variety of business parameters, each supplier intelligence business rule interrelating at least one spend formula associated with a first one of the business divisions with at least one spend formula associated with a second one of the business divisions; performing, using a computer system, an automatic analysis of at least a portion of the procurement data based on one or more of the set of supplier intelligence business rules to determine the financial effects of a decision made by the first business division on the second business division; and automatically generating a visual output indicating the results of the automatic analysis of the at least a portion of the procurement data. 6. The method of claim 1 , wherein the automatic analysis includes: determining a total cost associated with the procurement process; and determining the financial effects of particular procurement decisions of one or more of the plurality of business divisions on the total cost.
| 0.783282 |
5,467,425 | 1 | 2 |
1. A computer based language modelling system receiving data in the form of training text divided into a series of n-grams, each n-gram comprising a series of "n" words, each n-gram having an associated count, the history of an n-gram being represented by the initial n-1 words of the n-gram, comprising: a language modelling means for determining a conditional probability of a predicted word given the previous (n-1) words, comprising: a memory means for storing the data; a separating means coupled to said memory means for examining each word within each n-gram and classifying each n-gram into one of one or more classes based upon one or more words in a given n-gram, each class having one or more similar n-grams associated with said class, said similar n-grams having the same predicted word and x previous words, where x varies from (n-1) to zero, to associate each n-gram with exactly one of said one or more classes, each class is identified with one of one or more sets based upon the value of x used when determining the class of the n-gram; a factor means coupled to the output of said separating means and to said memory means for determining a factor for each of said one or more classes, said factor representing the relative strength of predicting said predicted word given the previous (n-1) words, the value of each factor being approximately equal to the ratio of the sum of the counts of each n-gram associated with a given class over the sum of the counts of all (n-1)-grams which when followed by said predicted word would belong to said given class; and a conditional probability means coupled to the output of said factor means for determining said conditional probability of the occurrence of said predicted word given that a particular sequence of (n-1) previous words have occurred using said factors, said conditional probability approximately equal to the ratio of a first factor, said first factor associated with the class that a given n-gram is associated with, said given n-gram equal to said predicted word and the history of said predicted word, the history equal to a particular sequence of (n-1) previous words, over the sum of one or more factors, said one or more factors associated with all of the classes of n-grams obtained by using said particular sequence of (n-1) words followed by any word of the vocabulary.
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1. A computer based language modelling system receiving data in the form of training text divided into a series of n-grams, each n-gram comprising a series of "n" words, each n-gram having an associated count, the history of an n-gram being represented by the initial n-1 words of the n-gram, comprising: a language modelling means for determining a conditional probability of a predicted word given the previous (n-1) words, comprising: a memory means for storing the data; a separating means coupled to said memory means for examining each word within each n-gram and classifying each n-gram into one of one or more classes based upon one or more words in a given n-gram, each class having one or more similar n-grams associated with said class, said similar n-grams having the same predicted word and x previous words, where x varies from (n-1) to zero, to associate each n-gram with exactly one of said one or more classes, each class is identified with one of one or more sets based upon the value of x used when determining the class of the n-gram; a factor means coupled to the output of said separating means and to said memory means for determining a factor for each of said one or more classes, said factor representing the relative strength of predicting said predicted word given the previous (n-1) words, the value of each factor being approximately equal to the ratio of the sum of the counts of each n-gram associated with a given class over the sum of the counts of all (n-1)-grams which when followed by said predicted word would belong to said given class; and a conditional probability means coupled to the output of said factor means for determining said conditional probability of the occurrence of said predicted word given that a particular sequence of (n-1) previous words have occurred using said factors, said conditional probability approximately equal to the ratio of a first factor, said first factor associated with the class that a given n-gram is associated with, said given n-gram equal to said predicted word and the history of said predicted word, the history equal to a particular sequence of (n-1) previous words, over the sum of one or more factors, said one or more factors associated with all of the classes of n-grams obtained by using said particular sequence of (n-1) words followed by any word of the vocabulary. 2. The system of claim 1, further comprising: a user interface for accepting input data in the form of spoken sounds; a signal processor coupled to the output of said user interface for creating a series of feature vector signals based upon said spoken sounds; a labelling means coupled to the output of said signal processor for labelling said series of feature vector signals with a label from a label alphabet to produce a series of labelled feature vector signals; and a matching means coupled to the output of said labelling means for creating and updating one or more sequences of word-series hypotheses which represent said sounds input into said user interface which were spoken up to a given point in time, comprising: a second memory means for storing said sequences of word-series hypotheses, a search controlling means coupled to an acoustic matching means, said language modelling means, and to said second memory means for controlling the sequence of performance of said acoustic matching means and of said language modelling means, and for controlling the inputs into said language modelling means in the form of signals representing a current word from a word choice set obtained from said acoustic matching means, a sequence of (n-1) previous words obtained from said second memory means, said (n-1) previous words being the last (n-1) words in one of the sequences of word-series hypotheses, said sequences of word-series hypotheses resulting from previous outputs of said matching means, said acoustic matching means coupled to said search controlling means for comparing an acoustic parameter vector signal with said labelled feature vector signals to reduce the number of word choices that likely represent said labelled feature vector signals, said word choices are output to the search controlling means which provides each of said word choices as an input to said language modelling means, said language modelling means determines a score for each of the word choices, said score is an estimate of the conditional probability of each of said word choices given that each of the sequences of word-series hypotheses have occurred, each of said sequences of word-series hypotheses is input into said language modelling means from said search controlling means.
| 0.5 |
7,779,388 | 7 | 9 |
7. A method for implementing (Simple Object Access Protocol) SOAP-based Web services via a programming language in a computing system, comprising: identifying a (Simple Object Access Protocol) SOAP message attribute mechanism corresponding to the at least one SOAP-based Web service, the SOAP message attribute declared via a construct of said programming language; querying a compiler for information about the SOAP message attribute; and generating at least one of additional code and data from the information about the SOAP message attribute for use at run-time when at least one of sending and receiving a SOAP message for at least one SOAP-based Web service occurs, wherein an underlying XML packaging of the SOAP message is transported according to said at least one of sending and receiving via at least one of hypertext transfer protocol (HTTP), file transfer protocol (FTP), transmission control protocol (TCP), user datagram protocol (UDP), internet relay chat (IRC), telnet protocol and Gopher protocol.
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7. A method for implementing (Simple Object Access Protocol) SOAP-based Web services via a programming language in a computing system, comprising: identifying a (Simple Object Access Protocol) SOAP message attribute mechanism corresponding to the at least one SOAP-based Web service, the SOAP message attribute declared via a construct of said programming language; querying a compiler for information about the SOAP message attribute; and generating at least one of additional code and data from the information about the SOAP message attribute for use at run-time when at least one of sending and receiving a SOAP message for at least one SOAP-based Web service occurs, wherein an underlying XML packaging of the SOAP message is transported according to said at least one of sending and receiving via at least one of hypertext transfer protocol (HTTP), file transfer protocol (FTP), transmission control protocol (TCP), user datagram protocol (UDP), internet relay chat (IRC), telnet protocol and Gopher protocol. 9. The method of claim 7 , further comprising, in connection with code that implements at least one SOAP-based Web service, declaring at least one SOAP handling mechanism corresponding to at least one SOAP-based Web service via a construct of said programming language.
| 0.5 |
6,064,961 | 1 | 2 |
1. A method for displaying text in a proofreader associated with a speech recognition application, comprising the steps of: retrieving initial text from a text document responsive to a user request; first centering and displaying said initial text in a display window of a graphical user interface; retrieving further text from said text document responsive to a further user request; second centering and displaying said further text in place of said initial text in said display window; and, repeating said retrieving further text step and said second centering and displaying step until no further text is requested.
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1. A method for displaying text in a proofreader associated with a speech recognition application, comprising the steps of: retrieving initial text from a text document responsive to a user request; first centering and displaying said initial text in a display window of a graphical user interface; retrieving further text from said text document responsive to a further user request; second centering and displaying said further text in place of said initial text in said display window; and, repeating said retrieving further text step and said second centering and displaying step until no further text is requested. 2. The method of claim 1, comprising the step of retrieving previous text for use in said second centering and displaying step.
| 0.831565 |
9,293,134 | 5 | 7 |
5. One or more non-transitory computer-readable media maintaining instructions executable by one or more processors to perform acts comprising: receiving a first audio signal from a first device, wherein the first audio signal contains first user speech, wherein the first audio signal is captured by one or more microphones of the first device; receiving a second audio signal from the first device, wherein the second audio signal contains second user speech, the second audio signal is captured by one or more microphones of a second device, and the second audio signal is provided by the second device to the first device; performing automatic speech recognition (ASR) on the first audio signal using a first ASR model to recognize the first user speech; and performing ASR on the second audio signal using a second ASR model to recognize the second user speech, wherein the second ASR model is different from the first ASR model.
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5. One or more non-transitory computer-readable media maintaining instructions executable by one or more processors to perform acts comprising: receiving a first audio signal from a first device, wherein the first audio signal contains first user speech, wherein the first audio signal is captured by one or more microphones of the first device; receiving a second audio signal from the first device, wherein the second audio signal contains second user speech, the second audio signal is captured by one or more microphones of a second device, and the second audio signal is provided by the second device to the first device; performing automatic speech recognition (ASR) on the first audio signal using a first ASR model to recognize the first user speech; and performing ASR on the second audio signal using a second ASR model to recognize the second user speech, wherein the second ASR model is different from the first ASR model. 7. The one or more non-transitory computer-readable media of claim 5 , wherein: the first ASR model was trained using far-field audio signals; and the second ASR model was trained using near-field audio signals.
| 0.735589 |
8,182,270 | 38 | 39 |
38. A method as recited in claim 2 , further comprising a step for grouping experimental data to determine information relating to one or more groups to which the particular learner belongs.
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38. A method as recited in claim 2 , further comprising a step for grouping experimental data to determine information relating to one or more groups to which the particular learner belongs. 39. A method as recited in claim 38 , wherein the step for providing the adaptive educational path for presentation includes implementing the at least a portion of the presentation based on the particular learner's similarity to other learners for which optimum settings have been established.
| 0.5 |
8,543,375 | 1 | 6 |
1. A computer-implemented method, comprising: receiving, at a computing device having one or more processors, composition inputs; determining, at the computing device, candidate selections for two or more different languages based on the composition inputs; evaluating, at the computing device, the candidate selections for the two or more different languages against language models for the two or more different languages, wherein each language model includes a rule set for a language, and wherein the language models collectively include rule sets for the two or more different languages; determining, at the computing device, a language context value for each of the two or more different languages based on the evaluation; identifying, at the computing device, candidate selections for presentation based on the language context values, the candidate selections including at least one candidate selection in each of the two or more languages; and providing for display, at the computing device, the candidate selections in a single, interleaved list of candidate selections, wherein the single, interleaved list of candidate selections (i) includes at least one candidate selection in each of the two or more languages and (ii) identifies a rank for each of the candidate selections, each rank being indicative of a relative likelihood that its corresponding candidate selection was intended from the composition inputs.
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1. A computer-implemented method, comprising: receiving, at a computing device having one or more processors, composition inputs; determining, at the computing device, candidate selections for two or more different languages based on the composition inputs; evaluating, at the computing device, the candidate selections for the two or more different languages against language models for the two or more different languages, wherein each language model includes a rule set for a language, and wherein the language models collectively include rule sets for the two or more different languages; determining, at the computing device, a language context value for each of the two or more different languages based on the evaluation; identifying, at the computing device, candidate selections for presentation based on the language context values, the candidate selections including at least one candidate selection in each of the two or more languages; and providing for display, at the computing device, the candidate selections in a single, interleaved list of candidate selections, wherein the single, interleaved list of candidate selections (i) includes at least one candidate selection in each of the two or more languages and (ii) identifies a rank for each of the candidate selections, each rank being indicative of a relative likelihood that its corresponding candidate selection was intended from the composition inputs. 6. The method of claim 1 , further comprising: receiving, at the computing device, additional composition inputs after receiving the composition inputs; determining, at the computing device, modified candidate selections for the two or more different languages based on the composition inputs and the additional composition inputs; evaluating, at the computing device, the modified candidate selections for the two or more different languages against language models for the two or more different languages; determining, at the computing device, a modified language context value for each of the two or more different languages based on the evaluation; identifying, at the computing device, modified candidate selections for presentation based on the language context values, the modified candidate selections including at least one of the modified candidate selections in each of the two or more languages; and providing for display, at the computing device, the modified candidate selections in a single, interleaved list of the modified candidate selections.
| 0.5 |
9,436,915 | 18 | 22 |
18. The diagnosis support apparatus according to claim 15 , wherein the inference unit calculates, for each of the plural candidates of diagnosis name to which a priori probability are set, an inference probability of each of the plural candidates of diagnosis name by calculating a posteriori probability based on the plurality of pieces of medical information.
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18. The diagnosis support apparatus according to claim 15 , wherein the inference unit calculates, for each of the plural candidates of diagnosis name to which a priori probability are set, an inference probability of each of the plural candidates of diagnosis name by calculating a posteriori probability based on the plurality of pieces of medical information. 22. The diagnosis support apparatus according to claim 18 , wherein the calculation unit calculates a posteriori probability of each inference result based on the partial set, and calculates the degree of effect by using the priori probability and the posteriori probability.
| 0.747243 |
7,856,472 | 70 | 235 |
70. A computer program product embodied on a non-transitory computer readable medium, comprising: computer code for displaying at least one window in connection with a website; computer code for displaying, utilizing the at least one window, a stock-related field; computer code for receiving a plurality of characters of text from a user as the user is typing the text utilizing the stock-related field; computer code for dynamically determining, after the user types each character in the received text, whether the characters typed so far match one or more text strings in one of a plurality of n-tuples including at least two text strings, each of the plurality of n-tuples including first text representing a stock ticker symbol and second text representing a company name corresponding to the stock ticker symbol; computer code for indicating to the user that a match has been found, utilizing the at least one window, if it is determined that the characters typed so far match the one or more text strings in the one of the plurality of n-tuples; computer code for displaying, utilizing the at least one window, a plurality of message summaries; wherein the plurality of message summaries comprise first information derived from a first message of a plurality of first messages and second information derived from a second message of a plurality of second messages associated with at least one online forum; computer code for displaying, utilizing the at least one window, a first set of representations; computer code for receiving first input from the user indicating a selection of one of the first set of representations; computer code for displaying a second set of representations representing a set of hyperlinks, utilizing the at least one window, in response to receiving the first input; computer code for receiving second input from the user indicating a selection of one of the second set of representations; and computer code for navigating to a destination specified by the selected one of the second set of representations, in response to receiving the second input.
