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1. A method comprising: receiving, by a computer system, first event data indicative of computer network activity of a plurality of users and network devices in a computer network; generating, by the computer system, classification metadata for each of the network devices and users, based on the first event data, to indicate relevance in a network security context of each of the users and network devices; identifying, by the computer system, usage relationships between one or more of the users and one or more of the network devices, based on first event data; assigning, by the computer system, usage similarity scores to the network devices based on the identified usage relationships, the usage similarity scores being indicative of which of the network devices have been used by the same or similar group of users; receiving, by the computer system, second event data indicative of computer network activity of a particular user of the plurality of users; and detecting, by the computer system and in response to the second event data, an anomaly indicative that the particular user has interacted with a particular network device with which the particular user does not normally interact, based on the usage similarity scores and the classification metadata.
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1. A method comprising: receiving, by a computer system, first event data indicative of computer network activity of a plurality of users and network devices in a computer network; generating, by the computer system, classification metadata for each of the network devices and users, based on the first event data, to indicate relevance in a network security context of each of the users and network devices; identifying, by the computer system, usage relationships between one or more of the users and one or more of the network devices, based on first event data; assigning, by the computer system, usage similarity scores to the network devices based on the identified usage relationships, the usage similarity scores being indicative of which of the network devices have been used by the same or similar group of users; receiving, by the computer system, second event data indicative of computer network activity of a particular user of the plurality of users; and detecting, by the computer system and in response to the second event data, an anomaly indicative that the particular user has interacted with a particular network device with which the particular user does not normally interact, based on the usage similarity scores and the classification metadata. 8. The method of claim 1 , wherein said detecting an anomaly comprises: detecting an anomaly in response to detecting that a particular user has interacted with a particular network device when a usage similarity score of the particular network device fails to satisfy a specific closeness criterion relative to usage similarity scores of network devices with which the particular user usually interacts.
| 0.797702 |
5. The non-transitory computer readable medium of claim 4 , each cluster similarity measure comprising a ratio of the intracluster variance of a similarity metric to an intercluster variance of the similarity metric.
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5. The non-transitory computer readable medium of claim 4 , each cluster similarity measure comprising a ratio of the intracluster variance of a similarity metric to an intercluster variance of the similarity metric. 6. The non-transitory computer readable medium of claim 5 , wherein the similarity metric is a cosine difference between feature vectors.
| 0.94187 |
1. A document processing method comprising: incrementally, for each of a set of samples of a document, extracting local features from the sample; for each of the samples, computing a local score based on the local features extracted from the sample; estimating a global score for the document based on the local scores currently computed; and computing a confidence in a decision for the estimated global score, the computed confidence being based on the local scores currently computed; and outputting a decision for the document based on the estimated score when the computed confidence in the decision reaches a threshold value, the extracting of local features, computing the local score, estimating of the global score, and the computing of the confidence in the decision being repeated with additional samples until the computed confidence in the decision reaches the threshold value.
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1. A document processing method comprising: incrementally, for each of a set of samples of a document, extracting local features from the sample; for each of the samples, computing a local score based on the local features extracted from the sample; estimating a global score for the document based on the local scores currently computed; and computing a confidence in a decision for the estimated global score, the computed confidence being based on the local scores currently computed; and outputting a decision for the document based on the estimated score when the computed confidence in the decision reaches a threshold value, the extracting of local features, computing the local score, estimating of the global score, and the computing of the confidence in the decision being repeated with additional samples until the computed confidence in the decision reaches the threshold value. 18. The method of claim 1 , further comprising, based on the local scores for samples computed so far, estimating a number of additional samples for which local features are still to be extracted and the local score is still to be computed and reestimating the global score for the document and recomputing the confidence in the decision when the local scores for that number of samples have been computed.
| 0.599247 |
1. A method for processing user profiles of a plurality of users in an electronic community, said method comprising: extracting, by a processor of a data processing system, noun phrases from activities of each user logged in an activity log server, each user having an existing user profile stored in a user profile and relationship database that is external to the activity log server; said processor updating the existing user profiles in the user profile and relationship database from the extracted noun phrases, a keyword being associated with each determined noun phrase and being within a semantic hierarchical tree, said updating based on a usage frequency of the extracted noun phrases and an importance value of the keywords; said processor executing a similarity based clustering algorithm to generate clusters of the updated user profiles, each cluster comprising a group of users of the users in the electronic community, each cluster representing a relationship between the users in each group; and said processor storing each cluster in the user profile and relationship database, wherein the similarity based clustering algorithm comprises a member importance function and a member similarity function, wherein the member importance function ascertains an importance value of keywords as a depth of said keywords in the semantic hierarchical tree, wherein the member similarity function ascertains a similarity distance between keywords as a path distance between said keywords in the semantic hierarchical tree, and wherein said executing the similarity based clustering algorithm comprises: using the member importance function and the member similarity function to ascertain the clusters.
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1. A method for processing user profiles of a plurality of users in an electronic community, said method comprising: extracting, by a processor of a data processing system, noun phrases from activities of each user logged in an activity log server, each user having an existing user profile stored in a user profile and relationship database that is external to the activity log server; said processor updating the existing user profiles in the user profile and relationship database from the extracted noun phrases, a keyword being associated with each determined noun phrase and being within a semantic hierarchical tree, said updating based on a usage frequency of the extracted noun phrases and an importance value of the keywords; said processor executing a similarity based clustering algorithm to generate clusters of the updated user profiles, each cluster comprising a group of users of the users in the electronic community, each cluster representing a relationship between the users in each group; and said processor storing each cluster in the user profile and relationship database, wherein the similarity based clustering algorithm comprises a member importance function and a member similarity function, wherein the member importance function ascertains an importance value of keywords as a depth of said keywords in the semantic hierarchical tree, wherein the member similarity function ascertains a similarity distance between keywords as a path distance between said keywords in the semantic hierarchical tree, and wherein said executing the similarity based clustering algorithm comprises: using the member importance function and the member similarity function to ascertain the clusters. 5. The method of claim 1 , wherein a digital hierarchical dictionary comprises synsets, each synset being a set of cognitive synonyms consisting of noun phrases, said synsets being interlinked into the semantic hierarchical tree within the digital hierarchical dictionary.
| 0.656061 |
11. The method of claim 1 , further comprising storing the translated code in a cache.
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11. The method of claim 1 , further comprising storing the translated code in a cache. 13. The method of claim 11 , further comprising deleting entries from a defragmentation pointer onwards until sufficient space is available in the cache for a block of translated code that would not otherwise fit in the cache.
| 0.923723 |
27. The computer-readable media of claim 16 , further comprising: restructuring the one or more identified components in the defined ecosystem or context ‘A’ into a new ecosystem or context ‘B’.
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27. The computer-readable media of claim 16 , further comprising: restructuring the one or more identified components in the defined ecosystem or context ‘A’ into a new ecosystem or context ‘B’. 28. The computer-readable media of claim 27 , wherein restructuring includes: mapping the movement of the one or more components from a current location to a potential location on a component interface definition meta-structure map; and determining whether the mapped movement of the one or more identified components on the component interface definition meta-structure map resolves the one or more gaps.
| 0.883342 |
1. A method for assigning a gesture dictionary, the method comprising: receiving data representative of a user in a physical space; processing the received data to identify a first motion or pose by the user that invokes an input command to a computer; correlating the first motion or pose to a first gesture dictionary of a plurality of gesture dictionaries, each gesture dictionary of the plurality of gesture dictionaries comprising a set of input commands to the computer that may be invoked by motions or poses of the user reflected in the received data; and assigning the first gesture dictionary to the user, the first gesture dictionary corresponding to the first motion or pose; and processing additional captured data with the first gesture dictionary to identify whether a second motion or pose by the user in the additional captured data invokes an input command to the computer.
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1. A method for assigning a gesture dictionary, the method comprising: receiving data representative of a user in a physical space; processing the received data to identify a first motion or pose by the user that invokes an input command to a computer; correlating the first motion or pose to a first gesture dictionary of a plurality of gesture dictionaries, each gesture dictionary of the plurality of gesture dictionaries comprising a set of input commands to the computer that may be invoked by motions or poses of the user reflected in the received data; and assigning the first gesture dictionary to the user, the first gesture dictionary corresponding to the first motion or pose; and processing additional captured data with the first gesture dictionary to identify whether a second motion or pose by the user in the additional captured data invokes an input command to the computer. 10. The method of claim 1 , wherein the gesture dictionary is assigned to the user in real time upon capturing the data representative of the user.
| 0.644054 |
11. An e-learning proxy comprising: instructions stored on a non-transitory computing-device-readable medium, wherein the instructions are configured when executed on a computing device to cause the computing device to: send a request to a network-side licensing/reporting server to verify a license to a specific instance of content in response to a learner's request for access to the content; receive from the licensing/reporting server, in response to a successful verification of the license, a location designator for a content player; and instruct a learner's browser to access and load the content player via the location designator; wherein: the proxy instructions include license information rendering the proxy specific to the specific instance of licensed content; the proxy itself does not contain any e-learning course content; the proxy is configured to be loaded from the user's LMS to the learner's browser in response to a request from the learner for access to the licensed content; the proxy instructions are configured, when the proxy is loaded to the learner's browser, to instruct the implementation script to initialize a runtime compliant with the e-learning standard; and the proxy instructions are further configured to use the runtime to communicate with the LMS and to report status updates of the learner's interaction with the content.
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11. An e-learning proxy comprising: instructions stored on a non-transitory computing-device-readable medium, wherein the instructions are configured when executed on a computing device to cause the computing device to: send a request to a network-side licensing/reporting server to verify a license to a specific instance of content in response to a learner's request for access to the content; receive from the licensing/reporting server, in response to a successful verification of the license, a location designator for a content player; and instruct a learner's browser to access and load the content player via the location designator; wherein: the proxy instructions include license information rendering the proxy specific to the specific instance of licensed content; the proxy itself does not contain any e-learning course content; the proxy is configured to be loaded from the user's LMS to the learner's browser in response to a request from the learner for access to the licensed content; the proxy instructions are configured, when the proxy is loaded to the learner's browser, to instruct the implementation script to initialize a runtime compliant with the e-learning standard; and the proxy instructions are further configured to use the runtime to communicate with the LMS and to report status updates of the learner's interaction with the content. 14. The e-learning proxy of claim 11 , wherein the proxy further comprises: a hypertext markup language (HTML) page configured to receive, for operation within a learner's browser, either or both of an e-learning standard-based manifest and an e-learning standard-based implementation script.
| 0.655402 |
1. A method comprising: receiving a query expression against a collection of XML documents stored in one or more database tables, wherein the collection of XML documents are stored based on a particular data storage format; determining that the query expression contains an updating expression that conforms to a language for querying XML documents, the updating expression specifying a particular operation for updating XML data; in response to determining that the query expression contains the updating expression that conforms to the language for querying XML documents, rewriting the query expression to form a first rewritten query expression comprising one or more path-based query operators, wherein the one or more path-based query operators are selected based on the particular operation for updating XML data specified by the updating expression; based on the particular data storage format storing the collection of XML documents, rewriting the first rewritten query expression to form a second rewritten query expression comprising one or more storage format specific query operators configured to update particular XML data of the collection of XML documents; and wherein the method is performed by one or more computing devices.
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1. A method comprising: receiving a query expression against a collection of XML documents stored in one or more database tables, wherein the collection of XML documents are stored based on a particular data storage format; determining that the query expression contains an updating expression that conforms to a language for querying XML documents, the updating expression specifying a particular operation for updating XML data; in response to determining that the query expression contains the updating expression that conforms to the language for querying XML documents, rewriting the query expression to form a first rewritten query expression comprising one or more path-based query operators, wherein the one or more path-based query operators are selected based on the particular operation for updating XML data specified by the updating expression; based on the particular data storage format storing the collection of XML documents, rewriting the first rewritten query expression to form a second rewritten query expression comprising one or more storage format specific query operators configured to update particular XML data of the collection of XML documents; and wherein the method is performed by one or more computing devices. 8. The method of claim 1 , wherein the first rewritten query expression further comprises one or more index-based query operators, wherein the index-based query operators identify an XML index.
| 0.806773 |
17. A speech recognition method according to claim 14 , wherein the step of outputting the second score comprises the step of performing prosodic recognition using a tone model which is modeled using a time-rate change of a pitch in the vowel interval as a feature amount.
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17. A speech recognition method according to claim 14 , wherein the step of outputting the second score comprises the step of performing prosodic recognition using a tone model which is modeled using a time-rate change of a pitch in the vowel interval as a feature amount. 18. A speech recognition method according to claim 17 , wherein the tone model is independent of the phonetic model used to calculate the acoustic distance.
| 0.891101 |
1. A method comprising: identifying, by an audio cue generator executing on a processing device, utterances in audio data of a digital interview, wherein the utterances each comprise a group of one or more words spoken by a candidate in the digital interview; generating, by the audio cue generator, audio cues of the digital interview based on the identified utterances; applying the audio cues to a prediction model to predict an achievement index for the candidate based on the audio cues; and displaying the candidate in a list of candidates based on the achievement index, wherein the list of candidates is sorted according to the candidates' achievement index.
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1. A method comprising: identifying, by an audio cue generator executing on a processing device, utterances in audio data of a digital interview, wherein the utterances each comprise a group of one or more words spoken by a candidate in the digital interview; generating, by the audio cue generator, audio cues of the digital interview based on the identified utterances; applying the audio cues to a prediction model to predict an achievement index for the candidate based on the audio cues; and displaying the candidate in a list of candidates based on the achievement index, wherein the list of candidates is sorted according to the candidates' achievement index. 11. The method of claim 1 , wherein the generating the audio cues further comprises: performing Fast Fourier Transform (FFT) variants on a sound spectrum of the audio data for frequency spectral analysis; and generating frequency statistics based on the frequency spectral analysis.
| 0.730245 |
19. 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 initial training data, the initial training data comprising initial training records, each initial training record identifying input data as input and a category as output; generating first intermediate training records by inputting input data of a first subset of the initial training records to a first trained predictive model, the first trained predictive model generated using at least a second subset of the initial training records and a training function, each first intermediate training record having a first score; generating second intermediate training records by inputting input data of the second subset of the initial training records to a second trained predictive model, the second trained predictive model generated using the training function and at least the first subset of the initial training records, each second intermediate training record having a second score; and generating, for the first trained predictive model and the second trained predictive model, a score normalization model using a score normalization training function, the first intermediate training records, and the second intermediate training records.
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19. 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 initial training data, the initial training data comprising initial training records, each initial training record identifying input data as input and a category as output; generating first intermediate training records by inputting input data of a first subset of the initial training records to a first trained predictive model, the first trained predictive model generated using at least a second subset of the initial training records and a training function, each first intermediate training record having a first score; generating second intermediate training records by inputting input data of the second subset of the initial training records to a second trained predictive model, the second trained predictive model generated using the training function and at least the first subset of the initial training records, each second intermediate training record having a second score; and generating, for the first trained predictive model and the second trained predictive model, a score normalization model using a score normalization training function, the first intermediate training records, and the second intermediate training records. 27. The system of claim 19 , wherein each of the intermediate training records comprises a value for each distinct category in the initial training data.
| 0.669259 |
4. The method of claim 1 , wherein the multiple responding devices comprises a first responding device that sends a first text message and a second responding device that sends a second text message, the method further comprising: receiving real-time content of the first text message, and sending the real-time content of the first text message to the second responding device as the content is keyed into the first responding device; and receiving real-time content of the second text message, and sending the real-time content of the second text message to the first responding device as the content is keyed into the second responding device.
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4. The method of claim 1 , wherein the multiple responding devices comprises a first responding device that sends a first text message and a second responding device that sends a second text message, the method further comprising: receiving real-time content of the first text message, and sending the real-time content of the first text message to the second responding device as the content is keyed into the first responding device; and receiving real-time content of the second text message, and sending the real-time content of the second text message to the first responding device as the content is keyed into the second responding device. 5. The method of claim 4 further comprising receiving an indication from the second responding device to prevent the first text message from being sent to the caller device.
| 0.866149 |
11. The computer-readable storage device of claim 10 , further comprising: determining whether the result is to be shared with the at least one second human user; and when determined that the result is to be shared with the at least one second human user, displaying the result associated with performing the agent action to the at least one second human user.
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11. The computer-readable storage device of claim 10 , further comprising: determining whether the result is to be shared with the at least one second human user; and when determined that the result is to be shared with the at least one second human user, displaying the result associated with performing the agent action to the at least one second human user. 15. The computer-readable storage device of claim 11 , wherein determining whether the result is to be shared with the at least one second human user comprises determining whether the result is associated with scheduling an event.
| 0.834106 |
11. A computer program product that includes a non-transitory computer readable medium useable by a processor, the medium having stored thereon a sequence of instructions which, when executed by the processor, causes the processor to translate an interpretation of a keyword query into a grammatically correct plain-language query, wherein the computer program product executes the steps of: acquiring at least one keyword to perform a keyword query search upon; generating a keyword query in order to semantically interpret the acquired keyword, further including the step of building a translation index to determine matching elements, wherein matching elements are derived from information comprising type names, attribute names, and atomic attributes values that are associated with a specific keyword; merging the matching elements in the event that differing keywords comprise a same matching element and type alias; providing a clause template for the customization of a plain-language sentence clause, wherein the plain-language sentence clause is based upon the matching elements that are selected for customization; generating at least one plain-language sentence clause for each of the matching elements, wherein the matching elements relate to matches including a type match, a path patch, a value match and a word match; determining if the plain-language sentence clauses can be merged, wherein the determination is based upon the attributes matched for a given type element; specifying the plain-language sentence clauses that are to be merged, the plain-language sentence clause mergers being based upon the attributes matched for a given matching element; merging the plain-language sentence clauses, wherein the merging occurs for at least one of the path match, the value match and a combination of the path match and the value match, and wherein the merging is not possible for the type match and the word match; generating at least one grammatically valid plain-language sentence interpretation for the keyword query from the generated plain-language sentence clauses, wherein the grammatically valid plain-language sentence is based upon differing matching elements; presenting the at least one grammatically valid plain-language sentence interpretation for the keyword query to a keyword query system user for the user's review.