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70. A computer program product embodied on a non-transitory computer readable medium, comprising: computer code for displaying at least one window in connection with a website; computer code for displaying, utilizing the at least one window, a stock-related field; computer code for receiving a plurality of characters of text from a user as the user is typing the text utilizing the stock-related field; computer code for dynamically determining, after the user types each character in the received text, whether the characters typed so far match one or more text strings in one of a plurality of n-tuples including at least two text strings, each of the plurality of n-tuples including first text representing a stock ticker symbol and second text representing a company name corresponding to the stock ticker symbol; computer code for indicating to the user that a match has been found, utilizing the at least one window, if it is determined that the characters typed so far match the one or more text strings in the one of the plurality of n-tuples; computer code for displaying, utilizing the at least one window, a plurality of message summaries; wherein the plurality of message summaries comprise first information derived from a first message of a plurality of first messages and second information derived from a second message of a plurality of second messages associated with at least one online forum; computer code for displaying, utilizing the at least one window, a first set of representations; computer code for receiving first input from the user indicating a selection of one of the first set of representations; computer code for displaying a second set of representations representing a set of hyperlinks, utilizing the at least one window, in response to receiving the first input; computer code for receiving second input from the user indicating a selection of one of the second set of representations; and computer code for navigating to a destination specified by the selected one of the second set of representations, in response to receiving the second input. 235. The computer program product of claim 70 , wherein at least one of the first set of representations is a stock-related representation.
| 0.823604 |
9,515,994 | 9 | 13 |
9. A multi-user searchable encryption method, the method comprising: generating, at a key generation server, system parameters, a primary key, and a secondary key; receiving, at a user terminal, the primary key; encrypting, at the user terminal, data to obtain cypher text, symmetric key to obtain encrypted symmetric key, keyword to obtain encrypted keyword; generating, at the user terminal, trapdoor using the primary key for a search keyword inputted by user, and a decryption parameter using the primary key; receiving, at a proxy server, the cypher text, the encrypted keyword, the encrypted symmetric key, and the secondary key; encrypting, at the proxy server, trapdoor using the secondary key; performing, at the proxy server, a lookup for search results corresponding to the search keyword; and storing, at a database server, the cypher text at a location address and returning the location address of the cypher text to the proxy server; wherein the method further comprises at least one selected from the group consisting of (a), (b), and (c): (a) re-encrypting, at the proxy server, the encrypted symmetric key and encrypted keyword using the secondary key; (b) maintaining, at the proxy server, a table of re-encrypted symmetric keys and a table to store location address of data; and (c) receiving, at the user terminal, a decryption attribute and the cypher text.
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9. A multi-user searchable encryption method, the method comprising: generating, at a key generation server, system parameters, a primary key, and a secondary key; receiving, at a user terminal, the primary key; encrypting, at the user terminal, data to obtain cypher text, symmetric key to obtain encrypted symmetric key, keyword to obtain encrypted keyword; generating, at the user terminal, trapdoor using the primary key for a search keyword inputted by user, and a decryption parameter using the primary key; receiving, at a proxy server, the cypher text, the encrypted keyword, the encrypted symmetric key, and the secondary key; encrypting, at the proxy server, trapdoor using the secondary key; performing, at the proxy server, a lookup for search results corresponding to the search keyword; and storing, at a database server, the cypher text at a location address and returning the location address of the cypher text to the proxy server; wherein the method further comprises at least one selected from the group consisting of (a), (b), and (c): (a) re-encrypting, at the proxy server, the encrypted symmetric key and encrypted keyword using the secondary key; (b) maintaining, at the proxy server, a table of re-encrypted symmetric keys and a table to store location address of data; and (c) receiving, at the user terminal, a decryption attribute and the cypher text. 13. The method of claim 9 , further comprising maintaining, at the proxy server, a table of secondary keys.
| 0.690751 |
9,092,490 | 8 | 10 |
8. A system comprising: one or more computers and one or more storage devices storing instructions that are operable, when executed by the one or more computers, to cause the one or more computers to perform operations comprising: receiving a query; obtaining search results including publication search results responsive to the query, wherein each publication search result refers to a respective book; determining that a score for a highest-ranked publication search result satisfies a threshold relative to respective scores of one or more other publication search results to be provided in response to the query, wherein the highest-ranked publication search result refers to a book; in response to determining that the score for the highest-ranked publication search result satisfies the threshold relative to respective scores of the other publication search results, wherein the score for the highest ranked publication search result satisfies the threshold if the score is at least a threshold multiple of a second score for a second publication search result ranked second in a ranked order of the publication search results, generating a rich result for the highest-ranked publication search result, wherein the rich result for the highest-ranked publication search result comprises more elements of data than any of the other publication search results to be provided in response to the query, wherein the rich result for the highest-ranked publication search result comprises data from one or more web resources that refer to the book, and wherein the elements of data for the rich result comprise a title of the book, an author of the book, and a link to a website related to the book; and providing the rich result and the one or more other publication search results in response to the query.
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8. A system comprising: one or more computers and one or more storage devices storing instructions that are operable, when executed by the one or more computers, to cause the one or more computers to perform operations comprising: receiving a query; obtaining search results including publication search results responsive to the query, wherein each publication search result refers to a respective book; determining that a score for a highest-ranked publication search result satisfies a threshold relative to respective scores of one or more other publication search results to be provided in response to the query, wherein the highest-ranked publication search result refers to a book; in response to determining that the score for the highest-ranked publication search result satisfies the threshold relative to respective scores of the other publication search results, wherein the score for the highest ranked publication search result satisfies the threshold if the score is at least a threshold multiple of a second score for a second publication search result ranked second in a ranked order of the publication search results, generating a rich result for the highest-ranked publication search result, wherein the rich result for the highest-ranked publication search result comprises more elements of data than any of the other publication search results to be provided in response to the query, wherein the rich result for the highest-ranked publication search result comprises data from one or more web resources that refer to the book, and wherein the elements of data for the rich result comprise a title of the book, an author of the book, and a link to a website related to the book; and providing the rich result and the one or more other publication search results in response to the query. 10. The system of claim 8 , wherein the operations further comprise: obtaining, from the one or more web resources that refer to the book, multiple variants of an element of data associated with the book; determining a most popular variant of the multiple variants based on occurrences of the most popular variant in the web resources that refer to the book; and generating the rich result using the determined most popular variant.
| 0.575639 |
8,164,596 | 2 | 4 |
2. The method of claim 1 , the method further comprising: in response to obtaining first input indicating a first point along the first timeline, displaying a first keyframe indicator on the first timeline at the first point; wherein the first keyframe indicator represents a first keyframe of the style sheet animation; wherein the first point corresponds to a first time during the style sheet animation; in response to obtaining second input indicating a second point along the first timeline, displaying a second keyframe indicator on the first timeline at the second point; wherein the second keyframe indicator represents a second keyframe of the style sheet animation; wherein the second point corresponds to a second time during the style sheet animation; wherein generating the style sheet language text further comprises generating style sheet language text that declares the first and second keyframes and that reflects an association between the first keyframe and the first time and an association between the second keyframe and the second time.
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2. The method of claim 1 , the method further comprising: in response to obtaining first input indicating a first point along the first timeline, displaying a first keyframe indicator on the first timeline at the first point; wherein the first keyframe indicator represents a first keyframe of the style sheet animation; wherein the first point corresponds to a first time during the style sheet animation; in response to obtaining second input indicating a second point along the first timeline, displaying a second keyframe indicator on the first timeline at the second point; wherein the second keyframe indicator represents a second keyframe of the style sheet animation; wherein the second point corresponds to a second time during the style sheet animation; wherein generating the style sheet language text further comprises generating style sheet language text that declares the first and second keyframes and that reflects an association between the first keyframe and the first time and an association between the second keyframe and the second time. 4. The method of claim 2 , further comprising: in response to obtaining the first input, associating the first keyframe with a default easing function.
| 0.864209 |
7,724,985 | 1 | 5 |
1. A computer usable storage medium having stored thereon instructions that when executed cause a computer system to perform a method for formatting an image, said method comprising: in response to receiving an image request, retrieving a vector image from a device that is a target of said image request, said vector image illustrating at least a portion of device; locating at said device a first image style identifier within said vector image, said first image style identifier being associated with a first set of style attributes for said vector image; searching for a second set of style attributes for said vector image, said second set of style attributes being stored apart from said vector image, and said second set of style attributes being given a higher priority than said first set of style attributes for said vector image; and if the second set of style attributes is found, causing said vector image to be formatted using at least said second set of style attributes.
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1. A computer usable storage medium having stored thereon instructions that when executed cause a computer system to perform a method for formatting an image, said method comprising: in response to receiving an image request, retrieving a vector image from a device that is a target of said image request, said vector image illustrating at least a portion of device; locating at said device a first image style identifier within said vector image, said first image style identifier being associated with a first set of style attributes for said vector image; searching for a second set of style attributes for said vector image, said second set of style attributes being stored apart from said vector image, and said second set of style attributes being given a higher priority than said first set of style attributes for said vector image; and if the second set of style attributes is found, causing said vector image to be formatted using at least said second set of style attributes. 5. The computer usable storage medium of claim 1 , wherein the causing of said vector image to be formatted using at least said second set of style attributes further comprises: if a style targeted by one of said first set of style attributes is also targeted by one of said second set of style attributes, substituting said one of said second set of style attributes for said one of said first set of style attributes.
| 0.532366 |
9,600,469 | 3 | 5 |
3. The non-transitory computer-readable recording medium of claim 2 , wherein the detecting of the errors comprises: searching whether or not the generated morpheme sequences are in the example-based index DB; extracting the frequencies of appearance at which the forward morpheme sequences and the backward morpheme sequences are identical to morpheme sequences retrieved from the example-based index DB, and calculating suitability scores based on the extracted frequencies of appearance; and detecting grammatical errors by determining whether the calculated suitability scores are smaller than a threshold or whether the calculated suitability scores are smaller than the threshold by a predetermined value or more.
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3. The non-transitory computer-readable recording medium of claim 2 , wherein the detecting of the errors comprises: searching whether or not the generated morpheme sequences are in the example-based index DB; extracting the frequencies of appearance at which the forward morpheme sequences and the backward morpheme sequences are identical to morpheme sequences retrieved from the example-based index DB, and calculating suitability scores based on the extracted frequencies of appearance; and detecting grammatical errors by determining whether the calculated suitability scores are smaller than a threshold or whether the calculated suitability scores are smaller than the threshold by a predetermined value or more. 5. The non-transitory computer-readable recording medium of claim 3 , wherein the calculating of the suitability scores comprises calculating the suitability scores by applying different weights according to frequencies of appearance at which the respective morphemes are identically arranged in the morpheme sequences retrieved from the example-based index DB.
| 0.541878 |
5,583,921 | 9 | 10 |
9. A data processing apparatus comprising: inputting means for inputting alphanumeric character data representing a message to be transmitted; storing means for storing said alphanumeric character data input by said inputting means; converting means for converting said alphanumeric character data stored in said storing means into first information indicating a series of key operations of a push-button telephone which must be operated by a user when inputting a message corresponding to said alphanumeric character data by said key operations of said push-button telephone connected to a public telephone network; and displaying means for displaying said first information indicating said series of key operations converted by said converting means.
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9. A data processing apparatus comprising: inputting means for inputting alphanumeric character data representing a message to be transmitted; storing means for storing said alphanumeric character data input by said inputting means; converting means for converting said alphanumeric character data stored in said storing means into first information indicating a series of key operations of a push-button telephone which must be operated by a user when inputting a message corresponding to said alphanumeric character data by said key operations of said push-button telephone connected to a public telephone network; and displaying means for displaying said first information indicating said series of key operations converted by said converting means. 10. A data processing apparatus according to claim 9, wherein said displaying means displays said first information while dividing a data display range into a plurality of preset data widths.
| 0.783447 |
8,984,051 | 1 | 6 |
1. A computer implemented method for communicating feed information to one or more recipients, the method comprising: receiving, via a user interface for accessing a social networking system, an identification of one or more first recipients to whom to communicate an information update configured to be published to an information feed of the social networking system, the information feed capable of being displayed on a display device; receiving, via the user interface, a first indicator in addition to the identification of the one or more first recipients; identifying the first indicator as a request to communicate the information update to one or more recipients in addition to the one or more first recipients; automatically causing identification of one or more second recipients responsive to identifying the first indicator, the identification of the one or more second recipients based on the one or more first recipients, the identification of the one or more second recipients comprising: determining that an entity satisfies a relevance measure associated with the first indicator, the determination based on performance of one or more operations and application of weighting information in relation to the one or more operations to indicate a degree of relevance between the entity and a first recipient, and selecting the entity to be included in the one or more second recipients; identifying a second indicator as a request to not communicate the information update to one or more potential recipients; automatically causing identification of one or more potential third recipients based on the second indicator, the one or more potential third recipients being excluded from having access to the information update; and providing the information update for access by the one or more second recipients.
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1. A computer implemented method for communicating feed information to one or more recipients, the method comprising: receiving, via a user interface for accessing a social networking system, an identification of one or more first recipients to whom to communicate an information update configured to be published to an information feed of the social networking system, the information feed capable of being displayed on a display device; receiving, via the user interface, a first indicator in addition to the identification of the one or more first recipients; identifying the first indicator as a request to communicate the information update to one or more recipients in addition to the one or more first recipients; automatically causing identification of one or more second recipients responsive to identifying the first indicator, the identification of the one or more second recipients based on the one or more first recipients, the identification of the one or more second recipients comprising: determining that an entity satisfies a relevance measure associated with the first indicator, the determination based on performance of one or more operations and application of weighting information in relation to the one or more operations to indicate a degree of relevance between the entity and a first recipient, and selecting the entity to be included in the one or more second recipients; identifying a second indicator as a request to not communicate the information update to one or more potential recipients; automatically causing identification of one or more potential third recipients based on the second indicator, the one or more potential third recipients being excluded from having access to the information update; and providing the information update for access by the one or more second recipients. 6. The method recited in claim 1 , wherein providing the information update for access includes: storing the information update in a storage medium in association with the one or more second recipients.
| 0.843895 |
9,384,731 | 1 | 5 |
1. On a computing system comprising a processor, a method of detecting phrase confusion risk in a proposed speech grammar for a computer program, the method comprising: providing, by downloading to a remote computing device, via the processor a speech grammar development tool executable by the remote computing device to receive input of a text representation of each of a plurality of proposed speech grammar terms, for each proposed speech grammar term, convert the text representation to a phonetic representation of the speech grammar term, determine whether a portion of the proposed speech grammar term has a spoken duration below a threshold duration, and if the portion of the proposed speech grammar term has a spoken duration below the threshold duration, then omit the portion from the phonetic representation of the proposed speech grammar term, compare via a speech recognition engine the phonetic representation of the speech grammar term to the phonetic representations of other speech grammar terms using a weighted similarity matrix, and provide an output regarding risk of confusion between two proposed speech grammar terms based upon a comparison by the speech recognition engine of the phonetic representations of the two proposed speech grammar terms; receiving via the processor data regarding incorrect speech grammar term identification; and modifying via the processor speech grammar used by the speech recognition engine, wherein modifying the speech grammar comprises modifying one or more weights in the weighted similarity matrix based upon the data.
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1. On a computing system comprising a processor, a method of detecting phrase confusion risk in a proposed speech grammar for a computer program, the method comprising: providing, by downloading to a remote computing device, via the processor a speech grammar development tool executable by the remote computing device to receive input of a text representation of each of a plurality of proposed speech grammar terms, for each proposed speech grammar term, convert the text representation to a phonetic representation of the speech grammar term, determine whether a portion of the proposed speech grammar term has a spoken duration below a threshold duration, and if the portion of the proposed speech grammar term has a spoken duration below the threshold duration, then omit the portion from the phonetic representation of the proposed speech grammar term, compare via a speech recognition engine the phonetic representation of the speech grammar term to the phonetic representations of other speech grammar terms using a weighted similarity matrix, and provide an output regarding risk of confusion between two proposed speech grammar terms based upon a comparison by the speech recognition engine of the phonetic representations of the two proposed speech grammar terms; receiving via the processor data regarding incorrect speech grammar term identification; and modifying via the processor speech grammar used by the speech recognition engine, wherein modifying the speech grammar comprises modifying one or more weights in the weighted similarity matrix based upon the data. 5. The method of claim 1 , wherein determining that the portion of the selected speech grammar term has a spoken duration below a threshold duration comprises force aligning one or more audio samples of the selected speech grammar term to the text representation of the selected speech grammar term and determining that a time stamp of the portion has a value below the threshold duration.
| 0.502558 |
9,501,741 | 1 | 9 |
1. A method for building an automated assistant, the method comprising: registering, by a processor, a service for use in conjunction with an active ontology by specifying at least one of: one or more active processing elements that the service can accept; and one or more active processing elements that the service cannot accept; filtering, by the active ontology, at least one request for services to the service in accordance with the one or more active processing elements specified by the service; wherein: the active ontology is configured to perform natural language processing on user input, the natural language processing performed in response to input facts relating to events collected from a user's environment, and the active ontology comprises a plurality of active processing elements configured to match specific types of facts.
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1. A method for building an automated assistant, the method comprising: registering, by a processor, a service for use in conjunction with an active ontology by specifying at least one of: one or more active processing elements that the service can accept; and one or more active processing elements that the service cannot accept; filtering, by the active ontology, at least one request for services to the service in accordance with the one or more active processing elements specified by the service; wherein: the active ontology is configured to perform natural language processing on user input, the natural language processing performed in response to input facts relating to events collected from a user's environment, and the active ontology comprises a plurality of active processing elements configured to match specific types of facts. 9. The method of claim 1 , wherein the service is invoked by the automated assistant to accomplish one or more tasks.
| 0.822188 |
8,478,749 | 1 | 2 |
1. A computer-implemented method of determining relevant search items, the method comprising the steps performed by a computer of: receiving search results identifying a plurality of documents resulting from a search, the plurality of documents containing one or more terms; generating a first matrix containing a term column representing the one or more terms and a document column representing the documents, wherein at least one row of the first matrix correlates one of the plurality of documents with one of the terms; selecting the document column of the first matrix or the term column of the first matrix as a sort preference; sorting the first matrix according to the sort preference; generating a second matrix containing values representing a measure of overlap between the plurality of documents and the terms, based on the sorted first matrix; and calculating cumulative confidence scores according to the values of the second matrix and ranking the search results according to the cumulative confidence scores; wherein the calculated cumulative confidence scores are determined by normalizing the second matrix with a third matrix, the third matrix having a plurality of element positions; and wherein, for each of the plurality of element positions, an element position has a value equaling the total number of unique words found in one or more of the plurality of documents corresponding to the element position.
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1. A computer-implemented method of determining relevant search items, the method comprising the steps performed by a computer of: receiving search results identifying a plurality of documents resulting from a search, the plurality of documents containing one or more terms; generating a first matrix containing a term column representing the one or more terms and a document column representing the documents, wherein at least one row of the first matrix correlates one of the plurality of documents with one of the terms; selecting the document column of the first matrix or the term column of the first matrix as a sort preference; sorting the first matrix according to the sort preference; generating a second matrix containing values representing a measure of overlap between the plurality of documents and the terms, based on the sorted first matrix; and calculating cumulative confidence scores according to the values of the second matrix and ranking the search results according to the cumulative confidence scores; wherein the calculated cumulative confidence scores are determined by normalizing the second matrix with a third matrix, the third matrix having a plurality of element positions; and wherein, for each of the plurality of element positions, an element position has a value equaling the total number of unique words found in one or more of the plurality of documents corresponding to the element position. 2. The method of claim 1 , wherein the terms are phrases including a plurality of words.
| 0.838235 |
7,937,395 | 27 | 38 |
27. A computer readable storage medium having stored therein instructions, which when executed by a computer system having memory, a display, and one or more processors cause the computer system to: display an application user interface on the display, the application user interface including a document authoring window and a search results window; display in the search results window a set of search results associated with one or more user-specified search keywords in a text-only display format, wherein each search result includes a chunk within a respective document that satisfies the search keywords; in response to a user request to view a chunk, launch a document display window in the application user interface and display therein a portion of the corresponding document that includes the chunk in its native display format, further including generate an empty region in the application user interface by shrinking the document authoring window and occupy the empty region with the document display window in the application user interface; and in response to a user request to duplicate a segment of the corresponding document in the document authoring window, generate therein an instance of the segment of the corresponding document in its native display format.
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27. A computer readable storage medium having stored therein instructions, which when executed by a computer system having memory, a display, and one or more processors cause the computer system to: display an application user interface on the display, the application user interface including a document authoring window and a search results window; display in the search results window a set of search results associated with one or more user-specified search keywords in a text-only display format, wherein each search result includes a chunk within a respective document that satisfies the search keywords; in response to a user request to view a chunk, launch a document display window in the application user interface and display therein a portion of the corresponding document that includes the chunk in its native display format, further including generate an empty region in the application user interface by shrinking the document authoring window and occupy the empty region with the document display window in the application user interface; and in response to a user request to duplicate a segment of the corresponding document in the document authoring window, generate therein an instance of the segment of the corresponding document in its native display format. 38. The computer readable storage medium of claim 27 , wherein different search keywords are highlighted in different manners in the search results window.
| 0.750804 |
8,701,081 | 8 | 13 |
8. One or more non-transitory computer-readable media holding executable instructions that when executed on a processing device replaces operators in model code, the media holding one or more instructions for: identifying a pattern in model code, where: the model code is associated with a graphical model having executable semantics, the pattern is associated with an operator in the model code, and the operator performs operations when the model code is executed; selecting, for the identified pattern, a hardware specific function that performs an operation equivalent to a respective operation performed by the operator in the model code; replacing the operator in the model code with the selected hardware specific function, wherein conceptual arguments of the selected hardware specific function match argument properties of the operator, and wherein argument properties of the selected hardware specific function do not exactly match function properties of the operator; and storing the model code.
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8. One or more non-transitory computer-readable media holding executable instructions that when executed on a processing device replaces operators in model code, the media holding one or more instructions for: identifying a pattern in model code, where: the model code is associated with a graphical model having executable semantics, the pattern is associated with an operator in the model code, and the operator performs operations when the model code is executed; selecting, for the identified pattern, a hardware specific function that performs an operation equivalent to a respective operation performed by the operator in the model code; replacing the operator in the model code with the selected hardware specific function, wherein conceptual arguments of the selected hardware specific function match argument properties of the operator, and wherein argument properties of the selected hardware specific function do not exactly match function properties of the operator; and storing the model code. 13. The media of claim 8 , wherein operation is a matrix operation.
| 0.838942 |
4,516,260 | 64 | 65 |
64. A talking electronic apparatus as set forth in claim 63, wherein at least some of the plurality of word-related problems involve respective requests to the operator to spell individual words and the correct answers corresponding thereto comprising the correct spelling of those words as derived from said digital speech data stored in said memory means.
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64. A talking electronic apparatus as set forth in claim 63, wherein at least some of the plurality of word-related problems involve respective requests to the operator to spell individual words and the correct answers corresponding thereto comprising the correct spelling of those words as derived from said digital speech data stored in said memory means. 65. A talking electronic apparatus as set forth in claim 64, wherein said means for selectively transferring a portion of said digital speech data from said memory means to said speech synthesis means comprises problem posing means for randomly selecting a word-related problem derivable from digital speech data stored in said memory means.
| 0.5 |
9,386,152 | 1 | 4 |
1. A system for a contact center, the system comprising: a processor; and memory, wherein the memory stores instructions that, when executed by the processor, cause the processor to: run an interactive voice response (IVR) node configured to engage in an interaction with a customer of the contact center by presenting set scripts to the customer and receiving corresponding responses from the customer; run an intelligent automated agent configured to communicate with the IVR node, the automated agent comprising an artificial intelligence engine; run a routing server node configured to identify an appropriate live agent from a pool of live agents; run a call server node configured to communicate with the automated agent for routing a first portion of the interaction with corresponding responses to the automated agent at a request of the customer, the call server node being further configured to communicate with an agent device associated with the identified live agent for further routing a second portion of the interaction with corresponding responses to the agent device; an electronic switch coupled to the processor and configured to deliver the interaction to the automated agent and to the agent device; and a non-transitory storage device coupled to the processor and configured to store customer profile data built from previous interactions between the customer and the contact center, wherein the instructions further cause the processor to: invoke the automated agent to retrieve a profile of the customer from the customer profile data during the first portion of the interaction and update the retrieved profile on the storage device to reflect the interaction, and invoke the artificial intelligence engine to learn knowledge from the first portion of the interaction and apply the learned knowledge to future interactions between the customer and the contact center.
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1. A system for a contact center, the system comprising: a processor; and memory, wherein the memory stores instructions that, when executed by the processor, cause the processor to: run an interactive voice response (IVR) node configured to engage in an interaction with a customer of the contact center by presenting set scripts to the customer and receiving corresponding responses from the customer; run an intelligent automated agent configured to communicate with the IVR node, the automated agent comprising an artificial intelligence engine; run a routing server node configured to identify an appropriate live agent from a pool of live agents; run a call server node configured to communicate with the automated agent for routing a first portion of the interaction with corresponding responses to the automated agent at a request of the customer, the call server node being further configured to communicate with an agent device associated with the identified live agent for further routing a second portion of the interaction with corresponding responses to the agent device; an electronic switch coupled to the processor and configured to deliver the interaction to the automated agent and to the agent device; and a non-transitory storage device coupled to the processor and configured to store customer profile data built from previous interactions between the customer and the contact center, wherein the instructions further cause the processor to: invoke the automated agent to retrieve a profile of the customer from the customer profile data during the first portion of the interaction and update the retrieved profile on the storage device to reflect the interaction, and invoke the artificial intelligence engine to learn knowledge from the first portion of the interaction and apply the learned knowledge to future interactions between the customer and the contact center. 4. The system of claim 1 , wherein the processor comprises a plurality of processors, and the IVR node, the call server node, and the automated agent are on different ones of the processors.
| 0.791209 |
9,465,590 | 1 | 6 |
1. A computer-implemented method comprising: providing a code generation framework comprising an Application Program Interface (API) code generator element, a serializer code generator element, and a deserializer code generator element; and in response to the code generation framework receiving a model comprising an enumeration literal as an input, causing the API code generator element to parse the model and generate an API used to create, manipulate, save, and load a first model instance version in a first language comprising a scripting language having dynamic typing, causing the serializer code generator element to parse the model and generate serialization code used to create a second model instance version in a second language from the first model instance version, wherein the model instance version in the second language is created by a follow-on application comprising a graphical model editor, and causing the deserializer code generator element to parse the model and generate deserialization code used to convert a model instance version in the second language into the first language, wherein the deserialization code comprises, a resource object having attached a string representation of the second model instance version, and a resource set object containing the resource object and an added resource of a referenced class element of another model, wherein the deserializer code generator element does not generate an object property from the enumeration literal that is a key word in the first language.
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1. A computer-implemented method comprising: providing a code generation framework comprising an Application Program Interface (API) code generator element, a serializer code generator element, and a deserializer code generator element; and in response to the code generation framework receiving a model comprising an enumeration literal as an input, causing the API code generator element to parse the model and generate an API used to create, manipulate, save, and load a first model instance version in a first language comprising a scripting language having dynamic typing, causing the serializer code generator element to parse the model and generate serialization code used to create a second model instance version in a second language from the first model instance version, wherein the model instance version in the second language is created by a follow-on application comprising a graphical model editor, and causing the deserializer code generator element to parse the model and generate deserialization code used to convert a model instance version in the second language into the first language, wherein the deserialization code comprises, a resource object having attached a string representation of the second model instance version, and a resource set object containing the resource object and an added resource of a referenced class element of another model, wherein the deserializer code generator element does not generate an object property from the enumeration literal that is a key word in the first language. 6. The method as in claim 1 wherein the model is defined in Ecore format.
| 0.719231 |
7,761,478 | 11 | 17 |
11. A computer-implemented method of managing a semantic business model, the method comprising: transforming each business area model of a plurality of business area models for a business entity into a set of intermediate models using at least one of a set of processors, wherein each business area model is a business model for a particular area of the business entity, and wherein each intermediate model stores data for at least one corresponding business area model in a meta-modeling language that is the same for each intermediate model, and wherein the transforming includes: extracting a set of business area sub-models from each business area model of the plurality of business area models; and merging the sets of business area sub-models into the set of intermediate models; and generating the semantic business model by merging the set of intermediate models using at least one of the set of processors, wherein merging the set of intermediate models includes: automatically identifying objects in the intermediate models and relationships for the objects, wherein the relationships for the objects are determined by parsing an attribute for a corresponding object into terms by performing at least one of the following: determining word boundaries in the attribute, expanding abbreviations in the attribute, or determining the context information for each term in the attribute, such that there is a relationship if there is a correlation between terms; and adding objects and relationship information for the objects identified from the set of intermediate models to the semantic business model.
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11. A computer-implemented method of managing a semantic business model, the method comprising: transforming each business area model of a plurality of business area models for a business entity into a set of intermediate models using at least one of a set of processors, wherein each business area model is a business model for a particular area of the business entity, and wherein each intermediate model stores data for at least one corresponding business area model in a meta-modeling language that is the same for each intermediate model, and wherein the transforming includes: extracting a set of business area sub-models from each business area model of the plurality of business area models; and merging the sets of business area sub-models into the set of intermediate models; and generating the semantic business model by merging the set of intermediate models using at least one of the set of processors, wherein merging the set of intermediate models includes: automatically identifying objects in the intermediate models and relationships for the objects, wherein the relationships for the objects are determined by parsing an attribute for a corresponding object into terms by performing at least one of the following: determining word boundaries in the attribute, expanding abbreviations in the attribute, or determining the context information for each term in the attribute, such that there is a relationship if there is a correlation between terms; and adding objects and relationship information for the objects identified from the set of intermediate models to the semantic business model. 17. The method of claim 11 , further comprising providing the semantic business model to use by an analysis system.
| 0.642857 |
9,967,103 | 4 | 5 |
4. One or more computer storage media having a plurality of executable instructions embodied thereon, which, when executed by one or more processors, cause the one or more processors to perform a method comprising: maintaining, by an electronic signature platform, a plurality of predefined roles comprising a signer role and an advisor role, each role having a different set of permitted job functions enforced in electronic signing workflows; providing a request to a signer device for a first user identity to sign an electronic document based on the first user identify having the signer role; receiving, from the signer device, annotations made by the first user identify to the electronic document; receive a first user made assignment of a second user identity to the advisor role; receiving, from an advisor device, a reply that is responsive to the annotations, the reply made by the second user identity based on being assigned to the advisor role; receiving an electronic signature provided by the first user identify based on the first user identity having the signer role, the electronic signature being in response to the request to sign the electronic document after providing the reply to the signer device; and in response to a request from the first user identity, transmitting an audit trail to the signer device comprising one or more descriptions and one or more timestamps corresponding to the annotations made by the first user identity and the reply made by the second user identity based on the receiving of the annotations and the receiving of the reply, wherein the electronic signature platform is further caused to: receive, from a third user identity, the request to sign the electronic document based on the third user identity having the sender role; based on the third user identity having the sender role, receive, a request from the third user identity to remind the first user identity of the request to sign the electronic document; and automatically transmit a reminder notification to the second user identity based on determining that the second user identity has failed to provide the reply, based on the second user identity having the advisor role, and further based on the user selection of the option.