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11. A computer program product that includes a non-transitory computer readable medium useable by a processor, the medium having stored thereon a sequence of instructions which, when executed by the processor, causes the processor to translate an interpretation of a keyword query into a grammatically correct plain-language query, wherein the computer program product executes the steps of: acquiring at least one keyword to perform a keyword query search upon; generating a keyword query in order to semantically interpret the acquired keyword, further including the step of building a translation index to determine matching elements, wherein matching elements are derived from information comprising type names, attribute names, and atomic attributes values that are associated with a specific keyword; merging the matching elements in the event that differing keywords comprise a same matching element and type alias; providing a clause template for the customization of a plain-language sentence clause, wherein the plain-language sentence clause is based upon the matching elements that are selected for customization; generating at least one plain-language sentence clause for each of the matching elements, wherein the matching elements relate to matches including a type match, a path patch, a value match and a word match; determining if the plain-language sentence clauses can be merged, wherein the determination is based upon the attributes matched for a given type element; specifying the plain-language sentence clauses that are to be merged, the plain-language sentence clause mergers being based upon the attributes matched for a given matching element; merging the plain-language sentence clauses, wherein the merging occurs for at least one of the path match, the value match and a combination of the path match and the value match, and wherein the merging is not possible for the type match and the word match; generating at least one grammatically valid plain-language sentence interpretation for the keyword query from the generated plain-language sentence clauses, wherein the grammatically valid plain-language sentence is based upon differing matching elements; presenting the at least one grammatically valid plain-language sentence interpretation for the keyword query to a keyword query system user for the user's review. 12. The computer program product of claim 11 , further comprising the step of providing a template for the overall structure of the at least one grammatically valid plain-language sentence.
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13. A non-transitory computer recordable medium comprising computer program instructions which, when executed, cause performance of a method comprising: selecting, using a processor, a demeanor for presentation of content of a content provider via the multimodal application, wherein selecting the demeanor comprises considering, by the processor, at least one characteristic of a user of the multimodal application to whom the content is to be provided, the user differing from the content provide, wherein the at least one characteristic of the user comprises whether the user is a repeat user of the multimodal application; and presenting the content to the user using the demeanor; wherein selecting the demeanor comprises selecting a visual demeanor and/or selecting a vocal demeanor; wherein selecting the visual demeanor comprises selecting from the group consisting of background colors, text colors, text fonts, and selection and placement of graphic elements; and wherein selecting the vocal demeanor comprises selecting from the group consisting of speech rate, voice family, pitch, pith range, stress and richness.
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13. A non-transitory computer recordable medium comprising computer program instructions which, when executed, cause performance of a method comprising: selecting, using a processor, a demeanor for presentation of content of a content provider via the multimodal application, wherein selecting the demeanor comprises considering, by the processor, at least one characteristic of a user of the multimodal application to whom the content is to be provided, the user differing from the content provide, wherein the at least one characteristic of the user comprises whether the user is a repeat user of the multimodal application; and presenting the content to the user using the demeanor; wherein selecting the demeanor comprises selecting a visual demeanor and/or selecting a vocal demeanor; wherein selecting the visual demeanor comprises selecting from the group consisting of background colors, text colors, text fonts, and selection and placement of graphic elements; and wherein selecting the vocal demeanor comprises selecting from the group consisting of speech rate, voice family, pitch, pith range, stress and richness. 14. The non-transitory computer recordable medium of claim 13 , wherein the at least one characteristic of the user comprises gender.
| 0.653595 |
1. A computer program product comprising one or more physical computer-readable hardware storage devices having stored thereon computer-executable instructions that, when executed by one or more processors of a computing system, cause the computing system to perform the following: an act of accessing a hierarchical structure of model elements, each element having an ancestral relationship status defined by the position of the each element in the hierarchical structure and parent-child associations of the each element and one or more other elements; for at least one of the model elements, an act of determining that the at least one model element should be changed from a first model element type to a second model element type, determining being at least in part behavior-based and based upon monitoring usage of the model elements; in response to the determination, an act of automatically changing the at least one model element from a first model element type to a second model element type and maintaining the ancestral relationship status of the changed at least one model element within the hierarchical structure of model elements such that the position in the hierarchical structure and parent-child associations of the at least one of the model elements are preserved for the changed at least one model element.
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1. A computer program product comprising one or more physical computer-readable hardware storage devices having stored thereon computer-executable instructions that, when executed by one or more processors of a computing system, cause the computing system to perform the following: an act of accessing a hierarchical structure of model elements, each element having an ancestral relationship status defined by the position of the each element in the hierarchical structure and parent-child associations of the each element and one or more other elements; for at least one of the model elements, an act of determining that the at least one model element should be changed from a first model element type to a second model element type, determining being at least in part behavior-based and based upon monitoring usage of the model elements; in response to the determination, an act of automatically changing the at least one model element from a first model element type to a second model element type and maintaining the ancestral relationship status of the changed at least one model element within the hierarchical structure of model elements such that the position in the hierarchical structure and parent-child associations of the at least one of the model elements are preserved for the changed at least one model element. 2. The computer program product in accordance with claim 1 , wherein the model elements are components of a computer program.
| 0.608028 |
5. A method of administering a knowledge management system using computers connected via a network, the method comprising: assigning a first user responsibility for monitoring use of sensitive information on knowledge management documents on a knowledge management system for a first project, the sensitive information including offensive words or phrases, words or phrases that are considered confidential, words or phrases that are considered proprietary, or words or phrases that are associated with an asset; assigning a second user responsibility for monitoring use of sensitive information on the knowledge management documents on the knowledge management system for a second project, the sensitive information of the second project being different from the sensitive information of the first project; and the knowledge management system notifying the first user but not the second user via the network if the sensitive information for the first project appears on a knowledge management document on the knowledge management document system.
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5. A method of administering a knowledge management system using computers connected via a network, the method comprising: assigning a first user responsibility for monitoring use of sensitive information on knowledge management documents on a knowledge management system for a first project, the sensitive information including offensive words or phrases, words or phrases that are considered confidential, words or phrases that are considered proprietary, or words or phrases that are associated with an asset; assigning a second user responsibility for monitoring use of sensitive information on the knowledge management documents on the knowledge management system for a second project, the sensitive information of the second project being different from the sensitive information of the first project; and the knowledge management system notifying the first user but not the second user via the network if the sensitive information for the first project appears on a knowledge management document on the knowledge management document system. 6. The method of claim 5 further comprising notifying an administrator who is responsible for all knowledge management documents on the knowledge management system if the sensitive information for the first project appears on a knowledge management document on the knowledge management system and notifying the administrator if the sensitive information for the second project appears on a knowledge management document on the knowledge management system.
| 0.5 |
1. A method for business context oriented request processing, the method comprising: establishing a stack of business context instances in a thread, each for a context type; receiving an object request for an object in an object request broker (ORB); passing the request from the ORB to an enterprise Java bean (EJB) container, the EJB container identifying a type of the object request, determining if a business context data service collaborator is registered for the object and calling the business context data service, the business context data service establishing a business context instance in the stack on the thread for the object; and, upon the EJB completing processing of the object request, calling by the EJB container a post-invocation collaborator to remove one of the context instances corresponding to the type from stack in the thread by clearing in the thread a context identifier for the business context instance and restoring the business context instance corresponding to the context identifier.
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1. A method for business context oriented request processing, the method comprising: establishing a stack of business context instances in a thread, each for a context type; receiving an object request for an object in an object request broker (ORB); passing the request from the ORB to an enterprise Java bean (EJB) container, the EJB container identifying a type of the object request, determining if a business context data service collaborator is registered for the object and calling the business context data service, the business context data service establishing a business context instance in the stack on the thread for the object; and, upon the EJB completing processing of the object request, calling by the EJB container a post-invocation collaborator to remove one of the context instances corresponding to the type from stack in the thread by clearing in the thread a context identifier for the business context instance and restoring the business context instance corresponding to the context identifier. 3. The method of claim 1 , further comprising transporting the business context instance of the thread from one application server to another.
| 0.628457 |
8. The method of claim 7 , wherein the pattern structure comprises natural language structure.
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8. The method of claim 7 , wherein the pattern structure comprises natural language structure. 9. The method of claim 8 , wherein natural language structure comprises natural language semantics.
| 0.956761 |
15. A mobile device, comprising: a processor; and a computer readable memory in communication with the processor, the memory storing statements and instructions for execution by the processor to perform a method of converting a Shockwave Flash (SWF) shape definition, including a first plurality of directed edges having a first path style in common, into a first vector graphics path definition corresponding to the first path style, the method including: i) creating a first path style graph representation corresponding to the first path style, based on mapping the first plurality of directed edges of the SWF shape definition having the first path style in common to a first plurality of vertices and undirected edges, and generating and storing connectivity information relating to the first plurality of vertices and undirected edges such that each of the undirected edges is connected to at least two of the first plurality of vertices and such that each of the first plurality of vertices is connected to two of the undirected edges, the first path style graph representation including first path style information and a first graph representation; ii) creating a first vector graphics path by traversing undirected edges of a graph represented by the first graph representation and by removing an undirected edge, after the undirected edge has been traversed, from each edge set to which the traversed edge belongs; and iii) creating the first vector graphics path definition including the first vector graphics path and the first path style information.
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15. A mobile device, comprising: a processor; and a computer readable memory in communication with the processor, the memory storing statements and instructions for execution by the processor to perform a method of converting a Shockwave Flash (SWF) shape definition, including a first plurality of directed edges having a first path style in common, into a first vector graphics path definition corresponding to the first path style, the method including: i) creating a first path style graph representation corresponding to the first path style, based on mapping the first plurality of directed edges of the SWF shape definition having the first path style in common to a first plurality of vertices and undirected edges, and generating and storing connectivity information relating to the first plurality of vertices and undirected edges such that each of the undirected edges is connected to at least two of the first plurality of vertices and such that each of the first plurality of vertices is connected to two of the undirected edges, the first path style graph representation including first path style information and a first graph representation; ii) creating a first vector graphics path by traversing undirected edges of a graph represented by the first graph representation and by removing an undirected edge, after the undirected edge has been traversed, from each edge set to which the traversed edge belongs; and iii) creating the first vector graphics path definition including the first vector graphics path and the first path style information. 23. The mobile device of claim 15 wherein the SWF shape definition includes a second plurality of directed edges having a second path style in common, and further comprising converting the SWF shape definition into a second vector graphics path definition corresponding to the second path style, the method comprising: iv) creating a second path style graph representation corresponding to the second path style, based on mapping the second plurality of directed edges of the SWF shape definition having the second path style in common to a second plurality of vertices and undirected edges, and generating and storing connectivity information relating to the second plurality of vertices and undirected edges, the second path style graph representation including second path style information and a second graph representation; v) creating a second vector graphics path by traversing undirected edges of a graph represented by the second graph representation and by removing an undirected edge, after the undirected edge has been traversed, from each edge set to which the traversed edge belongs; and vi) creating the second vector graphics path definition including the second vector graphics path and the second path style information.
| 0.5 |
1. A computer-implemented method comprising: receiving a query associated with a user of a social networking system; obtaining a result set comprising a plurality of objects from an object store of the social networking system that match the query, the plurality of objects including a first object having a first type and obtained based on the query using a first search algorithm, and a second object having a second type different from the first type and obtained based on the query using a second search algorithm; ordering at least a plurality of the objects of the result set based at least in part on measures of affinities of the user for the objects, an affinity of the user for an object comprising at least one from a group consisting of: a distance on a social graph between the user and the object, and a similarity between the user and the object, the social graph having nodes corresponding to objects and edges corresponding to relationships of the objects; and providing at least a portion of the result set to a client device.
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1. A computer-implemented method comprising: receiving a query associated with a user of a social networking system; obtaining a result set comprising a plurality of objects from an object store of the social networking system that match the query, the plurality of objects including a first object having a first type and obtained based on the query using a first search algorithm, and a second object having a second type different from the first type and obtained based on the query using a second search algorithm; ordering at least a plurality of the objects of the result set based at least in part on measures of affinities of the user for the objects, an affinity of the user for an object comprising at least one from a group consisting of: a distance on a social graph between the user and the object, and a similarity between the user and the object, the social graph having nodes corresponding to objects and edges corresponding to relationships of the objects; and providing at least a portion of the result set to a client device. 4. The computer-implemented method of claim 1 , wherein the result set comprises at least one object selected from the group consisting of an application object, a group object, a media object, an event object, a location object an inbox message, and a comment.
| 0.708839 |
12. The computer program of claim 10 , wherein the program is further configured to cause the at least one processor to draw the set of unlabeled patches x for an image dataset.
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12. The computer program of claim 10 , wherein the program is further configured to cause the at least one processor to draw the set of unlabeled patches x for an image dataset. 13. The computer program of claim 12 , wherein the learning rule is defined by:
∀φ k ∈Φ,Δφ k =ηy k ( x−Φy ) where η is a learning rate and k is a number of indexes over φ k in Φ.
| 0.898429 |
22. The system of claim 14 , wherein the method further comprises: receiving one or more test speech samples; generating a set of test data by extracting one or more acoustic features from every frame of the one or more test speech samples; transforming the set of test data into transformed data using the PLDA model to capture emotion and/or speaking style in the transformed data; and using the transformed data for clustering and/or classification to discover speech with emotion or speaking styles similar to that captured in the transformed data.
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22. The system of claim 14 , wherein the method further comprises: receiving one or more test speech samples; generating a set of test data by extracting one or more acoustic features from every frame of the one or more test speech samples; transforming the set of test data into transformed data using the PLDA model to capture emotion and/or speaking style in the transformed data; and using the transformed data for clustering and/or classification to discover speech with emotion or speaking styles similar to that captured in the transformed data. 27. The system of claim 22 , wherein the method further comprises augmenting the classification and/or clustering with supplemental emotion classification using emotion recognition done in parallel by one or more methods other than analysis of speech samples.
| 0.725475 |
19. A method of fitting graphical objects within a plurality of separate graphical frames in a document, each frame being associated with at least one value associated with a fitting attribute for fining one or more of the graphical objects in the frame, comprising: specifying details concerning the values of the attributes for the frames in a user interface; using an algorithm to automatically determine an optimized at least one value, wherein using the algorithm comprises: determining a plurality of intermediate optimized values, wherein each intermediate optimized value is associated with a particular frame; and selecting the optimized at least one value from said plurality of intermediate values, wherein said selecting is based on the specified details; and applying the optimized at least one value to each frame of the plurality of separate graphical frames to fit one or more of the graphical objects in each of the frames without modifying the size of the plurality of separate graphical frames.
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19. A method of fitting graphical objects within a plurality of separate graphical frames in a document, each frame being associated with at least one value associated with a fitting attribute for fining one or more of the graphical objects in the frame, comprising: specifying details concerning the values of the attributes for the frames in a user interface; using an algorithm to automatically determine an optimized at least one value, wherein using the algorithm comprises: determining a plurality of intermediate optimized values, wherein each intermediate optimized value is associated with a particular frame; and selecting the optimized at least one value from said plurality of intermediate values, wherein said selecting is based on the specified details; and applying the optimized at least one value to each frame of the plurality of separate graphical frames to fit one or more of the graphical objects in each of the frames without modifying the size of the plurality of separate graphical frames. 28. The method of claim 19 , wherein applying the optimized at least one value to its associated frame results in the graphical objects substantially filling each frame without overset.
| 0.61454 |
8. The method of claim 1 , wherein the event is added to the user profile by: accessing via the control circuitry, a social network associated with the user; analyzing by the control circuitry, data added to the social network by a friend of the user; detecting by the control circuitry, in the analyzed data, data associated with the user and that identifies the event.
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8. The method of claim 1 , wherein the event is added to the user profile by: accessing via the control circuitry, a social network associated with the user; analyzing by the control circuitry, data added to the social network by a friend of the user; detecting by the control circuitry, in the analyzed data, data associated with the user and that identifies the event. 9. The method of claim 8 , wherein the event comprises one of the group of: a natural disaster, a news article, a location, and a weather pattern.
| 0.939731 |
10. The computer-readable storage medium of claim 9 , wherein merging queries in each group of queries further comprises: generating a singleton cluster for each query in each group of queries; determining a cost associated with merging any two singleton clusters in each query into a single combined cluster corresponding to a single second type of query; and merging only singleton clusters where the cost associated with merging indicates that merging is cost-effective for optimizing multi-query processing.
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10. The computer-readable storage medium of claim 9 , wherein merging queries in each group of queries further comprises: generating a singleton cluster for each query in each group of queries; determining a cost associated with merging any two singleton clusters in each query into a single combined cluster corresponding to a single second type of query; and merging only singleton clusters where the cost associated with merging indicates that merging is cost-effective for optimizing multi-query processing. 11. The computer-readable storage medium of claim 10 , wherein determining the cost associated with merging further comprises determining an optimized query cost resulting from an optimized query generated by merging any two singleton clusters.
| 0.845272 |
1. A method for categorizing programming using program listings information, the method comprising: identifying, with a processor, a program listing associated with a plurality of simple categories; comparing, with the processor, the plurality of simple categories with a list of supported combination categories, wherein each combination category in the list comprises two or more simple categories; and assigning, with the processor, at least one combination category in the list to the identified program listing based on the comparison.
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1. A method for categorizing programming using program listings information, the method comprising: identifying, with a processor, a program listing associated with a plurality of simple categories; comparing, with the processor, the plurality of simple categories with a list of supported combination categories, wherein each combination category in the list comprises two or more simple categories; and assigning, with the processor, at least one combination category in the list to the identified program listing based on the comparison. 4. The method of claim 1 , wherein the processor is located in a remote server.
| 0.65924 |
20. The non transitory computer usable medium of claim 19 , wherein facilitating the discussion comprises: transmitting a first message to the support agent about the first page of the online financial document using the chat window; and receiving a second message from the support agent about the first page of the online financial document using the chat window.
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20. The non transitory computer usable medium of claim 19 , wherein facilitating the discussion comprises: transmitting a first message to the support agent about the first page of the online financial document using the chat window; and receiving a second message from the support agent about the first page of the online financial document using the chat window. 22. The non transitory computer usable medium of claim 20 , wherein the support agent is trained in a subject matter related to the first page of the online financial document.
| 0.962572 |
1. A computer-implemented method of text passage difficulty estimation, comprising: generating, using a computer processing system, an informational text scoring model, wherein generating the informational text scoring model includes: identifying a plurality of texts from a corpus of texts that are informational texts; determining one or more metrics for the informational texts; and configuring the informational text scoring model using the one or more informational text metrics for application in scoring a difficulty of a text passage; and generating, using a computer processing system, a literary text scoring model, wherein generating the literary text scoring model includes: identifying a plurality of texts from the corpus of texts that are literary texts; determining one or more metrics for the literary texts, wherein the literary text metrics include one or more metrics that are either not included in the informational text metrics, or are included but are weighted differently; and configuring the literary text scoring model using the one or more literary text metrics for application in scoring the difficulty of the text passage, wherein the informational text scoring model and the literary text scoring model are configured to provide data about the difficulty of the text passage in a hierarchically structured format.