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4. One or more computer storage media having a plurality of executable instructions embodied thereon, which, when executed by one or more processors, cause the one or more processors to perform a method comprising: maintaining, by an electronic signature platform, a plurality of predefined roles comprising a signer role and an advisor role, each role having a different set of permitted job functions enforced in electronic signing workflows; providing a request to a signer device for a first user identity to sign an electronic document based on the first user identify having the signer role; receiving, from the signer device, annotations made by the first user identify to the electronic document; receive a first user made assignment of a second user identity to the advisor role; receiving, from an advisor device, a reply that is responsive to the annotations, the reply made by the second user identity based on being assigned to the advisor role; receiving an electronic signature provided by the first user identify based on the first user identity having the signer role, the electronic signature being in response to the request to sign the electronic document after providing the reply to the signer device; and in response to a request from the first user identity, transmitting an audit trail to the signer device comprising one or more descriptions and one or more timestamps corresponding to the annotations made by the first user identity and the reply made by the second user identity based on the receiving of the annotations and the receiving of the reply, wherein the electronic signature platform is further caused to: receive, from a third user identity, the request to sign the electronic document based on the third user identity having the sender role; based on the third user identity having the sender role, receive, a request from the third user identity to remind the first user identity of the request to sign the electronic document; and automatically transmit a reminder notification to the second user identity based on determining that the second user identity has failed to provide the reply, based on the second user identity having the advisor role, and further based on the user selection of the option. 5. The one or more computer storage media of claim 4 , wherein the method further comprises receiving, from a third user identity having a sender role, the request to sign the electronic document, the third user identity assigning the first user identity to the sender role based on the third user identity having the sender role.
| 0.634146 |
9,342,493 | 15 | 16 |
15. A computing system comprising: one or more processors; one or more computer-readable tangible non-transitory storage devices; a display device; program instructions, stored on at least one of the one or more computer-readable tangible storage devices for execution by at least one of the one or more processors, to receive a first edit to the document at a first position in a document in a document editing application, wherein the document editing application is navigable among different partial views of the document, and the document editing application displays a frame element that indicates a position of a presently displayed partial view of the document on the display device with reference to an entirety of the document; program instructions, stored on at least one of the one or more computer-readable tangible storage devices for execution by at least one of the one or more processors, to display on the display device, in response to the first edit to the document, a first marker, in or proximate to the frame element, indicating where the first position of the first edit is located with reference to the entirety of the document, wherein the first marker is separate from the first edit to the document; program instructions, stored on at least one of the one or more computer-readable tangible storage devices for execution by at least one of the one or more processors, to receive additional edits to the document including an nth edit to the document, wherein each of the additional edits is at an additional position in the document in the document editing application; program instructions, stored on at least one of the one or more computer-readable tangible storage devices for execution by at least one of the one or more processors, to display, in response to the additional edits to the document, additional markers, in or proximate to the frame element on the display device, indicating where the additional positions of the additional edits are located with reference to the entirety of the document, wherein each of the additional markers are separate from the additional edits to the document and are visually distinct from each other, wherein the first marker and the second marker are different numbers, and wherein the numbers for the markers are modified as additional markers are added, such that a first number always represents a position of a most recent user activity, and a second number always represents a position of a second most recent user activity; and program instructions, stored on at least one of the one or more computer-readable tangible storage devices for execution by at least one of the one or more processors, to remove the first marker in response to the nth edit to the document.
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15. A computing system comprising: one or more processors; one or more computer-readable tangible non-transitory storage devices; a display device; program instructions, stored on at least one of the one or more computer-readable tangible storage devices for execution by at least one of the one or more processors, to receive a first edit to the document at a first position in a document in a document editing application, wherein the document editing application is navigable among different partial views of the document, and the document editing application displays a frame element that indicates a position of a presently displayed partial view of the document on the display device with reference to an entirety of the document; program instructions, stored on at least one of the one or more computer-readable tangible storage devices for execution by at least one of the one or more processors, to display on the display device, in response to the first edit to the document, a first marker, in or proximate to the frame element, indicating where the first position of the first edit is located with reference to the entirety of the document, wherein the first marker is separate from the first edit to the document; program instructions, stored on at least one of the one or more computer-readable tangible storage devices for execution by at least one of the one or more processors, to receive additional edits to the document including an nth edit to the document, wherein each of the additional edits is at an additional position in the document in the document editing application; program instructions, stored on at least one of the one or more computer-readable tangible storage devices for execution by at least one of the one or more processors, to display, in response to the additional edits to the document, additional markers, in or proximate to the frame element on the display device, indicating where the additional positions of the additional edits are located with reference to the entirety of the document, wherein each of the additional markers are separate from the additional edits to the document and are visually distinct from each other, wherein the first marker and the second marker are different numbers, and wherein the numbers for the markers are modified as additional markers are added, such that a first number always represents a position of a most recent user activity, and a second number always represents a position of a second most recent user activity; and program instructions, stored on at least one of the one or more computer-readable tangible storage devices for execution by at least one of the one or more processors, to remove the first marker in response to the nth edit to the document. 16. The computing system of claim 15 , further comprising: program instructions, stored on at least one of the one or more computer-readable tangible storage devices for execution by at least one of the one or more processors, to record a first automatic annotation for the first edit to the document at the first position of the first edit and additional automatic annotations for the additional edits to the document at the additional positions of each of the additional edits to the document; program instructions, stored on at least one of the one or more computer-readable tangible storage devices for execution by at least one of the one or more processors, to display on the display device a history list comprising the first automatic annotation and the additional automatic annotations; program instructions, stored on at least one of the one or more computer-readable tangible storage devices for execution by at least one of the one or more processors, to receive a user input selecting first automatic annotation or any one of the additional automatic annotations from the history list; and program instructions, stored on at least one of the one or more computer-readable tangible storage devices for execution by at least one of the one or more processors, to display on the display device, in response to the user input selecting one of the automatic annotations, a partial view of the document showing either the first position of the first edit or the additional position of one of the additional edits based on the user input selecting either the first automatic annotation or one of the additional automatic annotations.
| 0.5 |
9,208,149 | 11 | 14 |
11. A non-transitory computer readable medium including computer executable instructions, wherein the instructions, when executed by a processor, cause the processor to perform a method comprising: translating an original sentence which is a character string of a first language into a forward-translated sentence which is a character string of a second language; acquiring, by translating an original word in the original sentence corresponding to a first forward-translated word in the forward-translated sentence, at least one second forward-translated word different from the first forward-translated word, to obtain candidate words including the first forward-translated word and the at least one second forward-translated word; calculating a fluency for each of the candidate words, the fluency indicating naturalness of the forward-translated sentence if each of the candidate words is replaced with the first forward-translated word; obtaining at least one reverse-translated word for each of the candidate words by reverse-translating each candidate word into the first language; calculating a semantic similarity between the original word and each reverse-translated word; and selecting a corrected forward-translated word to be replaced with the first forward-translated word from among the candidate words based on the semantic similarity and fluency.
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11. A non-transitory computer readable medium including computer executable instructions, wherein the instructions, when executed by a processor, cause the processor to perform a method comprising: translating an original sentence which is a character string of a first language into a forward-translated sentence which is a character string of a second language; acquiring, by translating an original word in the original sentence corresponding to a first forward-translated word in the forward-translated sentence, at least one second forward-translated word different from the first forward-translated word, to obtain candidate words including the first forward-translated word and the at least one second forward-translated word; calculating a fluency for each of the candidate words, the fluency indicating naturalness of the forward-translated sentence if each of the candidate words is replaced with the first forward-translated word; obtaining at least one reverse-translated word for each of the candidate words by reverse-translating each candidate word into the first language; calculating a semantic similarity between the original word and each reverse-translated word; and selecting a corrected forward-translated word to be replaced with the first forward-translated word from among the candidate words based on the semantic similarity and fluency. 14. The medium according to claim 11 , wherein the obtaining the at least one reverse-translated word extracts at least one word of the first language obtained by translating each candidate words as the at least one reverse-translated word.
| 0.605263 |
8,073,860 | 11 | 19 |
11. A user-interface system for incrementally providing fully qualified links to a set of relevant search engines, the system comprising: computer memory comprising instructions in computer readable form that when executed cause a computer system to: identify a set of search engines and associate each search engine of the set with at least one descriptive category to which the subject matter of the corresponding search engine relates; provide access to a database containing a collection of potential full queries, each potential full query associated in said database with at least one descriptive category; receive a partial search query entered on a keypad by a user; infer, after each keypress received from the user, a set of potential full queries intended by the user, based at least in part on the partial search query; select a subset of the identified search engines that are relevant to the inferred full queries based on comparing the inferred full queries with the descriptive categories associated with the search engines, wherein the selecting the subset of relevant search engines is further based on the descriptive categories associated with the provided potential full queries; and provide, for each of the selected search engines, a fully qualified link designed to directly launch a search for a relevant query string using the search engine.
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11. A user-interface system for incrementally providing fully qualified links to a set of relevant search engines, the system comprising: computer memory comprising instructions in computer readable form that when executed cause a computer system to: identify a set of search engines and associate each search engine of the set with at least one descriptive category to which the subject matter of the corresponding search engine relates; provide access to a database containing a collection of potential full queries, each potential full query associated in said database with at least one descriptive category; receive a partial search query entered on a keypad by a user; infer, after each keypress received from the user, a set of potential full queries intended by the user, based at least in part on the partial search query; select a subset of the identified search engines that are relevant to the inferred full queries based on comparing the inferred full queries with the descriptive categories associated with the search engines, wherein the selecting the subset of relevant search engines is further based on the descriptive categories associated with the provided potential full queries; and provide, for each of the selected search engines, a fully qualified link designed to directly launch a search for a relevant query string using the search engine. 19. The system of claim 11 wherein the relevant query string is the partial search query entered by the user.
| 0.78373 |
8,175,878 | 8 | 9 |
8. The system of claim 4 in which generating the trie further comprises using a third vector and a fourth vector, where the first vector identifies each distinct left word for each distinct context in the collection, the second vector identifies a count of distinct left words for each context in the collection, the third vector identifies each distinct predicted word for a given context in the collection, and the fourth vector identifies a count of distinct predicted words for each context in the collection.
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8. The system of claim 4 in which generating the trie further comprises using a third vector and a fourth vector, where the first vector identifies each distinct left word for each distinct context in the collection, the second vector identifies a count of distinct left words for each context in the collection, the third vector identifies each distinct predicted word for a given context in the collection, and the fourth vector identifies a count of distinct predicted words for each context in the collection. 9. The system of claim 8 in which assigning one or more values identifying each n-gram further comprises: identifying a context key corresponding to the context of the n-gram using the first vector and the second vector; identifying a key for the n-gram using the context key and the third vector and the fourth vector.
| 0.5 |
8,903,894 | 5 | 6 |
5. A first apparatus according to claim 4 , wherein the timing of the second HTTP request is a finish time of the time interval for loading of the web page.
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5. A first apparatus according to claim 4 , wherein the timing of the second HTTP request is a finish time of the time interval for loading of the web page. 6. A first apparatus according to claim 5 , wherein the second HTTP response includes data which causes the web browser to generate the second HTTP request in response to the web browser generating a page load Javascript event, and wherein the time interval for loading of the web page is measured according to the start time and the finish time.
| 0.5 |
10,114,897 | 1 | 3 |
1. A method comprising: identifying a most recent interest from user device submitted data; searching a database for instances of the most recent interest; creating a new category based on the most recent interest; storing the new category in a memory; combining the new category with weighted query search terms and submitting a combined query, separate weights assigned to query search terms according to validity of information found in each of local and remote memories, information found in local memories contributing to higher weights than information found in remote memories in response to private browsing not enabled on the user device, information found in remote memories contributing to higher weights than information found in local memories in response to private browsing being enabled on the user device; receiving combined query results; and creating a modified user interface based on the results of the combined query.
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1. A method comprising: identifying a most recent interest from user device submitted data; searching a database for instances of the most recent interest; creating a new category based on the most recent interest; storing the new category in a memory; combining the new category with weighted query search terms and submitting a combined query, separate weights assigned to query search terms according to validity of information found in each of local and remote memories, information found in local memories contributing to higher weights than information found in remote memories in response to private browsing not enabled on the user device, information found in remote memories contributing to higher weights than information found in local memories in response to private browsing being enabled on the user device; receiving combined query results; and creating a modified user interface based on the results of the combined query. 3. The method of claim 1 , further comprising: filtering the results of the combined query to include filtered results that match the combined query, wherein if results are received from both local and remote memories, the results with a more recent timestamp are weighted more than an older timestamp portion of results.
| 0.5 |
9,348,920 | 12 | 13 |
12. A computer-implemented method for information retrieval, the computer-implemented method comprising: identifying a plurality of segments within a plurality of documents, wherein identifying segments includes analyzing the plurality of documents for features indicative of possible section headings, including at least one of: casing, spacing, punctuation, common words, or groups of words; accessing a concept hierarchy including a plurality of concepts of interest to the user, the concept hierarchy further including concept keywords associated with respective concepts; for each concept, determining statistical likelihoods that respective identified segments are associated with the concept, the statistical likelihoods each based on at least one of, for each combination of a particular concept and a particular segment: a density of particular concept keywords in the particular segment, wherein the density is based at least on a ratio of a quantity of particular concept keywords in the particular segment to a quantity of words in the particular segment; or a distribution of particular concept keywords within the particular segment, wherein the distribution is based on at least one of a longest span in the particular segment without any mention of particular concept keywords or a median gap between consecutive mentions of respective concept keywords in the particular segment; generating an index from the plurality of concepts and the statistical likelihoods that respective concepts are in each of the determined respective segments; and storing the index in a non-transitory computer storage.
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12. A computer-implemented method for information retrieval, the computer-implemented method comprising: identifying a plurality of segments within a plurality of documents, wherein identifying segments includes analyzing the plurality of documents for features indicative of possible section headings, including at least one of: casing, spacing, punctuation, common words, or groups of words; accessing a concept hierarchy including a plurality of concepts of interest to the user, the concept hierarchy further including concept keywords associated with respective concepts; for each concept, determining statistical likelihoods that respective identified segments are associated with the concept, the statistical likelihoods each based on at least one of, for each combination of a particular concept and a particular segment: a density of particular concept keywords in the particular segment, wherein the density is based at least on a ratio of a quantity of particular concept keywords in the particular segment to a quantity of words in the particular segment; or a distribution of particular concept keywords within the particular segment, wherein the distribution is based on at least one of a longest span in the particular segment without any mention of particular concept keywords or a median gap between consecutive mentions of respective concept keywords in the particular segment; generating an index from the plurality of concepts and the statistical likelihoods that respective concepts are in each of the determined respective segments; and storing the index in a non-transitory computer storage. 13. The computer-implemented method of claim 12 , further comprising: receiving input comprising at least one search concept; in response to the input, query the non-transitory computer storage to retrieve a result set based on the at least one search concept and the index, the result set comprising at least one segment; and transmit the retrieved result set for presentation in a user interface.
| 0.587992 |
9,430,547 | 11 | 15 |
11. The system according to claim 1 , wherein the partitioner assigns a unique record identifier to each of the respective records stored in the collection according to the schema file, and generates a machine-readable index file associated with each of the partitions of the collection using the unique record identifier assigned to each respective record in the collection.
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11. The system according to claim 1 , wherein the partitioner assigns a unique record identifier to each of the respective records stored in the collection according to the schema file, and generates a machine-readable index file associated with each of the partitions of the collection using the unique record identifier assigned to each respective record in the collection. 15. The system according to claim 11 , wherein the search manager node receives a set of one or more new collections comprising one or more new records, and transmits a set of new collections to the one or more search conductor node according to the schema file, and wherein each respective search conductor node, responsive to receiving the one or more new collections, automatically populates one or more collections associated with the respective search conductor node with the set of new one or more records in accordance with the schema file.
| 0.5 |
8,868,529 | 6 | 7 |
6. The method of claim 1 , wherein a respective ride matcher object associated with a candidate set determines that a first ride intent object from the candidate set matches a second ride intent object in the candidate set if each ride preference value of the first ride intent object matches a corresponding ride preference value of the second ride intent object.
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6. The method of claim 1 , wherein a respective ride matcher object associated with a candidate set determines that a first ride intent object from the candidate set matches a second ride intent object in the candidate set if each ride preference value of the first ride intent object matches a corresponding ride preference value of the second ride intent object. 7. The method of claim 6 , wherein the ride matcher determines that a first ride preference value matches a second ride preference value if the first ride preference value and the second ride preference value are within a predetermined threshold.
| 0.5 |
8,001,562 | 13 | 14 |
13. The apparatus according to claim 1 , wherein the second acquisition unit is configured to acquire the estimated value, based on an estimated value corresponding to the user.
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13. The apparatus according to claim 1 , wherein the second acquisition unit is configured to acquire the estimated value, based on an estimated value corresponding to the user. 14. The apparatus according to claim 13 , wherein the extraction unit is configured to extract, from the estimated value distributions, a start time and an end time of a zone of the video content corresponding to one of the estimated value distributions which exceeds a threshold value.
| 0.5 |
7,979,252 | 1 | 4 |
1. A computer-implemented system that facilitates model enhancement, comprising: at least one processor configured to provide a modeling component that builds and runs a model based on data associated with a user, the model indicating interruptability of the user; and a sampling component that determines a time at which to obtain additional data associated with the user for building the model, the data being obtained by probing the user, and the time being determined based on: failure analysis of the model; and a state of the user.
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1. A computer-implemented system that facilitates model enhancement, comprising: at least one processor configured to provide a modeling component that builds and runs a model based on data associated with a user, the model indicating interruptability of the user; and a sampling component that determines a time at which to obtain additional data associated with the user for building the model, the data being obtained by probing the user, and the time being determined based on: failure analysis of the model; and a state of the user. 4. The system of claim 1 , wherein the data is associated with a user state.
| 0.837607 |
8,214,360 | 14 | 15 |
14. An apparatus in a clustering search engine, comprising: a processor; and a memory coupled to the processor, wherein the memory comprises instructions which, when executed by the processor, cause the processor to: receive, from a requesting client of a requesting user, a search query, a bookmark data structure, and a viewed content history of the requesting user, wherein the viewed content history comprises at least a portion of a browser history maintained by the Web browser at the requesting client; perform a search to obtain a search result set comprising a plurality of data elements that satisfy the search query; classify the search result set using the bookmark data structure to generate a clustered result set, wherein the clustered result set comprises the plurality of data elements clustered into a base taxonomy of categories and wherein the base taxonomy of categories is defined by the bookmark data structure; classify the viewed content history of the requesting user into the base taxonomy of categories, wherein the base taxonomy of categories comprises a plurality of categories; rank the categories of the clustered result set according to the classification of the viewed content history to form a ranked cluster result set; and return the clustered result set to the requesting client, wherein the bookmark data structure is maintained by a Web browser at the requesting client.
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14. An apparatus in a clustering search engine, comprising: a processor; and a memory coupled to the processor, wherein the memory comprises instructions which, when executed by the processor, cause the processor to: receive, from a requesting client of a requesting user, a search query, a bookmark data structure, and a viewed content history of the requesting user, wherein the viewed content history comprises at least a portion of a browser history maintained by the Web browser at the requesting client; perform a search to obtain a search result set comprising a plurality of data elements that satisfy the search query; classify the search result set using the bookmark data structure to generate a clustered result set, wherein the clustered result set comprises the plurality of data elements clustered into a base taxonomy of categories and wherein the base taxonomy of categories is defined by the bookmark data structure; classify the viewed content history of the requesting user into the base taxonomy of categories, wherein the base taxonomy of categories comprises a plurality of categories; rank the categories of the clustered result set according to the classification of the viewed content history to form a ranked cluster result set; and return the clustered result set to the requesting client, wherein the bookmark data structure is maintained by a Web browser at the requesting client. 15. The apparatus of claim 14 , wherein the bookmark data structure comprises a hierarchy of folders, wherein at least one folder in the hierarchy of folders has at least one bookmark stored therein, and wherein the base taxonomy of categories comprises a category for each folder within the hierarchy of folders.
| 0.615479 |
8,051,088 | 1 | 14 |
1. A computer-implemented method, comprising: generating a plurality of intent maps representing event structures based on sentences included in a plurality of documents, including a first intent map corresponding to a first document and a second intent map corresponding to a second document, wherein generating an intent map for a selected document includes: parsing the selected document to identify one or more sentences included in the selected document; and extracting an event structure from at least one sentence of the one or more sentences, wherein the event structure includes data descriptive of an actor and an action described in the at least one sentence; performing a comparison of at least one event structure of the first intent map and at least one event structure of the second intent map; and determining, based on the comparison, whether at least a portion of the first document is duplicative of at least a portion of the second document.
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1. A computer-implemented method, comprising: generating a plurality of intent maps representing event structures based on sentences included in a plurality of documents, including a first intent map corresponding to a first document and a second intent map corresponding to a second document, wherein generating an intent map for a selected document includes: parsing the selected document to identify one or more sentences included in the selected document; and extracting an event structure from at least one sentence of the one or more sentences, wherein the event structure includes data descriptive of an actor and an action described in the at least one sentence; performing a comparison of at least one event structure of the first intent map and at least one event structure of the second intent map; and determining, based on the comparison, whether at least a portion of the first document is duplicative of at least a portion of the second document. 14. The computer-implemented method of claim 1 , further comprising determining based on the comparison whether a threshold number of the event structures of the first intent map are similar to event structures of the second intent map, wherein the portion of the first document and the portion of the second document are determined to be duplicative when at least the threshold number of the event structures of the first intent map are determined to be similar to event structures of the second intent map.
| 0.5 |
8,930,817 | 1 | 7 |
1. A method for displaying photographs in a photographic slideshow, comprising: receiving with an electronic device contextual information; after the receiving: identifying with the electronic device at least one photograph that is associated with the received contextual information; and selecting with the electronic device at least one theme element that is associated with the received contextual information, after the identifying and after the selecting, generating with the electronic device the photographic slideshow, wherein the generated photographic slideshow comprises a first slideshow portion, and wherein the first slideshow portion comprises at least a portion of a first photograph, at least a portion of the identified at least one photograph, and the selected at least one theme element; and after the generating, displaying with the electronic device the first slideshow portion of the generated photographic slideshow by displaying each of the at least a portion of the first photograph, the at least a portion of the identified at least one photograph, and the selected at least one theme element, wherein during the displaying the first slideshow portion: the displayed at least a portion of the identified at least one photograph is within a boundary of the displayed at least one theme element; each of the displayed at least a portion of the identified at least one photograph and the displayed at least one theme element is at least partially overlaid on the displayed at least a portion of the first photograph; and each of the displayed at least a portion of the identified at least one photograph and the displayed at least one theme element moves relative to the displayed at least a portion of the first photograph.
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1. A method for displaying photographs in a photographic slideshow, comprising: receiving with an electronic device contextual information; after the receiving: identifying with the electronic device at least one photograph that is associated with the received contextual information; and selecting with the electronic device at least one theme element that is associated with the received contextual information, after the identifying and after the selecting, generating with the electronic device the photographic slideshow, wherein the generated photographic slideshow comprises a first slideshow portion, and wherein the first slideshow portion comprises at least a portion of a first photograph, at least a portion of the identified at least one photograph, and the selected at least one theme element; and after the generating, displaying with the electronic device the first slideshow portion of the generated photographic slideshow by displaying each of the at least a portion of the first photograph, the at least a portion of the identified at least one photograph, and the selected at least one theme element, wherein during the displaying the first slideshow portion: the displayed at least a portion of the identified at least one photograph is within a boundary of the displayed at least one theme element; each of the displayed at least a portion of the identified at least one photograph and the displayed at least one theme element is at least partially overlaid on the displayed at least a portion of the first photograph; and each of the displayed at least a portion of the identified at least one photograph and the displayed at least one theme element moves relative to the displayed at least a portion of the first photograph. 7. The method of claim 1 , wherein the contextual information comprises at least one of: a location; a time; a date; a calendar event; user preference information; and user history information.
| 0.610887 |
6,154,754 | 10 | 11 |
10. An automatic synthesizer of semantic information from multimedia documents as claimed in claim 9 wherein said AIU structure synthesizer comprises: an error corrector for receiving said raw AIU's; a primitive identifier connected to said error corrector; a basic synthesizer connected to said primitive identifier; and, an abstract synthesizer connected to said basic synthesizer for providing abstract objects to said hyperlinker.
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10. An automatic synthesizer of semantic information from multimedia documents as claimed in claim 9 wherein said AIU structure synthesizer comprises: an error corrector for receiving said raw AIU's; a primitive identifier connected to said error corrector; a basic synthesizer connected to said primitive identifier; and, an abstract synthesizer connected to said basic synthesizer for providing abstract objects to said hyperlinker. 11. An automatic synthesizer of semantic information from multimedia documents as claim in claim 10 wherein said error corrector comprises: a plurality of state machines each state machine for receiving a pattern specified in a rule and for providing an output; a composite state machine for unioning together each of said output from each of said plurality of state machines; an execute state machine for providing pattern fitting by receiving an input AIU and a composite output from said composite state machine and by providing a corrected raw AIU and a syntax error; and, a substitute single character for receiving said syntax error and for providing a single character substitution to said input AIU.
| 0.681818 |
7,961,903 | 4 | 9 |
4. A data input method comprising: pre-defining hotkey from amongst a plurality of keys on a keypad; actuating the hotkey once to indicate that a first key activation sequence corresponding to a first pattern will be executed on the keypad after the one depression of the hotkey; executing the first key activation sequence thereafter by sequentially activating a first set of keys on the keypad, the first set of keys selected in conformance to the first pattern that is a visual representation of a desired character superimposed over the plurality of keys; receiving from the keypad, input data corresponding to the sequential activation of the first set of keys; monitoring the keypad for detecting two sequential actuations of the hotkey as an indication that the first key activation sequence has been completed; and analyzing the input data to identify from the first pattern, the desired character.
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4. A data input method comprising: pre-defining hotkey from amongst a plurality of keys on a keypad; actuating the hotkey once to indicate that a first key activation sequence corresponding to a first pattern will be executed on the keypad after the one depression of the hotkey; executing the first key activation sequence thereafter by sequentially activating a first set of keys on the keypad, the first set of keys selected in conformance to the first pattern that is a visual representation of a desired character superimposed over the plurality of keys; receiving from the keypad, input data corresponding to the sequential activation of the first set of keys; monitoring the keypad for detecting two sequential actuations of the hotkey as an indication that the first key activation sequence has been completed; and analyzing the input data to identify from the first pattern, the desired character. 9. The method of claim 4 , wherein the input data comprises a sequence of numbers corresponding to the first set of keys.
| 0.843264 |
9,524,440 | 13 | 14 |
13. A system for providing handwriting recognition for a superimposed stroke input to a computing device, the computing device comprising a processor and at least one non-transitory computer readable medium for recognizing the input under control of the processor, said at least one program configured to: create a segmentation graph based on a plurality of input strokes, at least two of the strokes being at least partially superimposed on one another, wherein the segmentation graph consists of nodes and paths corresponding to character hypotheses formed by segmenting the input strokes to take into account the at least partially superimposed strokes; assign a recognition score to each node of the segmentation graph based on language recognition information; generate linguistic meaning of the input strokes by optimizing the recognition scores of the node paths of the segmentation graph based on a language model; and provide an output based on the simultaneous analysis of the segmentation graph, the recognition score, and the language model.
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13. A system for providing handwriting recognition for a superimposed stroke input to a computing device, the computing device comprising a processor and at least one non-transitory computer readable medium for recognizing the input under control of the processor, said at least one program configured to: create a segmentation graph based on a plurality of input strokes, at least two of the strokes being at least partially superimposed on one another, wherein the segmentation graph consists of nodes and paths corresponding to character hypotheses formed by segmenting the input strokes to take into account the at least partially superimposed strokes; assign a recognition score to each node of the segmentation graph based on language recognition information; generate linguistic meaning of the input strokes by optimizing the recognition scores of the node paths of the segmentation graph based on a language model; and provide an output based on the simultaneous analysis of the segmentation graph, the recognition score, and the language model. 14. A system according to claim 13 , wherein the segmentation graph is based on continuous input strokes that have been broken into constituting segments.
| 0.574586 |
7,929,767 | 1 | 2 |
1. A computing system for handwritten mathematical expression recognition, the computing system comprising: a component configured to receive from a user a handwritten mathematical expression; a component configured to receive from the user a selection of a dominant symbol within the handwritten mathematical expression; a subordinate sub-expression analysis component configured: to identify sub-expression information for the dominant symbol within a handwritten mathematical expression, the dominant symbol having shape information, and to recognize a character that the dominant symbol represents based on shape information of the dominant symbol and the identified sub-expression information, the sub-expression information including location of sub-expressions relative to the dominant symbol; and a component configured to display to the user a list of one or more characters that the dominant symbol represents and receive from the user a selection of a character from the list; a component configured to display to the user the selected character to replace the dominant symbol in the handwritten mathematical expression along with a placeholder corresponding to a sub-expression of the selected character; and a component configured to receive from the user a symbol to be inserted in the placeholder, the symbol representing a sub-expression of the selected character the components being implemented as computer-executable instructions stored in memory of the computing system for execution by a processing unit of the computing system.
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1. A computing system for handwritten mathematical expression recognition, the computing system comprising: a component configured to receive from a user a handwritten mathematical expression; a component configured to receive from the user a selection of a dominant symbol within the handwritten mathematical expression; a subordinate sub-expression analysis component configured: to identify sub-expression information for the dominant symbol within a handwritten mathematical expression, the dominant symbol having shape information, and to recognize a character that the dominant symbol represents based on shape information of the dominant symbol and the identified sub-expression information, the sub-expression information including location of sub-expressions relative to the dominant symbol; and a component configured to display to the user a list of one or more characters that the dominant symbol represents and receive from the user a selection of a character from the list; a component configured to display to the user the selected character to replace the dominant symbol in the handwritten mathematical expression along with a placeholder corresponding to a sub-expression of the selected character; and a component configured to receive from the user a symbol to be inserted in the placeholder, the symbol representing a sub-expression of the selected character the components being implemented as computer-executable instructions stored in memory of the computing system for execution by a processing unit of the computing system. 2. The computing system of claim 1 , wherein the subordinate sub-expression analysis component is further configured to determine whether an expression structure candidate is valid and to store information of the valid expression structure candidate in a parse tree.
| 0.5 |
9,753,546 | 1 | 2 |
1. A computer-implemented method comprising: processing a plurality of data frames, each data frame of the plurality of data frames comprising one or more body point locations of each of a plurality of collaborating users that are interfacing with an application at each of a plurality of time intervals; defining a spatial volume for each of the plurality of collaborating users based on the plurality of processed data frames; detecting a gesture performed by a first collaborating user of the plurality of collaborating users based on the plurality of processed data frames; determining the gesture to be an input gesture based on the gesture being performed by the first collaborating user in a first spatial volume; interpreting, by a machine having a memory and at least one processor, the input gesture based on a context of the first spatial volume, the context of the first spatial volume comprising an intersection volume between the first spatial volume and a second spatial volume for a second collaborating user; and providing an input command to the application based on the interpreted input gesture, the input command being different for the gesture being within the intersection volume than for the gesture being outside of the intersection volume.