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1. A computer-implemented method of text passage difficulty estimation, comprising: generating, using a computer processing system, an informational text scoring model, wherein generating the informational text scoring model includes: identifying a plurality of texts from a corpus of texts that are informational texts; determining one or more metrics for the informational texts; and configuring the informational text scoring model using the one or more informational text metrics for application in scoring a difficulty of a text passage; and generating, using a computer processing system, a literary text scoring model, wherein generating the literary text scoring model includes: identifying a plurality of texts from the corpus of texts that are literary texts; determining one or more metrics for the literary texts, wherein the literary text metrics include one or more metrics that are either not included in the informational text metrics, or are included but are weighted differently; and configuring the literary text scoring model using the one or more literary text metrics for application in scoring the difficulty of the text passage, wherein the informational text scoring model and the literary text scoring model are configured to provide data about the difficulty of the text passage in a hierarchically structured format. 2. The method of claim 1 , wherein, for an input text passage: an automatic determination is made as to whether the text passage is an informational passage or a literary passage; a difficulty estimate is computed for the text passage using the informational text scoring model when the text passage is an informational passage; the difficulty estimate is computed for the text passage using the literary text scoring model when the text passage is a literary passage; and the difficulty estimate is outputted.
| 0.5 |
3. The computer server system of claim 1 , wherein the one or more processors are further adapted, for each response of the plurality of responses to the return queries of a selected digital filter of the plurality of digital filters, to determine an unmodified distance between responses of a respondent and a co-respondent of a selected combination of respondent and co-respondent digital filters; and to combine a plurality of unmodified distance determinations for the plurality of responses to the return queries to form an unmodified alignment score.
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3. The computer server system of claim 1 , wherein the one or more processors are further adapted, for each response of the plurality of responses to the return queries of a selected digital filter of the plurality of digital filters, to determine an unmodified distance between responses of a respondent and a co-respondent of a selected combination of respondent and co-respondent digital filters; and to combine a plurality of unmodified distance determinations for the plurality of responses to the return queries to form an unmodified alignment score. 4. The computer server system of claim 3 , wherein the one or more processors are further adapted, for each response of the plurality of responses to the return queries of the selected digital filter, to determine a normalized distance between responses of the respondent and the co-respondent of the selected combination of respondent and co-respondent digital filters; and to combine a plurality of normalized distance determinations for the plurality of responses to the return queries to form a normalized alignment score.
| 0.829264 |
15. The apparatus of claim 13 in which said spreading comprises modulating the message with a random carrier signal.
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15. The apparatus of claim 13 in which said spreading comprises modulating the message with a random carrier signal. 16. The apparatus of claim 15 in which the random carrier signal comprises a pseudorandom carrier signal.
| 0.907072 |
255. A system for using a computer to improve a precision ratio when searching a resume database, comprising: a memory device resident in the computer; and a processor disposed in communication with the memory device, the processor configured to: receive a resume; parse the resume to: locate at least one skill or experience-related phrase in the resume; determine an experience range for each said at least one skill or experience-related phrase by examining a use of each said at least one skill or experience-related phrase in the resume; and compute a term of experience for each said at least one skill or experience-related phrase based on the experience range, wherein the term of experience for each said at least one skill or experience-related phrase is a summation of the term of experience for each occurrence of the phrase associated with a different experience range; store the resume in the resume database; associate at least one of said at least one skill or experience-related phrase located in the resume with at least one implied skill or experience-related phrase, wherein a term of experience for each said at least one implied skill or experience-related phrase is the term of experience computed for said at least one of said at least one skill or experience-related phrase, and wherein said at least one skill or experience-related phrase and said at least one implied skill or experience-related phrase are searchable phrases in the resume; create a parsed resume based on the resume, the parsed resume including each searchable phrase in the resume, the term of experience for each searchable phrase, and a relationship between the term of experience and each searchable phrase; store the parsed resume in the resume database; send a database query to the resume database, the database query including a job description that includes at least one job requirement, each said at least one job requirement including a required skill or experience-related phrase, and a required term of experience; and receive a result set in response to the database query, wherein the result set includes the resume when the parsed resume satisfies the job description.
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255. A system for using a computer to improve a precision ratio when searching a resume database, comprising: a memory device resident in the computer; and a processor disposed in communication with the memory device, the processor configured to: receive a resume; parse the resume to: locate at least one skill or experience-related phrase in the resume; determine an experience range for each said at least one skill or experience-related phrase by examining a use of each said at least one skill or experience-related phrase in the resume; and compute a term of experience for each said at least one skill or experience-related phrase based on the experience range, wherein the term of experience for each said at least one skill or experience-related phrase is a summation of the term of experience for each occurrence of the phrase associated with a different experience range; store the resume in the resume database; associate at least one of said at least one skill or experience-related phrase located in the resume with at least one implied skill or experience-related phrase, wherein a term of experience for each said at least one implied skill or experience-related phrase is the term of experience computed for said at least one of said at least one skill or experience-related phrase, and wherein said at least one skill or experience-related phrase and said at least one implied skill or experience-related phrase are searchable phrases in the resume; create a parsed resume based on the resume, the parsed resume including each searchable phrase in the resume, the term of experience for each searchable phrase, and a relationship between the term of experience and each searchable phrase; store the parsed resume in the resume database; send a database query to the resume database, the database query including a job description that includes at least one job requirement, each said at least one job requirement including a required skill or experience-related phrase, and a required term of experience; and receive a result set in response to the database query, wherein the result set includes the resume when the parsed resume satisfies the job description. 316. The system of claim 255 , wherein the job description further includes a required salary range comprising a minimum required salary and a maximum required salary, and wherein the processor is further configured to: store the job description; and send a portion of the result set, wherein the result set includes at least one matching resume from the resume database, each said at least one matching resume satisfying the job description.
| 0.537945 |
1. A database computer system comprising a context-based graph-relational intersect derived (CB-GRID) database, wherein the CB-GRID database associates a real entity graph node to a synthetic entity graph node, and wherein the CB-GRID database in the database computer system comprises: one or more processors, wherein said one or more processors implement the CB-GRID database; a real entity graph node, wherein the real entity graph node identifies a real entity, and wherein the real entity graph node comprises a pointer to a primary key in a first tuple that non-contextually describes the real entity; a primary relational database, wherein the primary relational database comprises the first tuple that non-contextually describes the real entity, and wherein the first tuple contains the primary key; a context relational database, wherein the context relational database comprises a second tuple that contains a foreign key that matches the primary key in the primary relational database, and wherein the second tuple dynamically describes a context of data in the first tuple; a contextual entity relational database, wherein the contextual entity relational database comprises a third tuple that contains data from the first tuple and the second tuple, and wherein the third tuple comprises a contextual tuple key; a synthetic entity graph node, wherein the synthetic entity graph node is linked to the contextual entity relational database by the contextual tuple key, wherein the synthetic entity graph node describes a synthetic entity that is described by data in the contextual entity relational database, and wherein the contextual entity relational database links the real entity graph node to the synthetic entity graph node, wherein the real entity is a physical machine, wherein the synthetic entity graph node describes a software-modeled machine that is operating outside of nominal parameters, and wherein the CB-GRID database in the database computer system further comprises: output data from a sensor on the physical machine stored in the first tuple; model type data describing a model type of the physical machine stored in the second tuple; and software-modeled machine descriptor data stored in the third tuple, wherein the contextual entity relational database links the physical machine to the software-modeled machine that is operating outside of the nominal parameters.
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1. A database computer system comprising a context-based graph-relational intersect derived (CB-GRID) database, wherein the CB-GRID database associates a real entity graph node to a synthetic entity graph node, and wherein the CB-GRID database in the database computer system comprises: one or more processors, wherein said one or more processors implement the CB-GRID database; a real entity graph node, wherein the real entity graph node identifies a real entity, and wherein the real entity graph node comprises a pointer to a primary key in a first tuple that non-contextually describes the real entity; a primary relational database, wherein the primary relational database comprises the first tuple that non-contextually describes the real entity, and wherein the first tuple contains the primary key; a context relational database, wherein the context relational database comprises a second tuple that contains a foreign key that matches the primary key in the primary relational database, and wherein the second tuple dynamically describes a context of data in the first tuple; a contextual entity relational database, wherein the contextual entity relational database comprises a third tuple that contains data from the first tuple and the second tuple, and wherein the third tuple comprises a contextual tuple key; a synthetic entity graph node, wherein the synthetic entity graph node is linked to the contextual entity relational database by the contextual tuple key, wherein the synthetic entity graph node describes a synthetic entity that is described by data in the contextual entity relational database, and wherein the contextual entity relational database links the real entity graph node to the synthetic entity graph node, wherein the real entity is a physical machine, wherein the synthetic entity graph node describes a software-modeled machine that is operating outside of nominal parameters, and wherein the CB-GRID database in the database computer system further comprises: output data from a sensor on the physical machine stored in the first tuple; model type data describing a model type of the physical machine stored in the second tuple; and software-modeled machine descriptor data stored in the third tuple, wherein the contextual entity relational database links the physical machine to the software-modeled machine that is operating outside of the nominal parameters. 3. The database computer system of claim 1 , wherein the real entity is a physical information technology (IT) system, wherein the synthetic entity graph node describes a software-modeled IT system that is operating outside of nominal parameters, and wherein the CB-GRID database in the database computer system further comprises: output data from a sensor in the physical IT system stored in the first tuple; environmental data describing an external physical environment of the physical IT system stored in the second tuple; and software-modeled IT system descriptor data stored in the third tuple, wherein the contextual entity relational database links the physical IT system to the software-modeled IT system that is operating outside of the nominal parameters.
| 0.733495 |
1. A method comprising: determining a service executing on an originating device of a network of devices in which a plurality of services are deployed and configured to process information external to the network of devices and collected by at least one sensor associated with the network of devices, the service including executable code; determining a cause for re-deployment of the service executing on the originating device; mapping the service to a selected device from among the network of devices that includes the originating device and the selected device; and re-deploying the service on the selected device including transferring the executable code to the selected device for execution thereon and for continued processing of the external information therewith, wherein determining a cause for re-deployment of the service comprises: determining that the selected device is available for re-deployment of the service; and determining device metadata associated with the originating device and/or the selected device that indicates that the selected device is better able to execute the service, including determining the device metadata by representing device characteristics of the originating device and the selected device in a common format; value-matching the device characteristics of each of the originating device and the selected device to service characteristics of the service as represented in associated service metadata, and selecting the selected device as having a closer value-match of device characteristics to the service characteristics than the originating device.
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1. A method comprising: determining a service executing on an originating device of a network of devices in which a plurality of services are deployed and configured to process information external to the network of devices and collected by at least one sensor associated with the network of devices, the service including executable code; determining a cause for re-deployment of the service executing on the originating device; mapping the service to a selected device from among the network of devices that includes the originating device and the selected device; and re-deploying the service on the selected device including transferring the executable code to the selected device for execution thereon and for continued processing of the external information therewith, wherein determining a cause for re-deployment of the service comprises: determining that the selected device is available for re-deployment of the service; and determining device metadata associated with the originating device and/or the selected device that indicates that the selected device is better able to execute the service, including determining the device metadata by representing device characteristics of the originating device and the selected device in a common format; value-matching the device characteristics of each of the originating device and the selected device to service characteristics of the service as represented in associated service metadata, and selecting the selected device as having a closer value-match of device characteristics to the service characteristics than the originating device. 5. The method of claim 1 wherein mapping the service to a selected device comprises: determining service metadata associated with the service, the service metadata including a mobility description associated with the service and describing a nature and/or extent of allowed re-deployment of the service.
| 0.519371 |
14. One or more computer-readable storage media storing computer-executable instructions that, when executed on one or more processors, cause the one or more processors to perform operations comprising: receiving an input query transmitted over a network; determining an input query pattern associated with the input query; accessing a library comprising one or more query patterns associated with one or more predefined search task categories by one or more transition probabilities, each of the one or more predefined search task categories associated with a task to be accomplished; classifying the input query pattern into one or more search tasks according to the one or more predefined search task categories, the classifying based at least in part on a lookup of the input query pattern in the library and identifying one or more tasks to be accomplished associated with the one or more predefined search task categories associated with a query pattern of the one or more query patterns similar to the input query pattern; and determining a search result based at least in part on the classified one or more search tasks, the search result based at least in part on the identified one or more tasks to be accomplished.
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14. One or more computer-readable storage media storing computer-executable instructions that, when executed on one or more processors, cause the one or more processors to perform operations comprising: receiving an input query transmitted over a network; determining an input query pattern associated with the input query; accessing a library comprising one or more query patterns associated with one or more predefined search task categories by one or more transition probabilities, each of the one or more predefined search task categories associated with a task to be accomplished; classifying the input query pattern into one or more search tasks according to the one or more predefined search task categories, the classifying based at least in part on a lookup of the input query pattern in the library and identifying one or more tasks to be accomplished associated with the one or more predefined search task categories associated with a query pattern of the one or more query patterns similar to the input query pattern; and determining a search result based at least in part on the classified one or more search tasks, the search result based at least in part on the identified one or more tasks to be accomplished. 18. The one or more computer-readable storage media of claim 14 , wherein the input query is a search query transmitted over a network.
| 0.627445 |
2. The computer-readable storage medium of claim 1 , wherein the identifying of the selected platform comprises receiving a user input identifying the selected platform.
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2. The computer-readable storage medium of claim 1 , wherein the identifying of the selected platform comprises receiving a user input identifying the selected platform. 5. The computer-readable storage medium of claim 2 , wherein the calculation rule includes an engine including a plurality of scripted rules configured to perform a transaction-based function.
| 0.951576 |
2. The language learning system according to claim 1 , wherein the feature extraction module performs a phonetic segmentation operation on a plurality of training sentences to obtain a plurality of pronunciation units of the training sentences, and the feature extraction module obtains the training data from the pronunciation units of the training sentences, wherein the feature extraction module performs the phonetic segmentation operation on the learning sentence to obtain one or more pronunciation units of the learning sentence.
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2. The language learning system according to claim 1 , wherein the feature extraction module performs a phonetic segmentation operation on a plurality of training sentences to obtain a plurality of pronunciation units of the training sentences, and the feature extraction module obtains the training data from the pronunciation units of the training sentences, wherein the feature extraction module performs the phonetic segmentation operation on the learning sentence to obtain one or more pronunciation units of the learning sentence. 4. The language learning system according to claim 2 , wherein the feature extraction module extracts an energy contour of the pronunciation, divides the energy contour of the pronunciation into a plurality of sub energy contours of the pronunciation, calculates a mean value of each of the sub energy contours of the pronunciation, calculates a slope of each of the sub energy contours of the pronunciation, calculates a plurality of difference values between the sub energy contours of the pronunciation, and serves at least one of the mean values, the slopes, and the difference values of the sub energy contours of the pronunciation as the pronunciation feature of the pronunciation, wherein the feature extraction module extracts an energy contour of each of the training data, divides the energy contour of the training data into a plurality of sub energy contours, calculates a mean value of each of the sub energy contours of the training data, calculates a slope of each of the sub energy contours of the training data, calculates a plurality of difference values between the sub energy contours of the training data, and serves at least one of the mean values, the slopes, and the difference values of the sub energy contours of the training data as the pronunciation feature of the training data.
| 0.656816 |
9. A system for temporal content selection via a computer network, comprising: an interface executing on one or more processors configured to receive, responsive to a search query input into a search engine, a request for a content item to be displayed alongside search results, the request associated with a first keyword corresponding to the search query; a keyword lookup module executing on the one or more processors configured to parse a data structure storing historically linked keywords to select a second keyword linked to the first keyword via a historical link, the historical link generated based on a frequency of co-occurrence of the first keyword and the second keyword in historical content; a content selector executing on the one or more processors configured to select a first content item for display alongside the first search results based on the second keyword; the interface module configured to receive a second request for a content item to be displayed alongside second search results, the second request associated with the first keyword; the keyword lookup module further configured to parse a second data structure storing temporally linked keywords to select a third keyword temporally linked to the first keyword in the second data structure based on a frequency of co-occurrence of the first keyword and the third keyword in online documents published within a temporal threshold satisfying a frequency of co-occurrence threshold, wherein the temporal link between the first keyword and the third keyword is based on comparing the frequency of co-occurrence of the first keyword and the third keyword in the online documents with a historical frequency of co-occurrence of the first keyword and the third keyword; the content selector configured to identify a plurality of content items matching the third keyword, determine that a content provider of a second content item of the plurality of content items provided an indication to use temporal links for content selection, and select, based on the indication, the second content item as a candidate for display alongside the second search results.
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9. A system for temporal content selection via a computer network, comprising: an interface executing on one or more processors configured to receive, responsive to a search query input into a search engine, a request for a content item to be displayed alongside search results, the request associated with a first keyword corresponding to the search query; a keyword lookup module executing on the one or more processors configured to parse a data structure storing historically linked keywords to select a second keyword linked to the first keyword via a historical link, the historical link generated based on a frequency of co-occurrence of the first keyword and the second keyword in historical content; a content selector executing on the one or more processors configured to select a first content item for display alongside the first search results based on the second keyword; the interface module configured to receive a second request for a content item to be displayed alongside second search results, the second request associated with the first keyword; the keyword lookup module further configured to parse a second data structure storing temporally linked keywords to select a third keyword temporally linked to the first keyword in the second data structure based on a frequency of co-occurrence of the first keyword and the third keyword in online documents published within a temporal threshold satisfying a frequency of co-occurrence threshold, wherein the temporal link between the first keyword and the third keyword is based on comparing the frequency of co-occurrence of the first keyword and the third keyword in the online documents with a historical frequency of co-occurrence of the first keyword and the third keyword; the content selector configured to identify a plurality of content items matching the third keyword, determine that a content provider of a second content item of the plurality of content items provided an indication to use temporal links for content selection, and select, based on the indication, the second content item as a candidate for display alongside the second search results. 13. The system of claim 9 , wherein: the keyword lookup module is further configured to identify, via the second data structure, a geographic scope attribute of the temporal link; and the content selector is further configured to determine that a geographic location associated with the second request corresponds to the geographic scope attribute, and select at least one content item of the plurality of content items corresponding to the geographic scope attribute.
| 0.502804 |
15. A system, comprising: a memory; a model generator executed in the memory to perform operations, the operations comprising: providing a program coded in a first programming language having data structures, wherein at least one of the data structures includes a reference to reusable code; generating a model file identifying the reusable code, elements and attributes in a second programming language for the reference to the reusable code in the program; processing the data structure coded in the first programming language to generate a data structure schema in a second programming language describing elements and attributes of the data structure coded in the first programming language; and generating a reference in the data structure schema to the reusable code.
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15. A system, comprising: a memory; a model generator executed in the memory to perform operations, the operations comprising: providing a program coded in a first programming language having data structures, wherein at least one of the data structures includes a reference to reusable code; generating a model file identifying the reusable code, elements and attributes in a second programming language for the reference to the reusable code in the program; processing the data structure coded in the first programming language to generate a data structure schema in a second programming language describing elements and attributes of the data structure coded in the first programming language; and generating a reference in the data structure schema to the reusable code. 17. The system of claim 15 , wherein the operations further comprise: generating multiple data structure schemas in the second programming language for multiple data structures coded in the first programming language, wherein the same generated reference to the reusable code is included in the multiple schemas.
| 0.712451 |
17. The non-transitory computer-readable medium of claim 16 , wherein the method further comprises receiving a portion of the first program that is currently being presented based on a comparison of the first audio fingerprint to a plurality of audio fingerprints associated with the first program, wherein each of the plurality of audio fingerprints associated with the first program correspond to a particular portion of the first program.