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1. A computer-implemented method comprising: processing a plurality of data frames, each data frame of the plurality of data frames comprising one or more body point locations of each of a plurality of collaborating users that are interfacing with an application at each of a plurality of time intervals; defining a spatial volume for each of the plurality of collaborating users based on the plurality of processed data frames; detecting a gesture performed by a first collaborating user of the plurality of collaborating users based on the plurality of processed data frames; determining the gesture to be an input gesture based on the gesture being performed by the first collaborating user in a first spatial volume; interpreting, by a machine having a memory and at least one processor, the input gesture based on a context of the first spatial volume, the context of the first spatial volume comprising an intersection volume between the first spatial volume and a second spatial volume for a second collaborating user; and providing an input command to the application based on the interpreted input gesture, the input command being different for the gesture being within the intersection volume than for the gesture being outside of the intersection volume. 2. The computer-implemented method of claim 1 , wherein the context of the first spatial volume comprises a role of the first collaborating user.
| 0.772727 |
8,639,693 | 1 | 5 |
1. A computer-implemented method comprising: generating, using a processor, a plurality of collections of place listings, wherein the place listings in each collection includes a common trait, the common trait being based on a user's interaction with the place listings; identifying each place listing the user has interacted with as a seed listing; identifying a plurality of candidate listings, wherein at least one of the candidate listings is a member of a particular collection of the plurality of collections and at least one of the seed listings is also a member of the particular collection; determining the collections having at least one of the candidate listings and at least one of the seed listings as members; calculating, using the processor, a weight value for each seed listing and each candidate listing in each collection, wherein the weight value indicates a strength of association between either the seed listing and the corresponding collection or the candidate listing and the corresponding collection; calculating, using the processor, a recommendation score for each candidate listing, wherein the recommendation score is calculated based on the weight values of the seed listings and the weight values of the candidate listings, the recommendation score indicating a likelihood that the user would be interested in the candidate listing; and in the event that any of the recommendation scores exceed a threshold, providing at least one candidate listing, having a recommendation score exceeding the threshold, for display on a computing device.
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1. A computer-implemented method comprising: generating, using a processor, a plurality of collections of place listings, wherein the place listings in each collection includes a common trait, the common trait being based on a user's interaction with the place listings; identifying each place listing the user has interacted with as a seed listing; identifying a plurality of candidate listings, wherein at least one of the candidate listings is a member of a particular collection of the plurality of collections and at least one of the seed listings is also a member of the particular collection; determining the collections having at least one of the candidate listings and at least one of the seed listings as members; calculating, using the processor, a weight value for each seed listing and each candidate listing in each collection, wherein the weight value indicates a strength of association between either the seed listing and the corresponding collection or the candidate listing and the corresponding collection; calculating, using the processor, a recommendation score for each candidate listing, wherein the recommendation score is calculated based on the weight values of the seed listings and the weight values of the candidate listings, the recommendation score indicating a likelihood that the user would be interested in the candidate listing; and in the event that any of the recommendation scores exceed a threshold, providing at least one candidate listing, having a recommendation score exceeding the threshold, for display on a computing device. 5. The method of claim 1 , wherein the user's interaction of the place listings comprises browsing to a web page that includes at least one of the place listings.
| 0.623256 |
9,568,993 | 1 | 20 |
1. A method for automated avatar mood effects in a virtual world, comprising: detecting occurrence of a mood changing condition relatable to a user's avatar; determining an avatar mood effect from the plurality of predefined avatar mood effects to be applied to the user's avatar in the virtual world based on the detected mood changing condition; automatically applying the avatar mood effect to the user's avatar in the virtual world in response to detecting occurrence of the mood changing condition and determining an applicable avatar mood effect based on the detected mood changing condition; and presenting the automatically applied avatar mood effect in association with the user's avatar in the virtual world, wherein presenting the automatically applied avatar mood effect comprises presenting a predefined script spoken by the user's avatar in at least one of a visual form and an audible form and presenting different colored clothing worn by the user's avatar depending on the avatar mood effect applied, bright colored clothing worn by the user's avatar expressing a happy mood and dark, black or gray colored clothing worn by the user's avatar expressing a sad mood.
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1. A method for automated avatar mood effects in a virtual world, comprising: detecting occurrence of a mood changing condition relatable to a user's avatar; determining an avatar mood effect from the plurality of predefined avatar mood effects to be applied to the user's avatar in the virtual world based on the detected mood changing condition; automatically applying the avatar mood effect to the user's avatar in the virtual world in response to detecting occurrence of the mood changing condition and determining an applicable avatar mood effect based on the detected mood changing condition; and presenting the automatically applied avatar mood effect in association with the user's avatar in the virtual world, wherein presenting the automatically applied avatar mood effect comprises presenting a predefined script spoken by the user's avatar in at least one of a visual form and an audible form and presenting different colored clothing worn by the user's avatar depending on the avatar mood effect applied, bright colored clothing worn by the user's avatar expressing a happy mood and dark, black or gray colored clothing worn by the user's avatar expressing a sad mood. 20. The method of claim 1 , further comprising presenting different jewelry worn by the user's avatar depending on the avatar mood effect applied.
| 0.869643 |
5,418,942 | 12 | 13 |
12. The method of claim 11, wherein the information being stored includes both data and procedure information, and wherein data and procedure are arranged in a structure called a Context, wherein every Context contains both data and procedure, and wherein all procedure information is stored in the same manner as any other information in the computer memory.
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12. The method of claim 11, wherein the information being stored includes both data and procedure information, and wherein data and procedure are arranged in a structure called a Context, wherein every Context contains both data and procedure, and wherein all procedure information is stored in the same manner as any other information in the computer memory. 13. The method of claim 12, further comprising the step of providing a set of predefined procedures, the predefined procedures including: a) a Locate procedure for locating data within a data structure, b) a Delete procedure for deleting data within a data structure, c) a Precede procedure for adding data to a data structure, d) an Iteration procedure for iteratively executing a portion of a procedure, and e) an Enumeration procedure for executing one of a plurality of enumerated alternatives.
| 0.5 |
6,065,003 | 15 | 16 |
15. A method for providing an ordered list of target-entries that at least approximately match a search-entry, comprising the steps of: generating a find list of target-entries from a domain of valid results by, for each single-word valid result in the domain of valid results, generating a target-entry that is identical to the valid result; and for each multiple-word valid result in the domain of valid results, generating a target-entry for each permutation of the words of the valid result; selecting target-entries from the find list based on the initial characters of the search-entry and substitute characters for the initial characters of the search-entry; and comparing each of the target-entries with the search-entry; and ordering the target-entries based the results of the comparison.
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15. A method for providing an ordered list of target-entries that at least approximately match a search-entry, comprising the steps of: generating a find list of target-entries from a domain of valid results by, for each single-word valid result in the domain of valid results, generating a target-entry that is identical to the valid result; and for each multiple-word valid result in the domain of valid results, generating a target-entry for each permutation of the words of the valid result; selecting target-entries from the find list based on the initial characters of the search-entry and substitute characters for the initial characters of the search-entry; and comparing each of the target-entries with the search-entry; and ordering the target-entries based the results of the comparison. 16. The method of claim 15, wherein a plurality of scoring-heuristics are defined for comparing the target-entries to the search-entry, and the comparing step comprises the steps of: equating the score for each target-entry to an initial value; selecting scoring-heuristics from the plurality of scoring heuristics based on a particular target-entry and the search-entry to be compared; for each particular selected scoring-heuristic, applying the particular selected scoring-heuristic to compare the particular target-entry with the search-entry; and obtaining a sub-score as the result of applying the particular selected scoring-heuristic; and modify the score for the particular target-entry by the sub-score obtained.
| 0.5 |
9,582,270 | 6 | 8 |
6. The method of claim 1 , wherein said determining a similarity between the feature behavior model and the code behavior models comprises: obtaining a feature-related code scope method, receiving said feature behavior model and the code behavior model associated with the obtained feature-related code scope method; extracting all paths in the code behavior model; and for each path i extracted: generating a path i behavior model by a sequencing of the behavior signatures of the method-call statements in the extracted path.
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6. The method of claim 1 , wherein said determining a similarity between the feature behavior model and the code behavior models comprises: obtaining a feature-related code scope method, receiving said feature behavior model and the code behavior model associated with the obtained feature-related code scope method; extracting all paths in the code behavior model; and for each path i extracted: generating a path i behavior model by a sequencing of the behavior signatures of the method-call statements in the extracted path. 8. The method of claim 6 , wherein said determining a similarity between the path behavior model for the obtained feature-related code scope method and the feature behavior model comprises: determining, by said hardware processor, the total number of slave behavior in the feature behavior model and setting this value as Set all ; determining, by said hardware processor, how many slave behaviors in the feature behavior model also exist in the path i behavior model and setting this value to Set Hit ; determining, by said hardware processor, a maximum number of the behaviors in Set Hit that keep a same order with the corresponding “hitted” behaviors in the path behavior model and setting this value to Set sync ; and computing, by said hardware processor, a Similarity score representing the similarity between the path behavior model and the feature behavior model according to:
Similarity=(Set Hit *Set sync )/(Set all *Set all ).
| 0.5 |
8,818,992 | 5 | 8 |
5. The method of claim 1 , wherein at least one ranking criterion of the common ranking criteria comprises a plurality of ranking keys that are combined into a single ranking value.
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5. The method of claim 1 , wherein at least one ranking criterion of the common ranking criteria comprises a plurality of ranking keys that are combined into a single ranking value. 8. The method of claim 5 , further comprising causing, at least in part, updating the plurality of ranking keys in response to an over-the-air update of the user device.
| 0.761972 |
8,478,702 | 1 | 12 |
1. A method of identifying baselines for relationships, comprising: obtaining, at a computer, a first set of relationships between information atoms, wherein at least one of the information atoms comprises a descriptor of an individual; identifying baselines for the first set of relationships; obtaining a second set of relationships between the information atoms; determining a set of differences between the baselines and the relationships in the second set of relationships; identifying a third set of relationships between the set of differences and the descriptor; and associating the differences with the descriptor, based on the third set of relationships.
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1. A method of identifying baselines for relationships, comprising: obtaining, at a computer, a first set of relationships between information atoms, wherein at least one of the information atoms comprises a descriptor of an individual; identifying baselines for the first set of relationships; obtaining a second set of relationships between the information atoms; determining a set of differences between the baselines and the relationships in the second set of relationships; identifying a third set of relationships between the set of differences and the descriptor; and associating the differences with the descriptor, based on the third set of relationships. 12. The method of claim 1 , comprising; outputting one or more of the differences associated with the descriptor, based on the third set of relationships.
| 0.842536 |
10,032,463 | 15 | 16 |
15. The system of claim 13 , wherein the interaction history vector comprises a mapping of data regarding the plurality of prior interactions of the user to a first point in n-dimensional space, wherein n is a positive integer.
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15. The system of claim 13 , wherein the interaction history vector comprises a mapping of data regarding the plurality of prior interactions of the user to a first point in n-dimensional space, wherein n is a positive integer. 16. The system of claim 15 , wherein the data regarding the plurality of prior interactions of the user comprises data representing at least one of: an utterance, a search request, a purchase, or content access.
| 0.5 |
9,317,568 | 15 | 17 |
15. An electronic apparatus, comprising: at least one processor; and a memory device that stores instructions, wherein the at least one processor executes the instructions to: determine, based on a received search query, a first search result and a second search result; identify a first genre related to the first search result and a second genre related to the second search result, the first genre and the second genre being different genres; access a first genre-specific catalog associated with the first genre and a second genre-specific catalog associated with the second genre; determine a first content item included in the first genre-specific catalog that is associated with the first search result and a second content item included in the second genre-specific catalog that is associated with the second search result; calculate a perceived popularity for the first content item and a perceived popularity for the second content item by performing popularity queries for the first content item and the second content item, the popularity queries formed after receiving the search query; determine a presentation of the first search result and the second search result based on the calculated perceived popularity of the first content item and the calculated perceived popularity of the second content item; and provide the determined presentation of the first search result and the second search result for display on a device of a user.
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15. An electronic apparatus, comprising: at least one processor; and a memory device that stores instructions, wherein the at least one processor executes the instructions to: determine, based on a received search query, a first search result and a second search result; identify a first genre related to the first search result and a second genre related to the second search result, the first genre and the second genre being different genres; access a first genre-specific catalog associated with the first genre and a second genre-specific catalog associated with the second genre; determine a first content item included in the first genre-specific catalog that is associated with the first search result and a second content item included in the second genre-specific catalog that is associated with the second search result; calculate a perceived popularity for the first content item and a perceived popularity for the second content item by performing popularity queries for the first content item and the second content item, the popularity queries formed after receiving the search query; determine a presentation of the first search result and the second search result based on the calculated perceived popularity of the first content item and the calculated perceived popularity of the second content item; and provide the determined presentation of the first search result and the second search result for display on a device of a user. 17. The electronic apparatus of claim 15 , wherein: the first search result includes a link to a first digital instance that specifies the first content item; and the second search result includes a link to a second digital instance that specifies the second content item.
| 0.649485 |
6,049,799 | 10 | 11 |
10. The method of claim 9, wherein the document location data structure is a document location table containing data corresponding to a plurality of documents, including the document, and wherein the document location object corresponds to a single document location table.
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10. The method of claim 9, wherein the document location data structure is a document location table containing data corresponding to a plurality of documents, including the document, and wherein the document location object corresponds to a single document location table. 11. The method of claim 10, further comprising providing an update utility for updating document location data in the document location table.
| 0.5 |
7,720,804 | 1 | 13 |
1. A method of generating and maintaining a data warehouse, the method comprising: providing a predefined reusable metadata model having a data information model including metadata describing models for generating a data warehouse including metadata describing business logic for extracting information from one or more source systems and transforming the information into a data warehouse structure, and an information needs model including metadata regarding information needs for building reports, wherein the information needs model of the data information model comprises metadata defining user roles, metadata defining measures important to the user roles, members of the user roles, and context filters that apply to the members; providing data management services by a data warehouse solution system engine that generates a source framework model which is a semantic layer providing a logical business representation of the one or more source systems, to automatically generate a data warehouse from the one or more source systems using the data information model and the source framework model; providing a modeling user interface for presenting the data information model to a user for allowing the user to manipulate objects of the data warehouse; automatically generating the data warehouse from the one or more source systems using the data information model and the source framework model; and automatically generating reports using the information needs model and the data warehouse based on the metadata defining user roles, metadata defining measures important to the user roles, members of the user roles, and context filters that apply to the members.
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1. A method of generating and maintaining a data warehouse, the method comprising: providing a predefined reusable metadata model having a data information model including metadata describing models for generating a data warehouse including metadata describing business logic for extracting information from one or more source systems and transforming the information into a data warehouse structure, and an information needs model including metadata regarding information needs for building reports, wherein the information needs model of the data information model comprises metadata defining user roles, metadata defining measures important to the user roles, members of the user roles, and context filters that apply to the members; providing data management services by a data warehouse solution system engine that generates a source framework model which is a semantic layer providing a logical business representation of the one or more source systems, to automatically generate a data warehouse from the one or more source systems using the data information model and the source framework model; providing a modeling user interface for presenting the data information model to a user for allowing the user to manipulate objects of the data warehouse; automatically generating the data warehouse from the one or more source systems using the data information model and the source framework model; and automatically generating reports using the information needs model and the data warehouse based on the metadata defining user roles, metadata defining measures important to the user roles, members of the user roles, and context filters that apply to the members. 13. The method as claimed in claim 1 , wherein the providing data management services comprises generating extract-transform-load (ETL) code for extracting data from the source systems, transforming the data, and loading the data into the data warehouse based on the data information model.
| 0.685466 |
4,695,975 | 19 | 20 |
19. The visual communication device as claimed in claim 1, further comprising speech synthesizer means for pronouncing said natural language words received from said input means coincident with the display of the corresponding video images, and means for transmitting said pronunciations to said human receiver.