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17. The non-transitory computer-readable medium of claim 16 , wherein the method further comprises receiving a portion of the first program that is currently being presented based on a comparison of the first audio fingerprint to a plurality of audio fingerprints associated with the first program, wherein each of the plurality of audio fingerprints associated with the first program correspond to a particular portion of the first program. 18. The non-transitory computer-readable medium of claim 17 , wherein determining one or more of the plurality of keywords that are contextually relevant to the query comprises determining that the one or more of the plurality of keywords are associated with the portion of the first program that is currently being presented.
| 0.920168 |
8. The computer-implemented method of claim 1 , wherein the first set of performance data includes, for each database query language statement in the set of targeted database query language statements, a total execution time.
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8. The computer-implemented method of claim 1 , wherein the first set of performance data includes, for each database query language statement in the set of targeted database query language statements, a total execution time. 9. The computer-implemented method of claim 8 , wherein the first set of performance data further includes, for each database query language statement in the set of targeted database query language statements, a CPU time and an I/O time.
| 0.942743 |
21. A speech synthesis system according to claim 20 , wherein the compound speech unit database includes linguistic feature vectors from compound speech units derived from synthesized speech validated by an algorithm of perceptual measures.
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21. A speech synthesis system according to claim 20 , wherein the compound speech unit database includes linguistic feature vectors from compound speech units derived from synthesized speech validated by an algorithm of perceptual measures. 22. A speech synthesis system according to claim 21 , wherein the validation takes into account as side products from the speech segment selector at least one cost selected from the group of a normalized path cost, a peak cost, and a cost distribution along a best path.
| 0.935445 |
17. An apparatus comprising a touch screen and a controller, the controller being configured to: display on the touch screen a vowel that is selected based on a length of a dragging gesture, such that a count of strokes in the vowel is proportional to the length of the dragging gesture, wherein: (a) when the length of the dragging gesture is equal to or less than a preset first length, one of “ ” or “ ” is displayed; (b) when the length of the dragging gesture is larger than the preset first length and equal to or less than a second length, one of “ ”, “ ”, “ ”, and “ ” is displayed; and (c) when the length of the dragging gesture is larger than the second length, one of “ ” or “ ” is displayed.
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17. An apparatus comprising a touch screen and a controller, the controller being configured to: display on the touch screen a vowel that is selected based on a length of a dragging gesture, such that a count of strokes in the vowel is proportional to the length of the dragging gesture, wherein: (a) when the length of the dragging gesture is equal to or less than a preset first length, one of “ ” or “ ” is displayed; (b) when the length of the dragging gesture is larger than the preset first length and equal to or less than a second length, one of “ ”, “ ”, “ ”, and “ ” is displayed; and (c) when the length of the dragging gesture is larger than the second length, one of “ ” or “ ” is displayed. 18. The apparatus of claim 17 , wherein: when the length of the dragging is equal to or less than the preset first length and the dragging gesture is performed in a horizontal direction “ ” is displayed, and when the length of the dragging is equal to or less than the preset first length and the dragging gesture is performed in a vertical direction, “ ” is displayed.
| 0.545113 |
10. A source device comprising: a memory; one or more processors; and at least one module executable by the one or more processors to: capture a plurality of sets of graphical command tokens respectively renderable into a plurality of frames of video data; and responsive to determining that a length of a current set of graphical command tokens of the plurality of sets of graphical command tokens is different than a length of a previous set of the plurality of sets of graphical command tokens: determine a token prediction map that indicates, for each graphical command token of the current set of graphical command tokens, whether a similar graphical command token can be located in the previous set of graphical command tokens; and responsive to determining, based on the token prediction map, that the current set of graphical command tokens is sufficiently similar to the previous set of graphical command tokens, output, to a sink device, a compressed version of the current set of graphical command tokens.
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10. A source device comprising: a memory; one or more processors; and at least one module executable by the one or more processors to: capture a plurality of sets of graphical command tokens respectively renderable into a plurality of frames of video data; and responsive to determining that a length of a current set of graphical command tokens of the plurality of sets of graphical command tokens is different than a length of a previous set of the plurality of sets of graphical command tokens: determine a token prediction map that indicates, for each graphical command token of the current set of graphical command tokens, whether a similar graphical command token can be located in the previous set of graphical command tokens; and responsive to determining, based on the token prediction map, that the current set of graphical command tokens is sufficiently similar to the previous set of graphical command tokens, output, to a sink device, a compressed version of the current set of graphical command tokens. 11. The source device of claim 10 , wherein, responsive to determining, based on the token prediction map, that the current set of graphical command tokens is not sufficiently similar to the previous set of graphical command tokens, the at least one module is executable by the one or more processors to output, to the sink device, an uncompressed version of the current set of graphical command tokens.
| 0.824304 |
16. The method of claim 15 , further comprising: determining that a second document of the documents is similar to the first document; determining a second data measure for the second document, the second data measure of the second document indicative of an amount of data usage required to load the second document; and ranking the second document relative to other of the documents based at least in part on the second data measure.
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16. The method of claim 15 , further comprising: determining that a second document of the documents is similar to the first document; determining a second data measure for the second document, the second data measure of the second document indicative of an amount of data usage required to load the second document; and ranking the second document relative to other of the documents based at least in part on the second data measure. 17. The method of claim 16 , wherein ranking the first document relative to the other of the documents includes: ranking the first document relative to the second document based on comparison of the first data measure to the second data measure.
| 0.861572 |
1. A computer implemented method of analysis of competing hypotheses in estimative intelligence, said method comprising the steps of: a. deciding on a plurality of possible hypotheses to be considered; b. identifying significant items of evidence for and against each of said plurality of hypotheses; c. configuring a processor to construct and store onto a memory a model for analyzing the hypotheses by: i. producing a set of exhaustive and exclusive hypotheses, wherein only one hypothesis may be true; ii. assessing and assigning base rates for each hypothesis; iii. determining from said significant items of evidence identified in relation to respective hypotheses a set of items of evidence that are relevant to, have a causal influence on or would disconfirm more than one hypothesis; iv. assessing and assigning base rates for each item of evidence; v. deciding for each item of evidence whether the item should be treated as being a causal influence or diagnostic indicator with respect to the set of the hypotheses; vi. if the item of evidence is to be treated as a causal influence, making a judgment as to the likelihood of each hypothesis: A. if the evidence were true, and B. if the evidence were false; vii. if the item of evidence is to be treated as a diagnostic indicator, making a judgment as to the likelihood of the evidence being true: A. if the hypothesis were true; d. assessing the belief for each item of evidence being true; e. deciding a set of interim beliefs in each hypothesis for each individual item of evidence by: i. employing a conditional inference operator for evidence that is to be treated as a causal influence; and ii. employing a reverse conditional inference operator for evidence that is to be treated as a diagnostic indicator; f. deciding the overall belief in each hypothesis by employing a consensus operator on the respective set of interim beliefs; and g. outputting a set of beliefs representing the certainty and likelihood of each hypothesis.
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1. A computer implemented method of analysis of competing hypotheses in estimative intelligence, said method comprising the steps of: a. deciding on a plurality of possible hypotheses to be considered; b. identifying significant items of evidence for and against each of said plurality of hypotheses; c. configuring a processor to construct and store onto a memory a model for analyzing the hypotheses by: i. producing a set of exhaustive and exclusive hypotheses, wherein only one hypothesis may be true; ii. assessing and assigning base rates for each hypothesis; iii. determining from said significant items of evidence identified in relation to respective hypotheses a set of items of evidence that are relevant to, have a causal influence on or would disconfirm more than one hypothesis; iv. assessing and assigning base rates for each item of evidence; v. deciding for each item of evidence whether the item should be treated as being a causal influence or diagnostic indicator with respect to the set of the hypotheses; vi. if the item of evidence is to be treated as a causal influence, making a judgment as to the likelihood of each hypothesis: A. if the evidence were true, and B. if the evidence were false; vii. if the item of evidence is to be treated as a diagnostic indicator, making a judgment as to the likelihood of the evidence being true: A. if the hypothesis were true; d. assessing the belief for each item of evidence being true; e. deciding a set of interim beliefs in each hypothesis for each individual item of evidence by: i. employing a conditional inference operator for evidence that is to be treated as a causal influence; and ii. employing a reverse conditional inference operator for evidence that is to be treated as a diagnostic indicator; f. deciding the overall belief in each hypothesis by employing a consensus operator on the respective set of interim beliefs; and g. outputting a set of beliefs representing the certainty and likelihood of each hypothesis. 3. The analysis method of claim 1 wherein the steps of assigning base rates to hypotheses and to the items of evidence involves assigning prior probabilities to each hypothesis and to each item of evidence, respectively.
| 0.526534 |
1. A method performed by a data processing apparatus, the method comprising: accessing a word level pronunciation lexicon and a word level training text corpus for a natural language; segmenting, using a word decomposition system, the word level training text corpus into sub-lexical units; training an n-gram language model over the sub-lexical units to produce a sub-lexical language model; constructing, using the word decomposition system, a word to sub-lexical unit mapping transducer; constructing a word level language model by: obtaining a result of composing the mapping transducer with the sub-lexical language model, and performing a projection on the result of the composition of the mapping transducer and the sub-lexical language model; constructing a speech decoding network at least by composing a context dependency model with the word level pronunciation lexicon and with the word level language model; receiving an audio stream from a user; and recognizing the audio stream, using the speech decoding network.
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1. A method performed by a data processing apparatus, the method comprising: accessing a word level pronunciation lexicon and a word level training text corpus for a natural language; segmenting, using a word decomposition system, the word level training text corpus into sub-lexical units; training an n-gram language model over the sub-lexical units to produce a sub-lexical language model; constructing, using the word decomposition system, a word to sub-lexical unit mapping transducer; constructing a word level language model by: obtaining a result of composing the mapping transducer with the sub-lexical language model, and performing a projection on the result of the composition of the mapping transducer and the sub-lexical language model; constructing a speech decoding network at least by composing a context dependency model with the word level pronunciation lexicon and with the word level language model; receiving an audio stream from a user; and recognizing the audio stream, using the speech decoding network. 5. The method of claim 1 , wherein the n-gram language model is represented as a deterministic weighted finite-state automaton.
| 0.853211 |
17. The method of claim 10 , wherein said tag identifies at least one of an enumerated constant value.
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17. The method of claim 10 , wherein said tag identifies at least one of an enumerated constant value. 18. The method of claim 17 , wherein said enumerated constant value being one of a True, False or a user-defined value.
| 0.961612 |
5. A method of operating a speech recognition system, said method comprising the steps of: identifying a speaker by text-independent comparison of an input speech signal with a stored representation of speech signals corresponding to one of a plurality of speakers, said input speech signal including a plurality of words, providing a speech processing model to said speech recognition system in accordance with results of said identifying step, and recognizing said plurality of words within said input speech signal with said speech processing model, said stored representation of speech signals and said speech processing model being loaded into said system only once for recognition of said speaker and said plurality of words in said input speech signal so that said system performs continues speech recognition for said plurality of words, determining whether to perform speech recognition in one of a speaker-independent mode or a speaker-dependent mode; and selecting said speech processing model to be a speaker-dependent model or a speaker-independent model based on said determining step.
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5. A method of operating a speech recognition system, said method comprising the steps of: identifying a speaker by text-independent comparison of an input speech signal with a stored representation of speech signals corresponding to one of a plurality of speakers, said input speech signal including a plurality of words, providing a speech processing model to said speech recognition system in accordance with results of said identifying step, and recognizing said plurality of words within said input speech signal with said speech processing model, said stored representation of speech signals and said speech processing model being loaded into said system only once for recognition of said speaker and said plurality of words in said input speech signal so that said system performs continues speech recognition for said plurality of words, determining whether to perform speech recognition in one of a speaker-independent mode or a speaker-dependent mode; and selecting said speech processing model to be a speaker-dependent model or a speaker-independent model based on said determining step. 16. A method as recited in claim 5, including the further step of providing results of said identifying step to said speaker.
| 0.68557 |
9. A non-transitory computer readable medium having stored thereon instructions that when executed by a processor will cause the processor to perform the method of: obtaining an image of a user interface; identifying a screen control in the user interface; applying optical character recognition to read a text that is displayed on the screen control in the image; comparing the displayed text to a character string that is designated for the screen control; and if part of the character string is not included in the displayed text, identifying the displayed text as truncated text.
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9. A non-transitory computer readable medium having stored thereon instructions that when executed by a processor will cause the processor to perform the method of: obtaining an image of a user interface; identifying a screen control in the user interface; applying optical character recognition to read a text that is displayed on the screen control in the image; comparing the displayed text to a character string that is designated for the screen control; and if part of the character string is not included in the displayed text, identifying the displayed text as truncated text. 16. The non-transitory computer readable medium of claim 9 , wherein the method further comprises generating a notification when the displayed text is identified as truncated text.
| 0.555556 |
1. A method for tagging a media asset, the method comprising: receiving verbal input from a user while the user is accessing the media asset; receiving a request to adjust playback of the media asset; responsive to receiving the verbal input and the request, cross-referencing a combination of the verbal input and the request with an attribute database to identify an attribute associated with the combination; and associating the identified attribute with the media asset.
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1. A method for tagging a media asset, the method comprising: receiving verbal input from a user while the user is accessing the media asset; receiving a request to adjust playback of the media asset; responsive to receiving the verbal input and the request, cross-referencing a combination of the verbal input and the request with an attribute database to identify an attribute associated with the combination; and associating the identified attribute with the media asset. 7. The method of claim 1 further comprising transmitting a communication to a server with the identified attribute and an identifier of the media asset.
| 0.643928 |
1. A system for assessing sentiment of text, comprising: an input device; a display; and processing circuitry, wherein the processing circuitry is configured to control the system to at least: receive text input via the input device, the text associated with a review relating to a particular topic; and as the text is being inputted, determine, based on respective scores calculated for each of one or more words included in the received text, a real-time orientation value reflecting a sentiment of the received text; and modify an appearance of a visual display element on the display based on the determined real-time orientation value.
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1. A system for assessing sentiment of text, comprising: an input device; a display; and processing circuitry, wherein the processing circuitry is configured to control the system to at least: receive text input via the input device, the text associated with a review relating to a particular topic; and as the text is being inputted, determine, based on respective scores calculated for each of one or more words included in the received text, a real-time orientation value reflecting a sentiment of the received text; and modify an appearance of a visual display element on the display based on the determined real-time orientation value. 8. The system according to claim 1 , wherein the real-time orientation value is determined by extracting one or more words from the received text; accessing a training corpus to identify probability scores associated with the words; performing a central tendency calculation for the received text by evaluating the probability scores associated with the words; and assigning the orientation value to the received text based at least on the central tendency calculation.
| 0.656604 |
1. A method, implemented by a computing device, comprising: receiving a training data set that comprises a plurality of sensitive documents and a plurality of non-sensitive documents; determining, by the computing device, a quality of the training data set, wherein determining the quality of the training data set comprises performing at least one of k-fold cross validation or latent semantic indexing using the training data set; in response to determining that the training data set has a satisfactory quality, analyzing, by the computing device, the training data set using machine learning to generate a machine learning-based detection (MLD) profile, the MLD profile to be used by a data loss prevention (DLP) system to classify new documents as sensitive documents or as non-sensitive documents; and in response to determining that the training data set does not have satisfactory quality, identifying at least one document from the training data set that caused the quality of the training data set to be reduced.
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1. A method, implemented by a computing device, comprising: receiving a training data set that comprises a plurality of sensitive documents and a plurality of non-sensitive documents; determining, by the computing device, a quality of the training data set, wherein determining the quality of the training data set comprises performing at least one of k-fold cross validation or latent semantic indexing using the training data set; in response to determining that the training data set has a satisfactory quality, analyzing, by the computing device, the training data set using machine learning to generate a machine learning-based detection (MLD) profile, the MLD profile to be used by a data loss prevention (DLP) system to classify new documents as sensitive documents or as non-sensitive documents; and in response to determining that the training data set does not have satisfactory quality, identifying at least one document from the training data set that caused the quality of the training data set to be reduced. 6. The method of claim 1 , further comprising: identifying at least one of a document moving through a data loss vector or a request to move the document through the data loss vector; and determining whether the document is a sensitive document or a non-sensitive document based on application of the MLD profile.
| 0.735371 |
1. A computer-implemented method for determining a best alias rule in a semiconductor manufacturing process, the method comprising: obtaining an original rule and candidate alias rules; comparing the original rule to the candidate alias rules; ranking the candidate alias rules according to the comparison; filtering the ranked candidate alias rules; and selecting one rule among the filtered candidate alias rules based on a user's knowledge of the semiconductor manufacturing process, wherein the ranking includes: computing each distance from each candidate alias rule to the original rule; ordering the candidate alias rules in an ascending or descending order of the computed distances; comparing the computed distance of each candidate rule with at least one threshold; removing one or more of the candidate alias rules if the computed distance of the one or more of the candidate alias rules does not satisfy the at least one threshold, wherein the computing includes: calculating a full-alias value of each candidate alias rule, the full-alias value representing a measured degree of a similarity between a candidate alias rule and the original rule; calculating a used-alias value of each candidate alias rule, the used-alias value representing a measured dependency or correlation between a first tool step included in the original rule and a second tool step included in a candidate alias rule; and calculating a target-alias value of each candidate alias rule, the target-alias value representing a measure of a relative difference between a first target mean associated with the original rule and a second target mean associated with a first candidate alias rule, the first target mean including first measurements associated with the original rule, the second target mean including second measurements associated with the first candidate alias rule, the first candidate alias rule being one or more of the candidate alias rules, the first measurements including one or more of speed, power consumption and yield rate of semiconductor products manufactured according to the original rule, the second measurements including one or more of speed, power consumption and yield rate of semiconductor products manufactured according to the first candidate alias rule.