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19. The visual communication device as claimed in claim 1, further comprising speech synthesizer means for pronouncing said natural language words received from said input means coincident with the display of the corresponding video images, and means for transmitting said pronunciations to said human receiver. 20. The visual communications device as claimed in claim 19, further comprising means for synchronizing the pronunciations of said natural language words with the display of corresponding images.
| 0.5 |
8,156,184 | 1 | 7 |
1. A dialog server comprising: at least one processor; a memory, coupled to said at least one processor; a persistent storage device, accessible to said memory and said at least one processor; a position storage module, stored in a non-transitory manner in said persistent storage device, which, when loaded into said memory causes said at least one processor to store, in at least one of said memory and said persistent storage device, positional information on at least a first avatar and a second avatar; an utterance receiver module, stored in a non-transitory manner in said persistent storage device, which, when loaded into said memory causes said at least one processor to receive (i) at least one utterance from said first avatar, and (ii) at least one utterance strength representing an importance or attention level of said at least one utterance; an interest level calculator module, stored in a non-transitory manner in said persistent storage device, which, when loaded into said memory causes said at least one processor to calculate at least one interest level between said first avatar and said second avatar, based on said positional information; a message processor module, stored in a non-transitory manner in said persistent storage device, which, when loaded into said memory causes said at least one processor to generate a message from said at least one utterance in accordance with a value calculated from said at least one interest level and said at least one utterance strength; and a message transmitter module, stored in a non-transitory manner in said persistent storage device, which, when loaded into said memory causes said at least one processor to transmit said message to said second avatar; wherein said message processor module generates the message from said at least one utterance only when a value calculated from said at least one interest level and said at least one utterance strength is not less than a predetermined threshold value; and wherein said interest level calculator module calculates said at least one interest level by: calculating cosine of an angle between a facing direction of said first avatar and a line connecting said first and second avatars; dividing said cosine by a distance between said first and second avatars; and multiplying said cosine by a normalization factor which satisfies a requirement such that a sum of all interest levels between said first avatar and a number of avatars in a circle having a predetermined radius and centered about said first avatar is unity.
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1. A dialog server comprising: at least one processor; a memory, coupled to said at least one processor; a persistent storage device, accessible to said memory and said at least one processor; a position storage module, stored in a non-transitory manner in said persistent storage device, which, when loaded into said memory causes said at least one processor to store, in at least one of said memory and said persistent storage device, positional information on at least a first avatar and a second avatar; an utterance receiver module, stored in a non-transitory manner in said persistent storage device, which, when loaded into said memory causes said at least one processor to receive (i) at least one utterance from said first avatar, and (ii) at least one utterance strength representing an importance or attention level of said at least one utterance; an interest level calculator module, stored in a non-transitory manner in said persistent storage device, which, when loaded into said memory causes said at least one processor to calculate at least one interest level between said first avatar and said second avatar, based on said positional information; a message processor module, stored in a non-transitory manner in said persistent storage device, which, when loaded into said memory causes said at least one processor to generate a message from said at least one utterance in accordance with a value calculated from said at least one interest level and said at least one utterance strength; and a message transmitter module, stored in a non-transitory manner in said persistent storage device, which, when loaded into said memory causes said at least one processor to transmit said message to said second avatar; wherein said message processor module generates the message from said at least one utterance only when a value calculated from said at least one interest level and said at least one utterance strength is not less than a predetermined threshold value; and wherein said interest level calculator module calculates said at least one interest level by: calculating cosine of an angle between a facing direction of said first avatar and a line connecting said first and second avatars; dividing said cosine by a distance between said first and second avatars; and multiplying said cosine by a normalization factor which satisfies a requirement such that a sum of all interest levels between said first avatar and a number of avatars in a circle having a predetermined radius and centered about said first avatar is unity. 7. The dialog server according to claim 1 , further comprising: a position transmitter module, stored in a non-transitory manner in said persistent storage device, which, when loaded into said memory causes said at least one processor to transmit said positional information.
| 0.647436 |
8,271,484 | 6 | 8 |
6. The method of claim 3 wherein the adjusting includes, when an ancestor classification is not in the search results but at least one of its descendent classifications is in the search results, adding the ancestor classification to the search results.
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6. The method of claim 3 wherein the adjusting includes, when an ancestor classification is not in the search results but at least one of its descendent classifications is in the search results, adding the ancestor classification to the search results. 8. The method of claim 6 including indicating how well the terms of the items in the ancestor classification match the received search query based on how well the terms of the items in the descendent classification match the received search query.
| 0.5 |
6,110,226 | 9 | 10 |
9. A method of producing software code, comprising the steps of: producing code in a high-level programming language having both a source code and an intermediate form in which code is represented in terms of machine-independent code instructions, wherein a standard execution model of said high-level programming language is to interpret or compile at run time said machine-independent code instructions; and pre-compiling said first code for a specific machine using an optimizing ahead-of-time compiler, producing pre-compiled code; wherein pre-compiling comprises a stack slot compilation process in which a stack slot, representing data of a particular Java type, is mapped to one of a set of virtual registers according to a machine mode used to represent data of said particular Java type.
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9. A method of producing software code, comprising the steps of: producing code in a high-level programming language having both a source code and an intermediate form in which code is represented in terms of machine-independent code instructions, wherein a standard execution model of said high-level programming language is to interpret or compile at run time said machine-independent code instructions; and pre-compiling said first code for a specific machine using an optimizing ahead-of-time compiler, producing pre-compiled code; wherein pre-compiling comprises a stack slot compilation process in which a stack slot, representing data of a particular Java type, is mapped to one of a set of virtual registers according to a machine mode used to represent data of said particular Java type. 10. The method of claim 9, wherein stack slot compilation comprises: modeling a stack of said standard execution model using a stack of tree nodes; and expanding tree node expressions to obtain a machine-instruction-level code representation.
| 0.5 |
4,724,523 | 44 | 48 |
44. A method according to claim 41 in which said second pattern storing step comprises storing a signal representative of at least one of a partial verbal paradigm and a partial nominal paradigm.
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44. A method according to claim 41 in which said second pattern storing step comprises storing a signal representative of at least one of a partial verbal paradigm and a partial nominal paradigm. 48. A method according to claim 44 in which said partial paradigm storing step comprises storing a signal indicative of said corresponding stored entry being at least one of a verbal past tense inflectional form and a verbal past participle inflectional form.
| 0.5 |
9,378,735 | 1 | 4 |
1. A computer-implemented method comprising: under control of one or more computing devices configured with specific computer-executable instructions, obtaining a Gaussian mixture model-based (“GMM-based”) acoustic model; obtaining a neural network-based (“NN-based”) acoustic model; receiving an audio signal comprising speech; computing a first sequence of feature vectors from the audio signal; computing a GMM-based transform using the GMM-based acoustic model and the first sequence of feature vectors, wherein the GMM-based transform comprises a first linear portion and a first bias portion; computing a second linear portion of a NN-based transform by minimizing a first least squares difference function, wherein the first least squares difference function comprises a difference between the second linear portion and the first linear portion; computing a second bias portion of the NN-based transform by minimizing a second least squares difference function, wherein the second least squares difference function comprises a difference between the second bias portion and the first bias portion; computing a second sequence of feature vectors from the audio signal; computing a third sequence of feature vectors by applying the second linear portion and the second bias portion of the NN-based transform to the second sequence of feature vectors; performing speech recognition using the third sequence of feature vectors and the NN-based acoustic model generate speech processing results; and determining, using the speech processing results, an action to perform.
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1. A computer-implemented method comprising: under control of one or more computing devices configured with specific computer-executable instructions, obtaining a Gaussian mixture model-based (“GMM-based”) acoustic model; obtaining a neural network-based (“NN-based”) acoustic model; receiving an audio signal comprising speech; computing a first sequence of feature vectors from the audio signal; computing a GMM-based transform using the GMM-based acoustic model and the first sequence of feature vectors, wherein the GMM-based transform comprises a first linear portion and a first bias portion; computing a second linear portion of a NN-based transform by minimizing a first least squares difference function, wherein the first least squares difference function comprises a difference between the second linear portion and the first linear portion; computing a second bias portion of the NN-based transform by minimizing a second least squares difference function, wherein the second least squares difference function comprises a difference between the second bias portion and the first bias portion; computing a second sequence of feature vectors from the audio signal; computing a third sequence of feature vectors by applying the second linear portion and the second bias portion of the NN-based transform to the second sequence of feature vectors; performing speech recognition using the third sequence of feature vectors and the NN-based acoustic model generate speech processing results; and determining, using the speech processing results, an action to perform. 4. The computer-implemented method of claim 1 , wherein the first linear portion comprises a first matrix, wherein the second linear portion comprises a second matrix, wherein the first bias portion comprises a first column vector, and wherein the second bias portion comprises a second column vector.
| 0.62931 |
7,818,658 | 1 | 6 |
1. A method of generating a multimedia presentation by a multimedia presentation system, the multimedia presentation including a sequence of visual frames, the method comprising: reading a presentation script by the presentation system, the presentation script including presentation instructions for a combination of a plurality of visual assets into visual frames in a multimedia presentation; parsing the presentation script by the presentation system into presentation instructions for the visual assets; and generating, by the presentation system, a sequence of visual frames using the visual assets and the presentation instructions such that for each of a substantial number of individual visual frames, a selected plurality of the plurality of visual assets are combined into the individual visual frame according to the presentation instructions, wherein generating an individual visual frame includes: selecting selected visual assets from the plurality of visual assets to combine into the individual visual frame according to the presentation instructions, modifying the selected visual assets prior to combining according to the presentation instructions, positioning the modified selected visual assets in the individual visual frame according to the presentation instructions, and combining the modified selected visual assets such that when the positioning of a first and a second modified selected visual asset results in an intersection between the first and second modified visual assets in the individual visual frame, at the intersection of the first and second modified selected visual assets, the first and second modified selected visual assets are layered according to the presentation instructions.
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1. A method of generating a multimedia presentation by a multimedia presentation system, the multimedia presentation including a sequence of visual frames, the method comprising: reading a presentation script by the presentation system, the presentation script including presentation instructions for a combination of a plurality of visual assets into visual frames in a multimedia presentation; parsing the presentation script by the presentation system into presentation instructions for the visual assets; and generating, by the presentation system, a sequence of visual frames using the visual assets and the presentation instructions such that for each of a substantial number of individual visual frames, a selected plurality of the plurality of visual assets are combined into the individual visual frame according to the presentation instructions, wherein generating an individual visual frame includes: selecting selected visual assets from the plurality of visual assets to combine into the individual visual frame according to the presentation instructions, modifying the selected visual assets prior to combining according to the presentation instructions, positioning the modified selected visual assets in the individual visual frame according to the presentation instructions, and combining the modified selected visual assets such that when the positioning of a first and a second modified selected visual asset results in an intersection between the first and second modified visual assets in the individual visual frame, at the intersection of the first and second modified selected visual assets, the first and second modified selected visual assets are layered according to the presentation instructions. 6. The method of claim 1 , further comprising modifying the presentation script by a user using the presentation system.
| 0.668508 |
8,156,099 | 18 | 20 |
18. The device of claim 17 , where the processor is further to determine each split, of the plurality of splits, based on context information associated with the client device.
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18. The device of claim 17 , where the processor is further to determine each split, of the plurality of splits, based on context information associated with the client device. 20. The device of claim 18 , where the context information includes information derived from at least one of: an IP address associated with the client device, a profile associated with the client device, a search history associated with the client device, a language of the search query, or a hostname associated with the client device.
| 0.5 |
8,676,731 | 13 | 14 |
13. The method of claim 5 further comprising computing, in a production mode, from the combined component confidences, for each of the scanned data items, a confidence attribute, the confidence attribute indicative of a confidence that the scanned data item matches the data value.
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13. The method of claim 5 further comprising computing, in a production mode, from the combined component confidences, for each of the scanned data items, a confidence attribute, the confidence attribute indicative of a confidence that the scanned data item matches the data value. 14. The method of claim 13 further comprising: applying, from the confidence model, the determined component confidences to each of the transformations to compute a component confidence for each of the recognized values, and aggregating each of the computed component confidences to compute a confidence attribute indicative of a likelihood of accurate recognition of the data item as the corresponding data value.
| 0.5 |
9,800,618 | 15 | 18 |
15. The non-transitory computer-readable medium of claim 14 , wherein the instructions further cause the processor to: process, via the user agent, an indication from an identity manager specifying the user identity determined to satisfy the security policy requirements; and cause the evaluation operation to evaluate, in accordance with the indication, the privacy preference of the user identity specified in the determination.
|
15. The non-transitory computer-readable medium of claim 14 , wherein the instructions further cause the processor to: process, via the user agent, an indication from an identity manager specifying the user identity determined to satisfy the security policy requirements; and cause the evaluation operation to evaluate, in accordance with the indication, the privacy preference of the user identity specified in the determination. 18. The non-transitory computer-readable medium of claim 15 , wherein the instructions further cause the processor to conduct, via the user agent, a process to receive from the identity manager at least one indication of the at least one information card each representative of a user identity, and to determine a privacy preference for the at least one information card.
| 0.5 |
9,092,535 | 3 | 6 |
3. The method of claim 1 , wherein executing the first application program in response to detecting selection of the electronic message includes displaying an object on the display.
|
3. The method of claim 1 , wherein executing the first application program in response to detecting selection of the electronic message includes displaying an object on the display. 6. The method of claim 3 , wherein the object interacts with the body of text on the display.
| 0.694079 |
6,052,680 | 13 | 25 |
13. A system for processing an input, the system comprising: a) a first decision system having a first set of characteristic information; b) a second decision system having a second set of characteristic information; c) a relevance parameter; and d) a routing determination unit, the routing determination unit having i) an input for receiving the input, ii) a first output coupled with the first decision system, and iii) a second output coupled with the second decision system, and determining i) a first relevance value based on the input and the first set of characteristic information of the first decision system, ii) whether to apply the input to the first decision system based on the first relevance value and the relevance parameter, iii) a second relevance value based on the input and the second set of characteristic information of the second decision system, and iv) whether to apply the input to the second decision system based on the second relevance value and the relevance parameter.
|
13. A system for processing an input, the system comprising: a) a first decision system having a first set of characteristic information; b) a second decision system having a second set of characteristic information; c) a relevance parameter; and d) a routing determination unit, the routing determination unit having i) an input for receiving the input, ii) a first output coupled with the first decision system, and iii) a second output coupled with the second decision system, and determining i) a first relevance value based on the input and the first set of characteristic information of the first decision system, ii) whether to apply the input to the first decision system based on the first relevance value and the relevance parameter, iii) a second relevance value based on the input and the second set of characteristic information of the second decision system, and iv) whether to apply the input to the second decision system based on the second relevance value and the relevance parameter. 25. The system of claim 13 where if the input is applied to one of the first and second decision systems and the one of the first and second decision systems cannot process the input, the one of the first and second decision systems so informs the routing determination unit.
| 0.5 |
8,144,862 | 12 | 19 |
12. An apparatus for use in suppressing acoustic echo from a reference speech signal in a target speech signal, the target speech signal and the reference speech signal each being transmitted through a packet-based communications network and each having been encoded with a speech coder which generates speech parameters, the target speech signal comprising a sequence of target packets and the reference signal comprising a sequence of reference packets, the apparatus comprising: an energy estimator which estimates one or more reference speech energy levels in one or more reference packets based on one or more of said speech parameters generated by said encoding of said reference signal, the speech parameters comprising one or more linear predictive coding (LPC) coefficient parameters; an energy estimator which estimates a target speech energy level in a target packet based on one or more of said speech parameters generated by said encoding of said target signal; a comparator which compares the target speech energy level to said one or more reference speech energy levels; a spectral difference calculator which computes a spectral difference between the target packet and one or more of the reference packets based on said LPC coefficient parameters; and an echo detector which detects an echo in said target speech signal based on said comparison of said target speech energy level to said one or more reference speech energy levels and said spectral difference.