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1. A computer-implemented method for determining a best alias rule in a semiconductor manufacturing process, the method comprising: obtaining an original rule and candidate alias rules; comparing the original rule to the candidate alias rules; ranking the candidate alias rules according to the comparison; filtering the ranked candidate alias rules; and selecting one rule among the filtered candidate alias rules based on a user's knowledge of the semiconductor manufacturing process, wherein the ranking includes: computing each distance from each candidate alias rule to the original rule; ordering the candidate alias rules in an ascending or descending order of the computed distances; comparing the computed distance of each candidate rule with at least one threshold; removing one or more of the candidate alias rules if the computed distance of the one or more of the candidate alias rules does not satisfy the at least one threshold, wherein the computing includes: calculating a full-alias value of each candidate alias rule, the full-alias value representing a measured degree of a similarity between a candidate alias rule and the original rule; calculating a used-alias value of each candidate alias rule, the used-alias value representing a measured dependency or correlation between a first tool step included in the original rule and a second tool step included in a candidate alias rule; and calculating a target-alias value of each candidate alias rule, the target-alias value representing a measure of a relative difference between a first target mean associated with the original rule and a second target mean associated with a first candidate alias rule, the first target mean including first measurements associated with the original rule, the second target mean including second measurements associated with the first candidate alias rule, the first candidate alias rule being one or more of the candidate alias rules, the first measurements including one or more of speed, power consumption and yield rate of semiconductor products manufactured according to the original rule, the second measurements including one or more of speed, power consumption and yield rate of semiconductor products manufactured according to the first candidate alias rule. 2. The computer-implemented method according to claim 1 , wherein the original rule includes a binary decision rule comprising an IF statement, an ELSE statement and a THEN statement.
| 0.651821 |
4. A machine implemented method of communicating, comprising: (i) receiving an electronic message, via a device having a processing unit and program code stored on a storage device of said device; (ii) receiving a well-known animation character, via the device; (iii) converting the electronic message into speech using one of synthesized voice of the well-known animation character and actual voice of the well-known animation character, via the device; (iv) generating moving images of the well-known animation character, via the device; (v) outputting the speech, via the device; and (vi) displaying the moving images, via the device.
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4. A machine implemented method of communicating, comprising: (i) receiving an electronic message, via a device having a processing unit and program code stored on a storage device of said device; (ii) receiving a well-known animation character, via the device; (iii) converting the electronic message into speech using one of synthesized voice of the well-known animation character and actual voice of the well-known animation character, via the device; (iv) generating moving images of the well-known animation character, via the device; (v) outputting the speech, via the device; and (vi) displaying the moving images, via the device. 16. The method of claim 4 , wherein the step of outputting the speech comprises playing back the speech.
| 0.800206 |
30. A system, comprising: one or more computers and one or more storage devices storing instructions that when executed by the one or more computers cause the one or more computers to perform operations comprising: receiving a re-submitted query and a first time token from a client, wherein the first time token is data that identifies a most-recent time that any resource identified by any of the real-time search results presented by the client was undated; identifying real-time search results that are responsive to the re-submitted query and are more recent than the time identified by the first time token, wherein an identified real-time search result is more recent than the most recent time identified by the first time token received from the client when a resource identified by the identified real-time search received from the client; and sending the identified real-time search results and a second time token to the client, wherein the second time token is data the identifies a most-recent time that any resource identified by any of the identified real-time search results was updated.
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30. A system, comprising: one or more computers and one or more storage devices storing instructions that when executed by the one or more computers cause the one or more computers to perform operations comprising: receiving a re-submitted query and a first time token from a client, wherein the first time token is data that identifies a most-recent time that any resource identified by any of the real-time search results presented by the client was undated; identifying real-time search results that are responsive to the re-submitted query and are more recent than the time identified by the first time token, wherein an identified real-time search result is more recent than the most recent time identified by the first time token received from the client when a resource identified by the identified real-time search received from the client; and sending the identified real-time search results and a second time token to the client, wherein the second time token is data the identifies a most-recent time that any resource identified by any of the identified real-time search results was updated. 33. The system of claim 30 , wherein identifying real time search results that are responsive to the re-submitted query and are more recent than the time identified by the first time token includes filtering duplicate search results.
| 0.665019 |
1. A method for improving search and retrieval of resources in a collaborative tagging system, the method comprising: identifying, using a computing system in communication with a plurality of computer accessible resources, relationships among a plurality of tags in the collaborative tagging system, the plurality of tags associated by a plurality of users with the plurality of resources, and the relationships among the plurality of tags comprising sub-tag relationships between given pairs of tags based on co-occurrence of those pairs of tags at a given resource and associative semantics, the associative semantics comprising common association between tags in each given pair of tags by the plurality of users; using the identified tag relationships based on tag co-occurrence statistics to create a hierarchy of tags, the hierarchy comprising a directed acyclic graph where nodes in the directed acyclic graph comprise tags and edges in the directed acyclic graph comprise the identified tag relationships; using the created hierarchy to infer additional tags for resources automatically, to increase a number of tags associated with each resource in order to increase a total number of resources uncovered by a tag-based search of the plurality of resources and to maximize the recall of a tag cloud comprising a plurality of tags; and using the tag hierarchies to include inferred tags in the description of each one of the plurality of resources, increasing the weight of top level tags in the hierarchy and removing lower level tags from the tag cloud.
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1. A method for improving search and retrieval of resources in a collaborative tagging system, the method comprising: identifying, using a computing system in communication with a plurality of computer accessible resources, relationships among a plurality of tags in the collaborative tagging system, the plurality of tags associated by a plurality of users with the plurality of resources, and the relationships among the plurality of tags comprising sub-tag relationships between given pairs of tags based on co-occurrence of those pairs of tags at a given resource and associative semantics, the associative semantics comprising common association between tags in each given pair of tags by the plurality of users; using the identified tag relationships based on tag co-occurrence statistics to create a hierarchy of tags, the hierarchy comprising a directed acyclic graph where nodes in the directed acyclic graph comprise tags and edges in the directed acyclic graph comprise the identified tag relationships; using the created hierarchy to infer additional tags for resources automatically, to increase a number of tags associated with each resource in order to increase a total number of resources uncovered by a tag-based search of the plurality of resources and to maximize the recall of a tag cloud comprising a plurality of tags; and using the tag hierarchies to include inferred tags in the description of each one of the plurality of resources, increasing the weight of top level tags in the hierarchy and removing lower level tags from the tag cloud. 2. The method of claim 1 , wherein the step of identifying the relationships further comprises identifying relationships between selected pairs of tags.
| 0.577484 |
5. The system of claim 2 , wherein the executable instructions cause the processor to further perform the operations comprising: receive, from the second user, a search query to search for document information in the database, wherein the search query from the second user is identical to the search query from the first user.
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5. The system of claim 2 , wherein the executable instructions cause the processor to further perform the operations comprising: receive, from the second user, a search query to search for document information in the database, wherein the search query from the second user is identical to the search query from the first user. 6. The system of claim 5 , wherein the executable instructions cause the processor to further perform the operations comprising: determine, using one or more processors, that the terms of the search query from the second user are not in accordance with the dictionary information corresponding to the second user.
| 0.822303 |
1. A system for transmitting and displaying electronic media from a service provider to a subscriber comprising: a service provider computer coupled to a controller of the sub scriber via a network connection; software executing on said service provider computer to present a web page that is used to log onto the system; an electronic media library accessible by said service provider computer, said electronic media library searchable by key word; wherein, prior to initiating a search from the subscriber via the web page, the electronic media library, via the software, displays an index of key words available for searching and a number count for each of the key words, the number count indicating the number of different media content associated with each of the respective key words; wherein, when the electronic media library is searched by a user-selected key word from the index, a plurality of pre-assembled media content associated with the key word is presented, and upon receiving a user-selected one of the plurality of pre-assembled media content presented, the selected content is presented allowing the sub scriber to view the selected pre-assembled media content prior to the content being added to the electronic media collection; wherein the pre-assembled media content that is added to the electronic media collection is configured for transmission to a display; wherein a value specifying a number of discrete media for presentment as a preview at one time and, both prior to selection of any of the plurality of pre-assembled media content and prior to being added to the electronic media collection as the plurality of pre-assembled media content, is selectable by the user via the web page.
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1. A system for transmitting and displaying electronic media from a service provider to a subscriber comprising: a service provider computer coupled to a controller of the sub scriber via a network connection; software executing on said service provider computer to present a web page that is used to log onto the system; an electronic media library accessible by said service provider computer, said electronic media library searchable by key word; wherein, prior to initiating a search from the subscriber via the web page, the electronic media library, via the software, displays an index of key words available for searching and a number count for each of the key words, the number count indicating the number of different media content associated with each of the respective key words; wherein, when the electronic media library is searched by a user-selected key word from the index, a plurality of pre-assembled media content associated with the key word is presented, and upon receiving a user-selected one of the plurality of pre-assembled media content presented, the selected content is presented allowing the sub scriber to view the selected pre-assembled media content prior to the content being added to the electronic media collection; wherein the pre-assembled media content that is added to the electronic media collection is configured for transmission to a display; wherein a value specifying a number of discrete media for presentment as a preview at one time and, both prior to selection of any of the plurality of pre-assembled media content and prior to being added to the electronic media collection as the plurality of pre-assembled media content, is selectable by the user via the web page. 8. The system according to claim 1 , wherein the electronic media collection comprises: selected pre-assembled media content and pre assembled media content that has been modified according to subscriber-provided criteria.
| 0.578712 |
4. The system of claim 2 , wherein the instructions further cause the processor to perform the operations of: receiving an input indicating selection of one of the displayed variants; and adjusting a weighting assigned to the selected variant.
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4. The system of claim 2 , wherein the instructions further cause the processor to perform the operations of: receiving an input indicating selection of one of the displayed variants; and adjusting a weighting assigned to the selected variant. 5. The system of claim 4 , wherein the instructions further cause the processor to perform the operations of: determining that the input indicating selection comprises a keystroke on a spacebar key or a punctuation key, and selecting, as the selected variant, a first variant of the displayed variants.
| 0.753005 |
1. A method for performing a search of a virtual repository formed from a plurality of repositories, each repository of the virtual repository being associated with a separate database system, the method comprising: receiving a string-based search expression; generating an expression tree of nodes based on the string-based search expression, one or more of the nodes of the expression tree being an attribute node, each attribute node corresponding to an attribute included in the string-based search expression; adding repository-location information to the expression tree by associating metadata with each attribute node in the expression tree, the metadata associated with a given attribute node identifying one or more repositories of the virtual repository that support the attribute represented by that attribute node; generating, for each repository identified by the metadata associated with the one or more attribute nodes, a query expression specifically for that repository; and determining whether sub-trees of a particular node of the expression tree have attribute nodes associated with metadata identifying different types of repositories, and, if the metadata associated with the attribute nodes identify different types of repositories, constructing the particular node as a federation node for merging search results returned by the child sub-trees; searching each repository identified by the metadata associated with the one or more attribute nodes using the query expression specifically generated for that repository.
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1. A method for performing a search of a virtual repository formed from a plurality of repositories, each repository of the virtual repository being associated with a separate database system, the method comprising: receiving a string-based search expression; generating an expression tree of nodes based on the string-based search expression, one or more of the nodes of the expression tree being an attribute node, each attribute node corresponding to an attribute included in the string-based search expression; adding repository-location information to the expression tree by associating metadata with each attribute node in the expression tree, the metadata associated with a given attribute node identifying one or more repositories of the virtual repository that support the attribute represented by that attribute node; generating, for each repository identified by the metadata associated with the one or more attribute nodes, a query expression specifically for that repository; and determining whether sub-trees of a particular node of the expression tree have attribute nodes associated with metadata identifying different types of repositories, and, if the metadata associated with the attribute nodes identify different types of repositories, constructing the particular node as a federation node for merging search results returned by the child sub-trees; searching each repository identified by the metadata associated with the one or more attribute nodes using the query expression specifically generated for that repository. 3. The method of claim 1 , wherein the virtual repository includes one or more horizontally partitioned repositories and one or more vertically partitioned repositories.
| 0.654383 |
1. Document processing apparatus, comprising: key input means for entering characters comprising documents and titles as a string of characters and for entering instructions into said apparatus for sequentially displaying each of the titles, and for retrieving the document associated with the currently displayed title; means for storing the documents and titles in a random order, said storing means having a field for numbering, an evaluation value obtained by comparing each of titles being stored in the field to obtain a language dictionary word arrangement of the titles, and a currently displayed title being changed to a title to be displayed with other titles in a reverse sequence or in a forward sequence in accordance with the stored evaluation value; means for assigning titles an order by comparing each of the titles and generating the evaluation value for each of the titles, the order of the titles being indicated by the generated evaluation values; means responsive to generation of an instruction by said key input means for sequentially displaying each of the titles stored in said storing means in accordance with the order assigned thereto by said assigning means with display of one title being terminated upon display of the next title in accordance with the assigned orders thereof, the instruction for sequentially displaying each of the titles stored in said storing means entered by said key input means specifying the sequential display either in a forward sequence or a reverse sequence; and means responsive to an instruction for retrieving a document associated with the currently displayed title generated by said key input means for retrieving the document associated with the title displayed currently.
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1. Document processing apparatus, comprising: key input means for entering characters comprising documents and titles as a string of characters and for entering instructions into said apparatus for sequentially displaying each of the titles, and for retrieving the document associated with the currently displayed title; means for storing the documents and titles in a random order, said storing means having a field for numbering, an evaluation value obtained by comparing each of titles being stored in the field to obtain a language dictionary word arrangement of the titles, and a currently displayed title being changed to a title to be displayed with other titles in a reverse sequence or in a forward sequence in accordance with the stored evaluation value; means for assigning titles an order by comparing each of the titles and generating the evaluation value for each of the titles, the order of the titles being indicated by the generated evaluation values; means responsive to generation of an instruction by said key input means for sequentially displaying each of the titles stored in said storing means in accordance with the order assigned thereto by said assigning means with display of one title being terminated upon display of the next title in accordance with the assigned orders thereof, the instruction for sequentially displaying each of the titles stored in said storing means entered by said key input means specifying the sequential display either in a forward sequence or a reverse sequence; and means responsive to an instruction for retrieving a document associated with the currently displayed title generated by said key input means for retrieving the document associated with the title displayed currently. 3. A document processing apparatus according to claim 1, wherein said key input means enters a selection instruction into the apparatus instructing halting of sequential display of each of the titles stored in said storing means and the maintaining of the display of a currently displayed title.
| 0.574572 |
9. An email message generator comprising a computer programmed to generate and transmit email messages over a data network, said email message generator comprising: an analysis component for analyzing an outgoing email message in rich text format to identify one or more sections consisting of characters having a same formatting characteristic; a converter component for converting the email message from said rich text format to a platform-independent format; a generating component for generating one or more character images for characters found in said outgoing email message, and a set of replacement instructions for use at a destination system to render the email message by replacing characters specified in the platform-independent format with a corresponding character image from said character images; and a transmitting component for transmitting said outgoing email message to said destination system along with said one or more character images and said set of replacement instructions, wherein the analysis component further comprises a component for identifying a lowest common denominator of identified characters in the message data for use in generating character images by the generating component.
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9. An email message generator comprising a computer programmed to generate and transmit email messages over a data network, said email message generator comprising: an analysis component for analyzing an outgoing email message in rich text format to identify one or more sections consisting of characters having a same formatting characteristic; a converter component for converting the email message from said rich text format to a platform-independent format; a generating component for generating one or more character images for characters found in said outgoing email message, and a set of replacement instructions for use at a destination system to render the email message by replacing characters specified in the platform-independent format with a corresponding character image from said character images; and a transmitting component for transmitting said outgoing email message to said destination system along with said one or more character images and said set of replacement instructions, wherein the analysis component further comprises a component for identifying a lowest common denominator of identified characters in the message data for use in generating character images by the generating component. 16. The email message generator of claim 9 , wherein the set of replacement instructions instruct the destination system use a same character image to replace a same character at multiple locations within said email message.
| 0.576466 |
8. An article comprising a computer-readable storage medium containing instructions that if executed enable a system to: create a note with a first application program; generate a context reference for a target document for a second application program, the context reference comprising context information useable to recreate a user context for the document; and insert the context reference within the note.
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8. An article comprising a computer-readable storage medium containing instructions that if executed enable a system to: create a note with a first application program; generate a context reference for a target document for a second application program, the context reference comprising context information useable to recreate a user context for the document; and insert the context reference within the note. 10. The article of claim 8 , further comprising instructions that if executed enable the system to generate the context reference to represent a source for the second application program.
| 0.556738 |
22. The computer program product of claim 19 , wherein: the computer program instructions further include computer program instructions operable to cause the at least one computing device to determine the characterization of topic incoherency associated with each of the at least one document linking to that particular document includes, for each of the at least one document linking to that particular document, processing an indication of topics associated with that linking document.
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22. The computer program product of claim 19 , wherein: the computer program instructions further include computer program instructions operable to cause the at least one computing device to determine the characterization of topic incoherency associated with each of the at least one document linking to that particular document includes, for each of the at least one document linking to that particular document, processing an indication of topics associated with that linking document. 23. The computer program product of claim 22 , wherein: the computer program instructions operable to cause the at least one computing device to process an indication of topics associated with that linking document includes computer program instructions operable to cause the at least one computing device to determine the characterization of topic incoherency as a function of scores, with respect to a plurality of topics, for that linking document.
| 0.852665 |
12. A computer system, comprising: a processor for executing instructions; and a computer readable medium, coupled to the processor in operation, wherein the computer readable medium contains code modules of instructions that, if executed by the processor, are operable to cause the computer system to receive a query; identify a first term in the query, wherein the first term comprises a word or a phrase; match the first term with a search index associated with a plurality of documents, or with a searchable collection of documents, each document comprising one or more terms; return at least a portion of the plurality of documents or document representations as a search result based on the query, wherein at least two of the documents in the search result contain or are associated with the first term; and rank the search result based at least on a first score associated with the first term, wherein the first score is produced using a first groups of steps or a second group of steps, wherein the first group of steps comprise: (a) identifying a document in the plurality of documents, (b) identifying a sentence containing the first term and a second term, wherein the sentence comprises a subject and a non-subject portion of the sentence, (c) obtaining a pre-defined criterion for a computer program to determine whether the first term is of greater importance than the second term, or the second term is of greater importance than the first term based on whether the first term or the second term is contained in the subject or the non-subject portion of the sentence, and (d) determining the first score based at least on the pre-defined criterion; wherein the second group of steps comprise: (e) tokenizing the document into tokens as instances of terms, each token or term comprising one or more words or phrases; (f) identifying a first token instance of the first term, wherein the first token instance is or is contained in a grammatical subject of a sentence; (g) assigning a first importance value to the first token instance for being or being contained in a grammatical subject of a sentence; (h) identifying a second token instance of the first term, wherein the second token instance is or is in a non-subject portion of a sentence; (i) assigning a second importance value to the second token instance for not being or not being contained in a grammatical subject of a sentence, wherein the second importance value is different from the first importance value; (j) determining the first score for the first term based on the sum of the first importance value and the second importance value.
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12. A computer system, comprising: a processor for executing instructions; and a computer readable medium, coupled to the processor in operation, wherein the computer readable medium contains code modules of instructions that, if executed by the processor, are operable to cause the computer system to receive a query; identify a first term in the query, wherein the first term comprises a word or a phrase; match the first term with a search index associated with a plurality of documents, or with a searchable collection of documents, each document comprising one or more terms; return at least a portion of the plurality of documents or document representations as a search result based on the query, wherein at least two of the documents in the search result contain or are associated with the first term; and rank the search result based at least on a first score associated with the first term, wherein the first score is produced using a first groups of steps or a second group of steps, wherein the first group of steps comprise: (a) identifying a document in the plurality of documents, (b) identifying a sentence containing the first term and a second term, wherein the sentence comprises a subject and a non-subject portion of the sentence, (c) obtaining a pre-defined criterion for a computer program to determine whether the first term is of greater importance than the second term, or the second term is of greater importance than the first term based on whether the first term or the second term is contained in the subject or the non-subject portion of the sentence, and (d) determining the first score based at least on the pre-defined criterion; wherein the second group of steps comprise: (e) tokenizing the document into tokens as instances of terms, each token or term comprising one or more words or phrases; (f) identifying a first token instance of the first term, wherein the first token instance is or is contained in a grammatical subject of a sentence; (g) assigning a first importance value to the first token instance for being or being contained in a grammatical subject of a sentence; (h) identifying a second token instance of the first term, wherein the second token instance is or is in a non-subject portion of a sentence; (i) assigning a second importance value to the second token instance for not being or not being contained in a grammatical subject of a sentence, wherein the second importance value is different from the first importance value; (j) determining the first score for the first term based on the sum of the first importance value and the second importance value. 17. The computer system of claim 12 , when the second group of steps are used, the first score for the first term is determined further based on a first frequency of the first term in the text content.
| 0.507522 |
1. A method for searching a corpus of documents, comprising: defining a knowledge domain; identifying a set of reference documents in the corpus pertinent to the domain; inputting a first query; searching the corpus using the set of reference documents to find one or more of the documents in the corpus that contain information in the domain relevant to the first query; and adding at least one of the found documents to the set of reference documents for use in searching the corpus for information in the domain relevant to a second, subsequent query, which is substantially different from the first query, wherein searching the corpus comprises searching the corpus to find the documents that contain the information relevant to the query and ranking the found documents by comparing them to the set of reference documents, and wherein ranking the found documents comprises assessing links between the found documents and the reference documents.
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1. A method for searching a corpus of documents, comprising: defining a knowledge domain; identifying a set of reference documents in the corpus pertinent to the domain; inputting a first query; searching the corpus using the set of reference documents to find one or more of the documents in the corpus that contain information in the domain relevant to the first query; and adding at least one of the found documents to the set of reference documents for use in searching the corpus for information in the domain relevant to a second, subsequent query, which is substantially different from the first query, wherein searching the corpus comprises searching the corpus to find the documents that contain the information relevant to the query and ranking the found documents by comparing them to the set of reference documents, and wherein ranking the found documents comprises assessing links between the found documents and the reference documents. 6. The method according to claim 1 , wherein adding the at least one of the found documents comprises adding at least the document having the highest ranking.
| 0.58865 |
9. A computer system for use in partitioning a plurality of documents, said computer system comprising: a plurality of processing engines; and a non-transitory memory device accessible by said processing engines, said memory device for storing a plurality of documents, at least a portion of the plurality of documents including customer feedback content and support content responsive to the customer feedback content, wherein said plurality of processing engines are configured to perform the operations of: receiving a plurality of documents from a plurality of different communication channels, at least a portion of the plurality of documents including a set of content representing an interaction between a user and a support entity, wherein the set of content includes both customer feedback content received from a user, and support content provided by the support entity and to the user responsive to the customer feedback content; identifying, within each document of the portion of the received plurality of documents, the customer feedback content received from a user and the support content provided to the user responsive to the customer feedback; filtering, by a processor executing the instructions, the portion of the received plurality of documents, including removing the customer feedback content and retaining the support content; partitioning, after the filtering, the plurality of filtered documents, in which the customer feedback has been removed, into multiple clusters based on the support content of each of the plurality of filtered documents; after the partitioning, for each filtered document in each cluster of the multiple clusters, associating the customer feedback content that was filtered from the filtered document with the cluster to which the filtered document belongs and the retained support content from that filtered document, and storing association information in memory; receiving a new document including customer feedback related to an issue; determining, using the association information in the memory, that the customer feedback of the new document matches the customer feedback content associated with one of the clusters; and providing, over a communication connection with a user associated with the new document, the retained support content associated with the cluster based on the match between the customer feedback of the new document and the customer feedback content that was associated with the cluster after the partitioning.
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9. A computer system for use in partitioning a plurality of documents, said computer system comprising: a plurality of processing engines; and a non-transitory memory device accessible by said processing engines, said memory device for storing a plurality of documents, at least a portion of the plurality of documents including customer feedback content and support content responsive to the customer feedback content, wherein said plurality of processing engines are configured to perform the operations of: receiving a plurality of documents from a plurality of different communication channels, at least a portion of the plurality of documents including a set of content representing an interaction between a user and a support entity, wherein the set of content includes both customer feedback content received from a user, and support content provided by the support entity and to the user responsive to the customer feedback content; identifying, within each document of the portion of the received plurality of documents, the customer feedback content received from a user and the support content provided to the user responsive to the customer feedback; filtering, by a processor executing the instructions, the portion of the received plurality of documents, including removing the customer feedback content and retaining the support content; partitioning, after the filtering, the plurality of filtered documents, in which the customer feedback has been removed, into multiple clusters based on the support content of each of the plurality of filtered documents; after the partitioning, for each filtered document in each cluster of the multiple clusters, associating the customer feedback content that was filtered from the filtered document with the cluster to which the filtered document belongs and the retained support content from that filtered document, and storing association information in memory; receiving a new document including customer feedback related to an issue; determining, using the association information in the memory, that the customer feedback of the new document matches the customer feedback content associated with one of the clusters; and providing, over a communication connection with a user associated with the new document, the retained support content associated with the cluster based on the match between the customer feedback of the new document and the customer feedback content that was associated with the cluster after the partitioning. 11. The system of claim 9 , wherein the support content includes at least one support solution, and wherein partitioning the plurality of filtered documents includes partitioning the plurality of filtered documents based on the at least one support solution.
| 0.502197 |
7. At least one computer-readable storage medium having encoded thereon computer-executable instructions that, when executed by at least one computer, cause the at least one computer to carry out a method of processing results of a recognition by an automatic speech recognition (ASR) system on an utterance, the results comprising two or more results identified by the ASR system as likely to be accurate recognition results for the utterance, the two or more results comprising a first result and a second result, wherein the first result is identified by the ASR system as most likely among the two or more results to be an accurate recognition result, the method comprising: evaluating the first result using a medical fact extractor to extract a first set of one or more medical facts; evaluating the second result using the medical fact extractor to extract a second set of one or more medical facts; determining whether the first set of one or more medical facts has a meaning that is different in a medically significant way from a meaning of the second set of one or more medical facts; and in response to determining that the first set of one or more medical facts has a meaning that is different in a medically significant way from the meaning of the second set of one or more medical facts, triggering an alert, wherein the determining whether the first set of one or more medical facts has a meaning that is different in a medically significant way from the second set of one or more medical facts comprises: determining whether the first set comprises a medical fact having a fact type that is marked as significant; when it is determined that the first set comprises the medical fact having the fact type that is marked as significant, determining whether the second set comprises the medical fact; and in response to determining that the second set does not comprise the medical fact, determining that the second set has a meaning that differs from a meaning of the first set in a medically significant way.
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7. At least one computer-readable storage medium having encoded thereon computer-executable instructions that, when executed by at least one computer, cause the at least one computer to carry out a method of processing results of a recognition by an automatic speech recognition (ASR) system on an utterance, the results comprising two or more results identified by the ASR system as likely to be accurate recognition results for the utterance, the two or more results comprising a first result and a second result, wherein the first result is identified by the ASR system as most likely among the two or more results to be an accurate recognition result, the method comprising: evaluating the first result using a medical fact extractor to extract a first set of one or more medical facts; evaluating the second result using the medical fact extractor to extract a second set of one or more medical facts; determining whether the first set of one or more medical facts has a meaning that is different in a medically significant way from a meaning of the second set of one or more medical facts; and in response to determining that the first set of one or more medical facts has a meaning that is different in a medically significant way from the meaning of the second set of one or more medical facts, triggering an alert, wherein the determining whether the first set of one or more medical facts has a meaning that is different in a medically significant way from the second set of one or more medical facts comprises: determining whether the first set comprises a medical fact having a fact type that is marked as significant; when it is determined that the first set comprises the medical fact having the fact type that is marked as significant, determining whether the second set comprises the medical fact; and in response to determining that the second set does not comprise the medical fact, determining that the second set has a meaning that differs from a meaning of the first set in a medically significant way. 9. The at least one computer-readable storage medium of claim 7 , wherein the determining whether the first set of one or more medical facts has a meaning that is different in a medically significant way from the second set of one or more medical facts further comprises: comparing the first set to the second set to determine whether the first set of medical facts differs from the second set of medical facts; and when it is determined that the first set differs from the second set, determining that the first set has a meaning that differs in a medically significant way from a meaning of the second set.
| 0.5 |
10. The apparatus of claim 9 , wherein the learning process is configured in accordance with a teaching signal.
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10. The apparatus of claim 9 , wherein the learning process is configured in accordance with a teaching signal. 11. The apparatus of claim 10 , wherein the guiding of the robot apparatus using the one or more actuators to the target state is configured based on a control signal determined by the learning process in accordance with the conveyed information, and the teaching signal is configured based on the correction signal.
| 0.841816 |
18. The method as recited in claim 16 , wherein the act of constructing comprises: creating a meta-node to represent a specified service that is to be monitored; creating an observation node corresponding to an endhost client that is to report response time observations for the specified service; and making the meta-node a parent of the observation node.
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18. The method as recited in claim 16 , wherein the act of constructing comprises: creating a meta-node to represent a specified service that is to be monitored; creating an observation node corresponding to an endhost client that is to report response time observations for the specified service; and making the meta-node a parent of the observation node. 20. The method as recited in claim 18 , wherein the act of constructing further comprises: adding a service meta-node for each dependent service on which the specified service depends; recursively adding service meta-nodes for services on which each dependent service of the specified service is dependent; and creating respective root-cause nodes corresponding to respective endhosts that provide the specified service, at least one of the dependent services, or at least one of the services on which a dependent service depends.
| 0.740654 |
1. A computer implemented method comprising the steps of: displaying a plurality of document series display sections; and displaying a plurality of document entries in each of the plurality of document series display sections, the document entries in each of the document series display sections corresponding to a document series, each of the document entries having a presentation state selected from a group comprising an expanded state and a contracted state, each of the document entries corresponding to a different document, the presentation state of the document entries being selectable by a user, wherein when the presentation state of one of the one or more document entries is the expanded state, displaying at least a name of an author of a document associated with the corresponding document entry and a plurality of user-selectable icons, wherein when the presentation state of the one of the document entries is in the contracted state, hiding the user-selectable icons, wherein when one of the user-selectable icons is selected, opening an email window, the email window including a link to the document, wherein when one of the user-selectable icons is selected, displaying the document entries in the document series display section corresponding to the document series, wherein each document series display section has a different document series publisher, wherein each publisher maintains editing control of one of the plurality of document entries corresponding to the document series.
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1. A computer implemented method comprising the steps of: displaying a plurality of document series display sections; and displaying a plurality of document entries in each of the plurality of document series display sections, the document entries in each of the document series display sections corresponding to a document series, each of the document entries having a presentation state selected from a group comprising an expanded state and a contracted state, each of the document entries corresponding to a different document, the presentation state of the document entries being selectable by a user, wherein when the presentation state of one of the one or more document entries is the expanded state, displaying at least a name of an author of a document associated with the corresponding document entry and a plurality of user-selectable icons, wherein when the presentation state of the one of the document entries is in the contracted state, hiding the user-selectable icons, wherein when one of the user-selectable icons is selected, opening an email window, the email window including a link to the document, wherein when one of the user-selectable icons is selected, displaying the document entries in the document series display section corresponding to the document series, wherein each document series display section has a different document series publisher, wherein each publisher maintains editing control of one of the plurality of document entries corresponding to the document series. 7. The method of claim 1 , wherein when one of the user-selectable icons is selected, deleting the document entry.
| 0.632302 |
1. A system comprising at least one computer processor for analyzing requirements data, comprising: a requirements database storing partially-structured data related to a subject matter domain; and an analytic tool, comprising: a user interface configured to receive a query from a user; and an analyzer configured to: parse the query into a plurality of search terms; identify a first requirement from the requirements database based on a degree of relatedness between the plurality of search terms and the textual content of the first requirement; identify a plurality of soft links between the first requirement and a plurality of related requirements, wherein each of the plurality of soft links represents a relatedness score between the first requirement and a related requirement from the plurality of related requirements, and each of the relatedness scores exceeds a threshold; rank the plurality of related requirements based on their relatedness scores; and provide the highest ranked requirement to the user.
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1. A system comprising at least one computer processor for analyzing requirements data, comprising: a requirements database storing partially-structured data related to a subject matter domain; and an analytic tool, comprising: a user interface configured to receive a query from a user; and an analyzer configured to: parse the query into a plurality of search terms; identify a first requirement from the requirements database based on a degree of relatedness between the plurality of search terms and the textual content of the first requirement; identify a plurality of soft links between the first requirement and a plurality of related requirements, wherein each of the plurality of soft links represents a relatedness score between the first requirement and a related requirement from the plurality of related requirements, and each of the relatedness scores exceeds a threshold; rank the plurality of related requirements based on their relatedness scores; and provide the highest ranked requirement to the user. 2. The system of claim 1 , wherein the analyzer is configured to provide a plurality of requirements to the user in an order based on their relatedness scores.
| 0.633449 |
1. A computer-implemented system for application protocol field extraction, comprising: an extraction specification that specifies data elements to be extracted from data packets and is expressed in terms of a context-free grammar, where the grammar defines grammatical structures of data packets transmitted in accordance with an application protocol and is defined as a tuple having nonterminals, terminals, counters, production rules and a start nonterminal, such that the counters are variables with an integer value used to chronicle parsing history of the production rules, and at least one of the production rules includes an action association with a terminal or nonterminal comprising body of the production rule and the action is an expression for updating a value of a counter defined by the grammar; an automata generator configured to receive the extraction specification and generate a counting automaton; and a field extractor configured to receive a data flow comprised of a plurality of data packets traversing through a network and extract data elements from the data packets in accordance with the counting automaton, where the field extractor is implemented by an integrated circuit.
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1. A computer-implemented system for application protocol field extraction, comprising: an extraction specification that specifies data elements to be extracted from data packets and is expressed in terms of a context-free grammar, where the grammar defines grammatical structures of data packets transmitted in accordance with an application protocol and is defined as a tuple having nonterminals, terminals, counters, production rules and a start nonterminal, such that the counters are variables with an integer value used to chronicle parsing history of the production rules, and at least one of the production rules includes an action association with a terminal or nonterminal comprising body of the production rule and the action is an expression for updating a value of a counter defined by the grammar; an automata generator configured to receive the extraction specification and generate a counting automaton; and a field extractor configured to receive a data flow comprised of a plurality of data packets traversing through a network and extract data elements from the data packets in accordance with the counting automaton, where the field extractor is implemented by an integrated circuit. 2. The system of claim 1 wherein the production rules are in the form of <predicate>: <nonterminal>→<body>, such that the body is an ordered sequence of terminals and nonterminals and each predicate is expressed in terms of at least one counter.
| 0.648748 |
4. The method according to claim 1 , wherein the list of phonemes includes a plurality of phoneme sequences and wherein obtaining the list of phonemes includes obtaining the list of phonemes, at least in part, by analyzing the text database.
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4. The method according to claim 1 , wherein the list of phonemes includes a plurality of phoneme sequences and wherein obtaining the list of phonemes includes obtaining the list of phonemes, at least in part, by analyzing the text database. 5. The method according to claim 4 , wherein the plurality of phoneme sequences comprise a plurality of diphones, a plurality of triphones, a plurality of quadphones, a plurality of syllables, and/or a plurality of bisyllables.
| 0.886263 |
1. A computer system tangibly operating in an information technology hardware and software environment, comprising: a knowledge base model for representation and storage of regulatory knowledge, the regulatory knowledge including the following knowledge base model entities: regulations, procedures, parameters, concepts, decisions, rules, service calls, and their features and relationships to other knowledge base model entities; a data interface model for representation and storage of regulatory data, the regulatory data including the following groups of data interface model entities: simple transactional events, complex events, referential entities, profiles, and their features and their relationships to other data interface model entities and their relationships to knowledge base model entities; a reasoning session data model for representation and storage of the reasoning session data, the reasoning session data including the following reasoning session data model entities: reasoning sessions, session events, and their features and their relationships to other session data model entities and their relationships to data interface model entities and their relationships to knowledge base model entities; an interface configured to receive a request in the form of a structured or semi-structured message from an external source, to pass the received message to a reasoning session controller, and upon receiving a response from the reasoning session controller, pass that response to the external source; a reasoning session controller configured to receive an input request from the interface and to match the input request to a decision record from the knowledge base model decision entity, execute programmable instructions from the decision record, perform post-processing tasks after execution of the programmable instructions is completed, and select a next new session event for execution; a library of procedures configured to be invoked by a service call, take a subset of parameters established by a reasoning session and use those parameters to call external services, and place results returned by the external services in a data interface repository.
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1. A computer system tangibly operating in an information technology hardware and software environment, comprising: a knowledge base model for representation and storage of regulatory knowledge, the regulatory knowledge including the following knowledge base model entities: regulations, procedures, parameters, concepts, decisions, rules, service calls, and their features and relationships to other knowledge base model entities; a data interface model for representation and storage of regulatory data, the regulatory data including the following groups of data interface model entities: simple transactional events, complex events, referential entities, profiles, and their features and their relationships to other data interface model entities and their relationships to knowledge base model entities; a reasoning session data model for representation and storage of the reasoning session data, the reasoning session data including the following reasoning session data model entities: reasoning sessions, session events, and their features and their relationships to other session data model entities and their relationships to data interface model entities and their relationships to knowledge base model entities; an interface configured to receive a request in the form of a structured or semi-structured message from an external source, to pass the received message to a reasoning session controller, and upon receiving a response from the reasoning session controller, pass that response to the external source; a reasoning session controller configured to receive an input request from the interface and to match the input request to a decision record from the knowledge base model decision entity, execute programmable instructions from the decision record, perform post-processing tasks after execution of the programmable instructions is completed, and select a next new session event for execution; a library of procedures configured to be invoked by a service call, take a subset of parameters established by a reasoning session and use those parameters to call external services, and place results returned by the external services in a data interface repository. 15. The system of claim 1 , wherein the interface is configured to operate as an instant messaging window while receiving input requests and sending output responses.
| 0.711528 |
12. A computer-implemented method comprising: receiving a graph query on data representing a graph including a plurality of nodes, the graph query involving a traversal from a source node to a sink node that is adjacent to the source node; converting the graph query into a structured query language (SQL) query over the first table and a second table; retrieving, from a source table in a relational database, a record corresponding to the source node, the record containing a multi-valued field that stores a multi-valued non-local property of the source node, the multi-valued non-local property including a reference to at least one adjacent node of the source node; obtaining, based on the multi-valued non-local property of the source node, data of the sink node from a sink table with the SQL query; storing, for each node in the graph, an adjacency list in the multi-valued field, the adjacency list containing the reference to the at least one adjacent node; transforming the adjacency list to a temporary table; and using the temporary table to locate the corresponding record.