|
12. An apparatus for use in suppressing acoustic echo from a reference speech signal in a target speech signal, the target speech signal and the reference speech signal each being transmitted through a packet-based communications network and each having been encoded with a speech coder which generates speech parameters, the target speech signal comprising a sequence of target packets and the reference signal comprising a sequence of reference packets, the apparatus comprising: an energy estimator which estimates one or more reference speech energy levels in one or more reference packets based on one or more of said speech parameters generated by said encoding of said reference signal, the speech parameters comprising one or more linear predictive coding (LPC) coefficient parameters; an energy estimator which estimates a target speech energy level in a target packet based on one or more of said speech parameters generated by said encoding of said target signal; a comparator which compares the target speech energy level to said one or more reference speech energy levels; a spectral difference calculator which computes a spectral difference between the target packet and one or more of the reference packets based on said LPC coefficient parameters; and an echo detector which detects an echo in said target speech signal based on said comparison of said target speech energy level to said one or more reference speech energy levels and said spectral difference. 19. The apparatus of claim 12 further comprising an echo suppressor which suppresses said echo detected in said target speech signal by the echo detector.
| 0.841889 |
9,052,812 | 21 | 27 |
21. Non-transitory computer-readable media storing instructions for executing a computer-implemented method comprising: providing a graphical design environment to a user using a processor, wherein the graphical design environment includes a drag and drop interface that allows the user to add a widget to a design; displaying a note field in a note interface in the graphical design environment that accepts a text string from the user; exporting the design from the graphical design environment and storing the design as an intermittent coded representation of the design in a markup language format, wherein a set of at least two widgets that includes the widget are exported with the design; rendering the design in a design player using the intermitted coded representation of the design in the markup language format; displaying a discussion interface in the design player that: (i) is displayed in the design player consistently with the design; (ii) displays the text string from the user as a note; (iii) has a scrollbar; and (iv) accepts a comment from a second user regarding the note; allowing the second user to use the scrollbar to scroll through a set of notes that are associated with different portions of the design while viewing a fixed portion of the design; in response to selection of an interface element that is in the discussion interface with the note by the second user, placing the design player into a state wherein each widget in the set of at least two widgets is exposed for selection by the second user as a selected widget, and wherein selection of the selected widget by the second user links the note with the widget; and displaying the comment in the graphical design environment after being accepted in the discussion interface; wherein the text string and comment are: (i) stored in a data store along with an indication of the selected widget; (ii) read from the data store and rendered by the design player from the markup language format; and (iii) read from the data store and displayed in the graphical design environment from a design environment format; and wherein the data store is accessible to the graphical design environment and the design player.
|
21. Non-transitory computer-readable media storing instructions for executing a computer-implemented method comprising: providing a graphical design environment to a user using a processor, wherein the graphical design environment includes a drag and drop interface that allows the user to add a widget to a design; displaying a note field in a note interface in the graphical design environment that accepts a text string from the user; exporting the design from the graphical design environment and storing the design as an intermittent coded representation of the design in a markup language format, wherein a set of at least two widgets that includes the widget are exported with the design; rendering the design in a design player using the intermitted coded representation of the design in the markup language format; displaying a discussion interface in the design player that: (i) is displayed in the design player consistently with the design; (ii) displays the text string from the user as a note; (iii) has a scrollbar; and (iv) accepts a comment from a second user regarding the note; allowing the second user to use the scrollbar to scroll through a set of notes that are associated with different portions of the design while viewing a fixed portion of the design; in response to selection of an interface element that is in the discussion interface with the note by the second user, placing the design player into a state wherein each widget in the set of at least two widgets is exposed for selection by the second user as a selected widget, and wherein selection of the selected widget by the second user links the note with the widget; and displaying the comment in the graphical design environment after being accepted in the discussion interface; wherein the text string and comment are: (i) stored in a data store along with an indication of the selected widget; (ii) read from the data store and rendered by the design player from the markup language format; and (iii) read from the data store and displayed in the graphical design environment from a design environment format; and wherein the data store is accessible to the graphical design environment and the design player. 27. The non-transitory computer-readable media of claim 21 , wherein the method further comprises: restricting access to the text string such that it can only be edited by a predetermined set of users.
| 0.822751 |
8,218,859 | 15 | 20 |
15. A method for a transductive multi-label classification, implemented at least in part by a computing device, the method comprising: detecting concepts in a video content by using a hidden Markov random field formulation to identify labels for the concepts by: determining the transductive multi-label classification by measuring similarity scores based on the labels and pre-given labels on labeled data points; determining the labels are consistent between neighboring points; determining a multi-label interdependence on unlabeled data points is similar to a multi-label interdependence on the labeled data points; and analyzing the concepts for the multi-label interdependence on the unlabeled data points and the multi-label interdependence on the labeled data points by using chunklet analysis.
|
15. A method for a transductive multi-label classification, implemented at least in part by a computing device, the method comprising: detecting concepts in a video content by using a hidden Markov random field formulation to identify labels for the concepts by: determining the transductive multi-label classification by measuring similarity scores based on the labels and pre-given labels on labeled data points; determining the labels are consistent between neighboring points; determining a multi-label interdependence on unlabeled data points is similar to a multi-label interdependence on the labeled data points; and analyzing the concepts for the multi-label interdependence on the unlabeled data points and the multi-label interdependence on the labeled data points by using chunklet analysis. 20. The method of claim 15 , further comprising determining a multi-label interdependence between the labels on the labeled data points and on the unlabeled data points by analyzing relationships between concept pairs.
| 0.76044 |
8,078,602 | 16 | 17 |
16. A computer-readable storage medium comprising computer-readable program code, the computer-readable storage code being executable by a processor to perform a method, the method comprising: (A) obtaining consumer navigation data and behavioral data from multiple user computers; (B) for each of a plurality of web pages identified by the consumer navigation data, using said consumer navigation data and said behavioral data to determine implied consumer preference data; (C) building a search engine index using at least the consumer navigation and the implied consumer preference data, wherein said step of building comprises: (c1) using said consumer navigation data to determine a particular web page viewed by at least one of said multiple user computers; (c2) parsing a copy of the particular web page to determine the occurrence of one or more keywords in the particular web page; and (c3) ranking the particular web page relative to at least some of the one or more keywords, said ranking being based at least in part on results of the parsing and on implied consumer preference data associated with the particular web page; (D) receiving a search request based on a search keyword; and (E) based on the search request, retrieving results from the search engine index, said results identifying a plurality of web pages, and (F) providing the results, ordered, at least in part, by a ranking of each of the plurality of web pages relative to the search keyword.
|
16. A computer-readable storage medium comprising computer-readable program code, the computer-readable storage code being executable by a processor to perform a method, the method comprising: (A) obtaining consumer navigation data and behavioral data from multiple user computers; (B) for each of a plurality of web pages identified by the consumer navigation data, using said consumer navigation data and said behavioral data to determine implied consumer preference data; (C) building a search engine index using at least the consumer navigation and the implied consumer preference data, wherein said step of building comprises: (c1) using said consumer navigation data to determine a particular web page viewed by at least one of said multiple user computers; (c2) parsing a copy of the particular web page to determine the occurrence of one or more keywords in the particular web page; and (c3) ranking the particular web page relative to at least some of the one or more keywords, said ranking being based at least in part on results of the parsing and on implied consumer preference data associated with the particular web page; (D) receiving a search request based on a search keyword; and (E) based on the search request, retrieving results from the search engine index, said results identifying a plurality of web pages, and (F) providing the results, ordered, at least in part, by a ranking of each of the plurality of web pages relative to the search keyword. 17. The computer-readable storage medium of claim 16 , wherein the consumer navigation data include addresses of web pages viewed at a user computer.
| 0.641827 |
6,014,517 | 1 | 5 |
1. A programming development apparatus for automatically creating interfaces between computer programs written in different computer languages, comprising: a comment parser for parsing the commented section of a first computer program written in a first computer language to identify the defining terms, and formatters of arguments and associating them into related groups; a memory array for storing the related groups of arguments; a wrapper scripter for scripting a wrapper in code written in the first computer language to transform arguments stored in the memory array from the format used by a second computer program written in a second computer language to the format used by the first computer program, and from the format used by the first computer program to the format used by the second computer program so that the second computer program is able to call the first computer program and send arguments to the first computer program and also receive arguments from the first computer program when the first computer program returns.
|
1. A programming development apparatus for automatically creating interfaces between computer programs written in different computer languages, comprising: a comment parser for parsing the commented section of a first computer program written in a first computer language to identify the defining terms, and formatters of arguments and associating them into related groups; a memory array for storing the related groups of arguments; a wrapper scripter for scripting a wrapper in code written in the first computer language to transform arguments stored in the memory array from the format used by a second computer program written in a second computer language to the format used by the first computer program, and from the format used by the first computer program to the format used by the second computer program so that the second computer program is able to call the first computer program and send arguments to the first computer program and also receive arguments from the first computer program when the first computer program returns. 5. The apparatus of claim 1, wherein the comment parser further comprises a means for accepting external names to be added to arguments located in the commented section of the first computer program.
| 0.5 |
8,065,655 | 14 | 16 |
14. The method of claim 12 further comprising creating the autogeneration tool on a first server; and running the autogeneration tool on a second server.
|
14. The method of claim 12 further comprising creating the autogeneration tool on a first server; and running the autogeneration tool on a second server. 16. The method of claim 14 further comprising generating at least one artifact that corresponds to the ontology class and properties.
| 0.755515 |
7,734,091 | 1 | 7 |
1. An apparatus for pattern-matching characters in a stream of received characters, the apparatus comprising: a character processing unit comprising means for storing characters, and means for comparing a received input character with at least one stored character; and a controller for controlling the character processing unit, the controller including means for receiving an input stream of characters of a document to be pattern-matched and means for controlling the character processing unit to compare characters from the input stream with characters stored by the character processing unit, the controller comprising: means for assessing characters in the received character stream and then selectively controlling and using the character processing unit on the basis of that assessment; and means for parsing the document, wherein the character processing unit further comprises means for providing feedback to the controller as to whether the characters from the input stream match the characters stored by the character processing unit, the feedback enabling the controller to parse the document.
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1. An apparatus for pattern-matching characters in a stream of received characters, the apparatus comprising: a character processing unit comprising means for storing characters, and means for comparing a received input character with at least one stored character; and a controller for controlling the character processing unit, the controller including means for receiving an input stream of characters of a document to be pattern-matched and means for controlling the character processing unit to compare characters from the input stream with characters stored by the character processing unit, the controller comprising: means for assessing characters in the received character stream and then selectively controlling and using the character processing unit on the basis of that assessment; and means for parsing the document, wherein the character processing unit further comprises means for providing feedback to the controller as to whether the characters from the input stream match the characters stored by the character processing unit, the feedback enabling the controller to parse the document. 7. The apparatus of claim 1 , wherein the controller comprises means for pausing the processing and input of the received character stream.
| 0.681193 |
4,829,423 | 45 | 48 |
45. The method of claim 44, wherein the grammar provided in step (a) is a syntactically constrained grammar.
|
45. The method of claim 44, wherein the grammar provided in step (a) is a syntactically constrained grammar. 48. The method of claim 45, wherein the grammar is regular.
| 0.644578 |
8,635,066 | 2 | 3 |
2. The computer-implemented method of claim 1 , further comprising: identifying portions of the facial image sequence that indicate a particular speaker is silent; and filtering out portions of the audio input that correspond to the portions of the facial image sequence that indicate the particular speaker is silent.
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2. The computer-implemented method of claim 1 , further comprising: identifying portions of the facial image sequence that indicate a particular speaker is silent; and filtering out portions of the audio input that correspond to the portions of the facial image sequence that indicate the particular speaker is silent. 3. The computer-implemented method of claim 2 , wherein the identifying includes identifying portions of the facial image sequence that indicate the particular speaker is silent based on facial features of the particular speaker shown in the portions of the facial image sequence.
| 0.5 |
8,332,624 | 1 | 2 |
1. A method comprising: constructing a reduced ordered binary decision diagram from a resource description framework graph; computing a hash identifier corresponding to the decision diagram; and causing, at least in part, a storing of the hash identifier with the decision diagram.
|
1. A method comprising: constructing a reduced ordered binary decision diagram from a resource description framework graph; computing a hash identifier corresponding to the decision diagram; and causing, at least in part, a storing of the hash identifier with the decision diagram. 2. A method of claim 1 , wherein the constructing step comprises: serializing the resource description framework graph into variables of a predetermined format; determining a bit size of the variables by calculating sizes of the variables or using a fixed size; and constructing a representation of the reduced ordered binary decision diagram from the variables.
| 0.502747 |
7,899,666 | 35 | 38 |
35. The method of claim 28 wherein each of the domains is a word that indicates a linguistic context in which an associated synset is used in a particular language.
|
35. The method of claim 28 wherein each of the domains is a word that indicates a linguistic context in which an associated synset is used in a particular language. 38. The method of claim 35 wherein for each association between one of the domains and one of the synsets, a weight referred to as a domain percentage is assigned to indicate how frequently a meaning related to the synset is used in context of the associated domain versus use of synset's meaning to the synset in common general language.
| 0.5 |
5,406,477 | 3 | 11 |
3. A computer-based method to project the future value of a variable that relates to an enterprise, the method comprising the steps of: creating a model of the enterprise wherein the model is a frame-based model of the enterprise including a set of frames, each frame in the model representing real-world knowledge, each frame comprising a list of relationships, each relationship in the list of relationships specifying a relationship with another frame, each frame further comprising a list of attributes that store data relating to the frame; storing the model of the enterprise in a knowledge base; providing a set of reasoning methods; providing a set of reconciliation rules; accepting as input a query that requests information about the future value of the variable; applying each reasoning method in the set of reasoning methods, each reasoning method utilizing the set of frees, to generate from each reasoning method an intermediate hypothesis as to the future value of the variable; and reconciling between each intermediate hypothesis to obtain the future value of the variable by: (i) locating available reconciliation rules from the set of reconciliation rules; (ii) ordering the available reconciliation rules according to a pre-selected preference scheme; and (iii) applying the available reconciliation rules in the order determined at step (ii).
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3. A computer-based method to project the future value of a variable that relates to an enterprise, the method comprising the steps of: creating a model of the enterprise wherein the model is a frame-based model of the enterprise including a set of frames, each frame in the model representing real-world knowledge, each frame comprising a list of relationships, each relationship in the list of relationships specifying a relationship with another frame, each frame further comprising a list of attributes that store data relating to the frame; storing the model of the enterprise in a knowledge base; providing a set of reasoning methods; providing a set of reconciliation rules; accepting as input a query that requests information about the future value of the variable; applying each reasoning method in the set of reasoning methods, each reasoning method utilizing the set of frees, to generate from each reasoning method an intermediate hypothesis as to the future value of the variable; and reconciling between each intermediate hypothesis to obtain the future value of the variable by: (i) locating available reconciliation rules from the set of reconciliation rules; (ii) ordering the available reconciliation rules according to a pre-selected preference scheme; and (iii) applying the available reconciliation rules in the order determined at step (ii). 11. The method of claim 3 wherein the set of reasoning methods includes proportionality reasoning.
| 0.883055 |
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