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12. A computer-implemented method comprising: receiving a graph query on data representing a graph including a plurality of nodes, the graph query involving a traversal from a source node to a sink node that is adjacent to the source node; converting the graph query into a structured query language (SQL) query over the first table and a second table; retrieving, from a source table in a relational database, a record corresponding to the source node, the record containing a multi-valued field that stores a multi-valued non-local property of the source node, the multi-valued non-local property including a reference to at least one adjacent node of the source node; obtaining, based on the multi-valued non-local property of the source node, data of the sink node from a sink table with the SQL query; storing, for each node in the graph, an adjacency list in the multi-valued field, the adjacency list containing the reference to the at least one adjacent node; transforming the adjacency list to a temporary table; and using the temporary table to locate the corresponding record. 16. The method of claim 12 , wherein the graph query includes a pattern to be matched in the graph, the method further comprising: obtaining a plurality of candidate schemes for decomposition of the pattern, each of the candidate schemes resulting in a plurality of sub-patterns of the pattern; determining cost for each of the candidate schemes, the cost including sub-pattern cost associated with the individual sub-patterns resulted from the candidate scheme and connection cost associated with a connection of the sub-patterns; selecting one of the candidate schemes with the cost below a predefined threshold; and decomposing the pattern according to the selected candidate scheme.
| 0.550586 |
5. The method of claim 1 , wherein the node of the taxonomy is associated with at least one commonly-appearing feature commonly appearing in conversations related to the topic to which the node corresponds, the at least one commonly-appearing feature comprising one or more words and/or phrases, and wherein comparing the first set of one or more features to the information regarding the node comprises comparing the first set of one or more features of the at least the part of the conversation to the at least one commonly-appearing feature associated with the node.
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5. The method of claim 1 , wherein the node of the taxonomy is associated with at least one commonly-appearing feature commonly appearing in conversations related to the topic to which the node corresponds, the at least one commonly-appearing feature comprising one or more words and/or phrases, and wherein comparing the first set of one or more features to the information regarding the node comprises comparing the first set of one or more features of the at least the part of the conversation to the at least one commonly-appearing feature associated with the node. 6. The method of claim 5 , wherein: the node of the taxonomy is further associated with at least one second feature that is more likely to appear in conversations related to the topic to which the node corresponds than to appear in conversations unrelated to the topic to which the node corresponds; and wherein comparing the one or more features to the information regarding the node comprises comparing the one or more features of the at least the part of the conversation to the at least one commonly-appearing feature and to the at least one second feature associated with the node.
| 0.940248 |
11. An apparatus comprising: a memory having stored therein one or more digital signals to represent at least one file for a particular displayable web page to comprise at least two of a plurality of segmented portions; at least one processing unit coupled to the memory and programmed with instructions to: access the plurality of segmented portions of the at least one displayable web page, and use one or more machine learned models to: identify one or more feature properties of the plurality of segmented portions within the one or more files, or otherwise to be inferable from the one or more files, classify the at least two of the plurality of segmented portions as at least one of a plurality of segment types to be based, at least in part, on the one or more feature properties to be identified, the one or more feature properties to be identified are to comprise at least language feature properties of language model of content to be displayed in one or more of the at least two of the plurality of segmented portions, and determine content quality scores for at least two of the plurality of segmented portions of at least the particular displayable web page; and establish an index in the memory, the index to be established for the plurality of segmented portions and to be based, at least in part, on the segment type, the index to indicate the content quality scores.
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11. An apparatus comprising: a memory having stored therein one or more digital signals to represent at least one file for a particular displayable web page to comprise at least two of a plurality of segmented portions; at least one processing unit coupled to the memory and programmed with instructions to: access the plurality of segmented portions of the at least one displayable web page, and use one or more machine learned models to: identify one or more feature properties of the plurality of segmented portions within the one or more files, or otherwise to be inferable from the one or more files, classify the at least two of the plurality of segmented portions as at least one of a plurality of segment types to be based, at least in part, on the one or more feature properties to be identified, the one or more feature properties to be identified are to comprise at least language feature properties of language model of content to be displayed in one or more of the at least two of the plurality of segmented portions, and determine content quality scores for at least two of the plurality of segmented portions of at least the particular displayable web page; and establish an index in the memory, the index to be established for the plurality of segmented portions and to be based, at least in part, on the segment type, the index to indicate the content quality scores. 14. The apparatus as recited in claim 11 , wherein at least one of the one or more machine learned models is to operate in an unsupervised mode and is to identify one or more digital signals to represent a vector space representation as one of the feature properties.
| 0.641981 |
1. A method of updating speech recognition neural networks, the method comprising: receiving a first audio signal comprising a first speech utterance; performing speech recognition on the first audio signal based at least in part on an acoustic model neural network to obtain a lattice of speech recognition results, wherein the lattice comprises a first path associated with a first score a second path associate with a second score; updating first weights of the acoustic model neural network substantially in real time, wherein updating the first weights comprises performing a first update using information associated with the first path and performing a second update using information associated with the second path; receiving a second audio signal comprising a second speech utterance; and performing speech recognition on the second audio signal based at least in part on the acoustic model neural network and the updated first weights.
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1. A method of updating speech recognition neural networks, the method comprising: receiving a first audio signal comprising a first speech utterance; performing speech recognition on the first audio signal based at least in part on an acoustic model neural network to obtain a lattice of speech recognition results, wherein the lattice comprises a first path associated with a first score a second path associate with a second score; updating first weights of the acoustic model neural network substantially in real time, wherein updating the first weights comprises performing a first update using information associated with the first path and performing a second update using information associated with the second path; receiving a second audio signal comprising a second speech utterance; and performing speech recognition on the second audio signal based at least in part on the acoustic model neural network and the updated first weights. 3. The method of claim 1 , further comprising: computing a feature vector from the first audio signal; determining a hidden Markov model state associated with the feature vector from the first path of the lattice of speech recognition results; and wherein updating the first weights of the acoustic model neural network comprises using the feature vector as an input to the acoustic model neural network and the hidden Markov model state as an output to the acoustic model neural network.
| 0.581444 |
18. A method for transmitting code for extracting contact data from quotes in a multi-tenant database system on a transmission medium, the method comprising: transmitting code to obtain and store a data string having a plurality of tokens in content of a search result from a search for quoted material associated with a contact; transmitting code to extract a sequence of tokens corresponding to the data string; transmitting code to recognize a first set of tokens in the sequence of tokens as a first entity based on entity recognition probabilistic scoring derived from a machine evaluation of a training set of entities; transmitting code to recognize a second set of tokens in the sequence of tokens as a second entity based on identifying the first entity as a first node in a tree-like structure and identifying the second entity as by a second node in the tree-like structure, the first node connected to the second node by an arc representing a probability that the first entity is followed by the second entity in a probable entity sequence, the first node connected to another node by another arc representing another probability that the first entity is followed b another entity in another probable entity sequence, the tree-like structure created by a machine evaluation of a training set of input strings; transmitting code to align one or more tokens of the first set of tokens as one of a plurality of probable entities using the probabilistic scoring of the first set of tokens and grammatical rules; transmitting code to assign the aligned one or more tokens to one entity field of the plurality of predetermined entity fields of the data set; and transmitting code to create and store a new record for the data set if none exists, or updating an existing record for the data set, using the assigned aligned one or more tokens.
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18. A method for transmitting code for extracting contact data from quotes in a multi-tenant database system on a transmission medium, the method comprising: transmitting code to obtain and store a data string having a plurality of tokens in content of a search result from a search for quoted material associated with a contact; transmitting code to extract a sequence of tokens corresponding to the data string; transmitting code to recognize a first set of tokens in the sequence of tokens as a first entity based on entity recognition probabilistic scoring derived from a machine evaluation of a training set of entities; transmitting code to recognize a second set of tokens in the sequence of tokens as a second entity based on identifying the first entity as a first node in a tree-like structure and identifying the second entity as by a second node in the tree-like structure, the first node connected to the second node by an arc representing a probability that the first entity is followed by the second entity in a probable entity sequence, the first node connected to another node by another arc representing another probability that the first entity is followed b another entity in another probable entity sequence, the tree-like structure created by a machine evaluation of a training set of input strings; transmitting code to align one or more tokens of the first set of tokens as one of a plurality of probable entities using the probabilistic scoring of the first set of tokens and grammatical rules; transmitting code to assign the aligned one or more tokens to one entity field of the plurality of predetermined entity fields of the data set; and transmitting code to create and store a new record for the data set if none exists, or updating an existing record for the data set, using the assigned aligned one or more tokens. 19. The method of claim 18 , wherein the probabilistic scoring is learned from training sets of input strings.
| 0.616777 |
1. A method for bridging data distributed service (DDS) domains, comprising: (a) in a networked system having a first DDS domain representing a first communication data space in which only publishers and subscribers of said first DDS domain interact and a second DDS domain, representing a second communication data space in which only publishers and subscribers of said second DDS domain interact, wherein each of said DDS domain comprise a plurality of DDS software applications each running on a computer platform in an independent and distributed manner across said networked system, and wherein each of said DDS software applications publishes data and subscribes to data; and (b) having a DDS domain bridge executable as a software application or a software library on a computer system, said DDS domain bridge is a direct communication coupling bridge for DDS data between said first communication data space formed said second DDS domain, said DDS domain bridge communicatively coupled with said DDS software applications of both said DDS domains and monitoring discovery data by said DDS software applications for atopic name, a topic type, QoS properties or any combination thereof, wherein said DDS domain bridge comprises a plurality of bridge domain rules based on said topic name, said topic type, said QoS properties or any combination thereof organized as creation rules and enabling rules, wherein said creation rules control the creation of input DDS dataflow objects and output DDS dataflow objects for said DDS data, wherein said enabling rules control the enabling state of said input and output DDS dataflow objects for said DDS data, and when said input and output DDS dataflow objects are both in said enabling state set by said enabling rules a DDS dataflow is established between said input DDS dataflow objects and said output DDS dataflow object, wherein said DDS dataflow enables data propagation from one or more of said DDS software applications in said first DDS domain through said enabled input DDS dataflow objects subscribing to said published data to one or more of said DDS software applications in said second DDS domain through said enabled output DDS dataflow objects publishing said received data.
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1. A method for bridging data distributed service (DDS) domains, comprising: (a) in a networked system having a first DDS domain representing a first communication data space in which only publishers and subscribers of said first DDS domain interact and a second DDS domain, representing a second communication data space in which only publishers and subscribers of said second DDS domain interact, wherein each of said DDS domain comprise a plurality of DDS software applications each running on a computer platform in an independent and distributed manner across said networked system, and wherein each of said DDS software applications publishes data and subscribes to data; and (b) having a DDS domain bridge executable as a software application or a software library on a computer system, said DDS domain bridge is a direct communication coupling bridge for DDS data between said first communication data space formed said second DDS domain, said DDS domain bridge communicatively coupled with said DDS software applications of both said DDS domains and monitoring discovery data by said DDS software applications for atopic name, a topic type, QoS properties or any combination thereof, wherein said DDS domain bridge comprises a plurality of bridge domain rules based on said topic name, said topic type, said QoS properties or any combination thereof organized as creation rules and enabling rules, wherein said creation rules control the creation of input DDS dataflow objects and output DDS dataflow objects for said DDS data, wherein said enabling rules control the enabling state of said input and output DDS dataflow objects for said DDS data, and when said input and output DDS dataflow objects are both in said enabling state set by said enabling rules a DDS dataflow is established between said input DDS dataflow objects and said output DDS dataflow object, wherein said DDS dataflow enables data propagation from one or more of said DDS software applications in said first DDS domain through said enabled input DDS dataflow objects subscribing to said published data to one or more of said DDS software applications in said second DDS domain through said enabled output DDS dataflow objects publishing said received data. 3. The method as set forth in claim 1 , wherein said DDS domain bridge is configured to prevent showing, publishing or writing classified data.
| 0.52569 |
1. An Extensible Markup Language (XML) application processor module that receives an XML character stream, which processor module comprises: an XML interface module, a parallel bit stream processor module which includes a processor equipped with parallel processing instructions, a lexical item stream module, a parser and a parsed data receiver wherein: the XML interface module applies the character stream as input to the parallel bit stream processor module and to the parser; the parallel bit stream processor module, in response to the character stream: (a) using the parallel processing instructions, forms a plurality of parallel property bit streams Pj wherein each of the parallel property bit streams consists of a stream of bit values Pj(i) such that Pj(i) is a property associated with code unit C(i) of the character stream and each parallel processing instruction produces a plurality of bit values Pj(i); (b) segments the parallel property bit streams into blocks, each block consisting of bit values Pj(i) at a multiplicity of positions i; and (c) applies the blocks as input to the lexical item stream module; the lexical item stream module processes the blocks to form lexical item streams marking predetermined XML lexical items and applies the lexical item streams as input to the parser; the parser, in response to the lexical item streams and the character stream, forms a stream of parsed XML data and applies the stream of parsed XML data as input to the parsed data receiver; and the parsed data receiver processes the stream of parsed XML data.
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1. An Extensible Markup Language (XML) application processor module that receives an XML character stream, which processor module comprises: an XML interface module, a parallel bit stream processor module which includes a processor equipped with parallel processing instructions, a lexical item stream module, a parser and a parsed data receiver wherein: the XML interface module applies the character stream as input to the parallel bit stream processor module and to the parser; the parallel bit stream processor module, in response to the character stream: (a) using the parallel processing instructions, forms a plurality of parallel property bit streams Pj wherein each of the parallel property bit streams consists of a stream of bit values Pj(i) such that Pj(i) is a property associated with code unit C(i) of the character stream and each parallel processing instruction produces a plurality of bit values Pj(i); (b) segments the parallel property bit streams into blocks, each block consisting of bit values Pj(i) at a multiplicity of positions i; and (c) applies the blocks as input to the lexical item stream module; the lexical item stream module processes the blocks to form lexical item streams marking predetermined XML lexical items and applies the lexical item streams as input to the parser; the parser, in response to the lexical item streams and the character stream, forms a stream of parsed XML data and applies the stream of parsed XML data as input to the parsed data receiver; and the parsed data receiver processes the stream of parsed XML data. 6. The Extensible Markup Language (XML) application processor module of claim 1 wherein the parsed data receiver is a textbase acquisition system.
| 0.678753 |
8. A computer-implemented method comprising: accessing a learning schedule indicative of when, for each of a plurality of learning episodes, content corresponding to the learning episode is to be presented via an electronic app; adjusting, using one or more processors, the learning schedule based on a past performance of a user, a past performance of a group of other users, a target performance time, and a target performance metric, the adjusting including changing a number of learning episodes included in the plurality of learning episodes; automatically identifying, using the one or more processors, a presentation time for an episode of the plurality of learning episodes based on the adjusted learning schedule; and displaying, at a device of the user, an interface that includes an electronic notification at the presentation time, the electronic notification including one or more elements configured to receive input corresponding to a request to access an electronic content object associated with the episode, wherein the electronic content object is displayed at the device of the user upon detecting that the one or more elements have received input corresponding to the request.
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8. A computer-implemented method comprising: accessing a learning schedule indicative of when, for each of a plurality of learning episodes, content corresponding to the learning episode is to be presented via an electronic app; adjusting, using one or more processors, the learning schedule based on a past performance of a user, a past performance of a group of other users, a target performance time, and a target performance metric, the adjusting including changing a number of learning episodes included in the plurality of learning episodes; automatically identifying, using the one or more processors, a presentation time for an episode of the plurality of learning episodes based on the adjusted learning schedule; and displaying, at a device of the user, an interface that includes an electronic notification at the presentation time, the electronic notification including one or more elements configured to receive input corresponding to a request to access an electronic content object associated with the episode, wherein the electronic content object is displayed at the device of the user upon detecting that the one or more elements have received input corresponding to the request. 11. The computer-implemented method as recited in claim 8 , further comprising: determining the past performance of the user based on one or more first inputs associated with the user and received via a first interface; and determining the past performance of the group of other users based on one or more second inputs associated with the group of other users and received via one or more second interfaces.
| 0.800876 |
1. A method for detecting speech utterances within a telephone call comprising the steps of: receiving a signal representing a telephone call received over a telephone network by a telephone gateway; initializing a componentized voice server having an internal speech detection module with a plurality of software-based speech detection routines and a Pluggable, configurable external speech detection component operationally located remotely from the voice server, wherein the external speech detection component is implemented as an electronic module plugged into a piece of equipment coupled in a signal path between the telephone network and the voice server; presenting through a user interface options for speech detection settings and receiving through the user interface user selections indicating speech detection parameters, wherein the speech detection parameters determine whether the internal speech detection module, the external speech detection component or both the internal speech detection module and the external speech detection component will be activated; when the received speech detection parameters indicate that the external speech detection component will be activated: sending a message from the voice server to the external speech detection component to activate said external speech detection component; processing the received signal to detect a speech utterance within the signal using the activated external speech detection component; sending a message from the external speech detection component to the voice server conveying results of detecting a speech utterance; and performing with said voice server at least one programmatic action responsive to the detecting of the speech utterance, the programmatic action comprising recognizing speech in the detected speech utterance; and when the received speech detection parameters indicate that both the internal speech detection module and the external speech detection component will be activated: sending a message from the voice server to the external speech detection component to activate said external speech detection component; processing the received signal using the activated external speech detection component; sending a message from the external speech detection component to the voice server conveying results of an attempt to detect a speech utterance; and performing with said voice server at least one programmatic action, the programmatic action comprising using the internal speech detection module conjunctively with the results of the attempt to detect the speech utterance in the external speech detection component to detect the speech utterance in the received signal; and when the received speech detection parameters indicate that the internal speech detection module will be activated: processing the received signal to detect a speech utterance within the signal using the internal speech detection module; and performing with said voice server at least one programmatic action responsive to the detecting of the speech utterance.
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1. A method for detecting speech utterances within a telephone call comprising the steps of: receiving a signal representing a telephone call received over a telephone network by a telephone gateway; initializing a componentized voice server having an internal speech detection module with a plurality of software-based speech detection routines and a Pluggable, configurable external speech detection component operationally located remotely from the voice server, wherein the external speech detection component is implemented as an electronic module plugged into a piece of equipment coupled in a signal path between the telephone network and the voice server; presenting through a user interface options for speech detection settings and receiving through the user interface user selections indicating speech detection parameters, wherein the speech detection parameters determine whether the internal speech detection module, the external speech detection component or both the internal speech detection module and the external speech detection component will be activated; when the received speech detection parameters indicate that the external speech detection component will be activated: sending a message from the voice server to the external speech detection component to activate said external speech detection component; processing the received signal to detect a speech utterance within the signal using the activated external speech detection component; sending a message from the external speech detection component to the voice server conveying results of detecting a speech utterance; and performing with said voice server at least one programmatic action responsive to the detecting of the speech utterance, the programmatic action comprising recognizing speech in the detected speech utterance; and when the received speech detection parameters indicate that both the internal speech detection module and the external speech detection component will be activated: sending a message from the voice server to the external speech detection component to activate said external speech detection component; processing the received signal using the activated external speech detection component; sending a message from the external speech detection component to the voice server conveying results of an attempt to detect a speech utterance; and performing with said voice server at least one programmatic action, the programmatic action comprising using the internal speech detection module conjunctively with the results of the attempt to detect the speech utterance in the external speech detection component to detect the speech utterance in the received signal; and when the received speech detection parameters indicate that the internal speech detection module will be activated: processing the received signal to detect a speech utterance within the signal using the internal speech detection module; and performing with said voice server at least one programmatic action responsive to the detecting of the speech utterance. 2. The method of claim 1 , wherein both of said internal speech detection module and said external speech detection component technique are utilized simultaneously.
| 0.58975 |
7. An article of manufacture comprising a computer-readable medium storing a computer program that, when executed by at least one processor, causes the at least one processor to perform a method for voice enabling a Web page, the method comprising: receiving speech input for an input field in the Web page; determining whether the input field is a free form input field; and performing a plurality of first actions in response to determining that the input field is not a free form input field, wherein the plurality of first actions includes: generating a speech grammar for the input field based upon terms associated with the input field, wherein the terms associated with the input field comprise terms in a hidden title attribute of the input field, and wherein generating the speech grammar comprises generating the speech grammar for the input field based upon the terms in the hidden title attribute; providing the received speech input and the generated speech grammar to an ASR engine configured to recognize the received speech input to produce a first textual equivalent to the received speech input using the generated speech grammar; and inserting the first textual equivalent into the input field.
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7. An article of manufacture comprising a computer-readable medium storing a computer program that, when executed by at least one processor, causes the at least one processor to perform a method for voice enabling a Web page, the method comprising: receiving speech input for an input field in the Web page; determining whether the input field is a free form input field; and performing a plurality of first actions in response to determining that the input field is not a free form input field, wherein the plurality of first actions includes: generating a speech grammar for the input field based upon terms associated with the input field, wherein the terms associated with the input field comprise terms in a hidden title attribute of the input field, and wherein generating the speech grammar comprises generating the speech grammar for the input field based upon the terms in the hidden title attribute; providing the received speech input and the generated speech grammar to an ASR engine configured to recognize the received speech input to produce a first textual equivalent to the received speech input using the generated speech grammar; and inserting the first textual equivalent into the input field. 11. The article of manufacture of claim 7 , wherein the terms associated with the input field are in the Web page.
| 0.758052 |
1. A method comprising the steps of: requesting, through a first service call, creation of a data structure describing a server virtual private network (VPN) gateway; receiving, in response to the first service call, a handle for the server VPN gateway; requesting, through a second service call, creation of a data structure describing a client VPN gateway; receiving, in response to the second service call, a handle for the client VPN gateway; requesting, through a third service call, creation of a VPN that includes the client VPN gateway and the server VPN gateway; receiving, in response to the third service call, a generic gateway configuration document; translating the generic gateway configuration document to a device-specific gateway configuration document; and applying the device-specific gateway configuration document to the client VPN gateway.
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1. A method comprising the steps of: requesting, through a first service call, creation of a data structure describing a server virtual private network (VPN) gateway; receiving, in response to the first service call, a handle for the server VPN gateway; requesting, through a second service call, creation of a data structure describing a client VPN gateway; receiving, in response to the second service call, a handle for the client VPN gateway; requesting, through a third service call, creation of a VPN that includes the client VPN gateway and the server VPN gateway; receiving, in response to the third service call, a generic gateway configuration document; translating the generic gateway configuration document to a device-specific gateway configuration document; and applying the device-specific gateway configuration document to the client VPN gateway. 2. The method of claim 1 , wherein the generic gateway document is an Extensible Markup Language (XML) document.
| 0.510771 |
4. The method of claim 1 , wherein, when input messages for a query can be processed independent of each other, the installing step includes installing identical logic for processing such query on two or more servers in the cluster, and the input messages are divided up among the servers having the logic.
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4. The method of claim 1 , wherein, when input messages for a query can be processed independent of each other, the installing step includes installing identical logic for processing such query on two or more servers in the cluster, and the input messages are divided up among the servers having the logic. 6. The method of claim 4 , where the input messages are divided up randomly.
| 0.907713 |
1. A non-transitory article of manufacture comprising computer readable instructions stored thereon which when executed by a processor cause a computing environment to: receive, from a remote computer system, a data stream containing information from at least one instance of a data object with a structure unknown to the computing environment, the data stream comprising: a header, wherein the header of the data stream includes metadata describing one or more structure elements of the data object, and a body, wherein the body of the data stream includes the information from the at least one instance of the data object; extract the information from the at least one instance of the data object from the body of the data stream in accordance with the one or more structure elements described in the metadata; and dynamically create a user interface (UI) based on the one or more structure elements of the data object, wherein the UI includes a first area to show the one or more structure elements based on a description in the metadata, a second area to present information from the at least one instance of the data object corresponding to a selected element from the one or more structure elements, and a UI control mechanism to allow a user to select the element of the one or more structure elements, and to change a structure of the selected element or the information from the at least one instance of the data object corresponding to the selected element.
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1. A non-transitory article of manufacture comprising computer readable instructions stored thereon which when executed by a processor cause a computing environment to: receive, from a remote computer system, a data stream containing information from at least one instance of a data object with a structure unknown to the computing environment, the data stream comprising: a header, wherein the header of the data stream includes metadata describing one or more structure elements of the data object, and a body, wherein the body of the data stream includes the information from the at least one instance of the data object; extract the information from the at least one instance of the data object from the body of the data stream in accordance with the one or more structure elements described in the metadata; and dynamically create a user interface (UI) based on the one or more structure elements of the data object, wherein the UI includes a first area to show the one or more structure elements based on a description in the metadata, a second area to present information from the at least one instance of the data object corresponding to a selected element from the one or more structure elements, and a UI control mechanism to allow a user to select the element of the one or more structure elements, and to change a structure of the selected element or the information from the at least one instance of the data object corresponding to the selected element. 3. The article of manufacture of claim 1 , comprising further computer readable instructions stored thereon which when executed by the processor cause the computing environment to: receive supplementary information in the header of the data stream related to the data object, wherein the supplementary information is selected from a group consisting of: a default value of a property of the data object; a start index indicating a position of an instance in an order of instances of the data object persisted in a computer system, wherein information from the instance with the start index position is included into the body of the data stream; a number of instances of the data object persisted in the computer system from which information is included in the body of the data stream; and a total number of instances of the data object persisted in the computer system.
| 0.5 |
1. An enterprise level information networking system implemented using a database system, the enterprise level information networking system comprising: database system software stored on a non-transitory data storage medium for execution by at least one computing device of the enterprise level information networking system, the database system software operable to cause: processing, using the database system, a request to share a first rights-managed file with at least one user, the first rights-managed file being stored as one of a plurality of rights-managed files in a library as securable data objects in the database system, the library capable of restricting access of the at least one user to the first rights-managed file according to a library permission policy, the library permission policy configured to control authorization of offline actions regarding the first rights-managed file and of online actions regarding the first rights-managed file by users in relation to interacting with the rights-managed files stored in the library; determining, using the database system, that the request complies with a plurality of access rights, the access rights comprising at least one access right associated with the library permission policy, the at least one access right associated with the library permission policy configured to provide an identical level of access to each of the rights-managed files stored in the library; and storing or updating, using the database system, a data object in the database system to associate the at least one user as having access to the requested rights-managed file according to the access rights.
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1. An enterprise level information networking system implemented using a database system, the enterprise level information networking system comprising: database system software stored on a non-transitory data storage medium for execution by at least one computing device of the enterprise level information networking system, the database system software operable to cause: processing, using the database system, a request to share a first rights-managed file with at least one user, the first rights-managed file being stored as one of a plurality of rights-managed files in a library as securable data objects in the database system, the library capable of restricting access of the at least one user to the first rights-managed file according to a library permission policy, the library permission policy configured to control authorization of offline actions regarding the first rights-managed file and of online actions regarding the first rights-managed file by users in relation to interacting with the rights-managed files stored in the library; determining, using the database system, that the request complies with a plurality of access rights, the access rights comprising at least one access right associated with the library permission policy, the at least one access right associated with the library permission policy configured to provide an identical level of access to each of the rights-managed files stored in the library; and storing or updating, using the database system, a data object in the database system to associate the at least one user as having access to the requested rights-managed file according to the access rights. 5. The system of claim 1 , wherein the at least one user comprises a group of one or more users; and wherein the at least one access right associated with the library permission policy identify each user of the group.
| 0.536383 |
1. A computer implemented method for managing content assets for licensing, comprising: storing, by a server computer, a plurality of content assets; assigning, by a server computer, each content asset to a collection, wherein a collection includes a plurality of content assets within a single element of a classification matrix, wherein each column of a classification matrix corresponds to one of a plurality of license models and each row of a classification matrix corresponds to one of a plurality of value tiers, wherein a license model specifies a set of usage restrictions on a license to a content asset and a value tier designates a relative level of quality and corresponds to a plurality of license prices, wherein license prices for higher quality value tiers are generally higher than license prices for relatively lower quality value tiers; receiving by the server computer, from a client computer, a specification of a usage requirement and a price range; identifying, by the server computer, the collections associated with the specified usage requirement and price range; and providing, by the server computer, to the client interface a list of the identified collections.
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1. A computer implemented method for managing content assets for licensing, comprising: storing, by a server computer, a plurality of content assets; assigning, by a server computer, each content asset to a collection, wherein a collection includes a plurality of content assets within a single element of a classification matrix, wherein each column of a classification matrix corresponds to one of a plurality of license models and each row of a classification matrix corresponds to one of a plurality of value tiers, wherein a license model specifies a set of usage restrictions on a license to a content asset and a value tier designates a relative level of quality and corresponds to a plurality of license prices, wherein license prices for higher quality value tiers are generally higher than license prices for relatively lower quality value tiers; receiving by the server computer, from a client computer, a specification of a usage requirement and a price range; identifying, by the server computer, the collections associated with the specified usage requirement and price range; and providing, by the server computer, to the client interface a list of the identified collections. 3. The method of claim 1 , wherein the content assets have keywords associated therewith, the method further comprising: receiving from the client computer at least one keyword and a selection of at least one collection from the identified collections; determining, by the server computer which of the plurality of content assets are associated with the at least one received keyword and with the selected at least one collection; and providing, to the client computer, a list of the determined content assets.
| 0.718637 |
15. The method of claim 1 further comprising, after the playing back the second audio clip, but before the playing back the first menu audio clip: playing back a third audio clip to indicate an end of the announcing of the first audio clip.
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15. The method of claim 1 further comprising, after the playing back the second audio clip, but before the playing back the first menu audio clip: playing back a third audio clip to indicate an end of the announcing of the first audio clip. 16. The method of claim 15 , wherein the third audio clip that is played back comprises at least one of a tone and a beep.
| 0.906732 |
2. The method of providing parameterized queries in CEP as in claim 1 , further comprising building a map of the determined positions of the one or more bind variables.
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2. The method of providing parameterized queries in CEP as in claim 1 , further comprising building a map of the determined positions of the one or more bind variables. 3. The method of providing parameterized queries in CEP as in claim 2 , wherein the substituting of the one or more bind variables with the corresponding sets of parameters further comprises using the map of determined positions of the one or more bind variables to place the bound sets of parameters within the query template.
| 0.916113 |
7. A system for providing recommendations to improve a query, comprising: a processor; and a matching engine coupled to the processor and performing operations, the operations comprising: receiving, from a user via a user interface, a query with query keywords and selected categories; calculating a query relevance indicator for each of the selected categories, wherein the query relevance indicator for a category of the selected categories is calculated based on a keyword relevance indicator of each keyword specified in the query, a total number of keywords in the query, a keyword relevance indicator of each keyword in the category that is not specified in the query, and a total number of different keywords in the category and the query; and in response to determining that the selected categories are ranked high with reference to the query relevance indicator for each of the selected categories, calculating a query relevance indicator for each subcategory, wherein the query relevance indicator for a subcategory is calculated based on the keyword relevance indicator of each keyword specified in the query, the total number of keywords in the query, a keyword relevance indicator of each keyword in the subcategory that is not specified in the query, and a total number of different keywords in the subcategory and the query; ranking each subcategory based on the query relevance indicator of the query with each subcategory; and in response to determining that high-ranked subcategories are not in the selected categories, providing recommendations of one or more new query keywords and the ranked subcategories for use in selecting new categories to be submitted with the query; and in response to receiving a new query using at least one of the one or more new query keywords, executing the new query to identify services.
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7. A system for providing recommendations to improve a query, comprising: a processor; and a matching engine coupled to the processor and performing operations, the operations comprising: receiving, from a user via a user interface, a query with query keywords and selected categories; calculating a query relevance indicator for each of the selected categories, wherein the query relevance indicator for a category of the selected categories is calculated based on a keyword relevance indicator of each keyword specified in the query, a total number of keywords in the query, a keyword relevance indicator of each keyword in the category that is not specified in the query, and a total number of different keywords in the category and the query; and in response to determining that the selected categories are ranked high with reference to the query relevance indicator for each of the selected categories, calculating a query relevance indicator for each subcategory, wherein the query relevance indicator for a subcategory is calculated based on the keyword relevance indicator of each keyword specified in the query, the total number of keywords in the query, a keyword relevance indicator of each keyword in the subcategory that is not specified in the query, and a total number of different keywords in the subcategory and the query; ranking each subcategory based on the query relevance indicator of the query with each subcategory; and in response to determining that high-ranked subcategories are not in the selected categories, providing recommendations of one or more new query keywords and the ranked subcategories for use in selecting new categories to be submitted with the query; and in response to receiving a new query using at least one of the one or more new query keywords, executing the new query to identify services. 12. The system of claim 7 , wherein the operations further comprise: executing the query in a current form; and providing a list of one or more services.
| 0.649814 |
1. A method comprising: providing a parallel programming interface comprising multidimensional data types and a set of parallel operations; receiving at least one first parallel processing request at the parallel programming interface, the at least one first parallel processing request comprising at least one evaluation request of one or more parallel operations on one or more input arrays; building an expression graph in response to receiving the at least one first parallel processing request, the expression graph comprising nodes representing parallel processing operations; and evaluating the one or more parallel operations in response to determining that a second parallel processing request received at the parallel programming interface subsequent to receiving the at least one first parallel processing request at the parallel programming interface requires evaluation of at least one of the one or more parallel operations, the determining occurring in response to receiving the second parallel processing request at the parallel programming interface, the evaluating comprising: creating one or more shader programs formed according to resource constraints of a graphics environment; the creating comprising: determining that an output texture from a child node comprises an inconsistent size constraint with an input texture of a parent node of the child node; and breaking the child node into a separate shader program responsive to determining the inconsistent size constraint; receiving an output responsive to invoking the one or more shader programs on a graphics processor; and providing the output at the parallel programming interface; wherein the method is implemented by a computer; and wherein the at least one first parallel processing request and the second parallel processing request are received from an application program executing on the computer.
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1. A method comprising: providing a parallel programming interface comprising multidimensional data types and a set of parallel operations; receiving at least one first parallel processing request at the parallel programming interface, the at least one first parallel processing request comprising at least one evaluation request of one or more parallel operations on one or more input arrays; building an expression graph in response to receiving the at least one first parallel processing request, the expression graph comprising nodes representing parallel processing operations; and evaluating the one or more parallel operations in response to determining that a second parallel processing request received at the parallel programming interface subsequent to receiving the at least one first parallel processing request at the parallel programming interface requires evaluation of at least one of the one or more parallel operations, the determining occurring in response to receiving the second parallel processing request at the parallel programming interface, the evaluating comprising: creating one or more shader programs formed according to resource constraints of a graphics environment; the creating comprising: determining that an output texture from a child node comprises an inconsistent size constraint with an input texture of a parent node of the child node; and breaking the child node into a separate shader program responsive to determining the inconsistent size constraint; receiving an output responsive to invoking the one or more shader programs on a graphics processor; and providing the output at the parallel programming interface; wherein the method is implemented by a computer; and wherein the at least one first parallel processing request and the second parallel processing request are received from an application program executing on the computer. 8. The method of claim 1 , wherein creation of at least one of the one or more shader programs begins prior to determining that the second parallel processing request received at the parallel programming interface subsequent to receiving the at least one first parallel processing request at the parallel programming interfaces requires evaluation of at least one of the one or more parallel operations.
| 0.693009 |
13. A non-transitory computer readable medium storing instructions, the instructions comprising: one or more instructions which, when executed by at least one processor, cause the at least one processor to: identify one or more particular terms, of a plurality of terms, in a search query; form, based on identifying the one or more particular terms, another search query; obtain first context data that includes a first set of documents returned for the search query and second context data that includes a second set of documents returned for the other search query; compare information associated with the first set of documents returned for the search query and information associated with the second set of documents returned for the other search query; determine, based on the comparing, that the first context data and the second context data are different; and store, based on the determining, information indicating that the search query and the other search query are associated with different context data.
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13. A non-transitory computer readable medium storing instructions, the instructions comprising: one or more instructions which, when executed by at least one processor, cause the at least one processor to: identify one or more particular terms, of a plurality of terms, in a search query; form, based on identifying the one or more particular terms, another search query; obtain first context data that includes a first set of documents returned for the search query and second context data that includes a second set of documents returned for the other search query; compare information associated with the first set of documents returned for the search query and information associated with the second set of documents returned for the other search query; determine, based on the comparing, that the first context data and the second context data are different; and store, based on the determining, information indicating that the search query and the other search query are associated with different context data. 16. The non-transitory computer readable medium of claim 13 , where the instructions further include: one or more instructions to identify, based on determining that the first context data and the second context data are different, a particular term, of the one or more particular terms, that causes the first context data to be different than the second context data.
| 0.538655 |
11. The method of claim 1 , wherein the method of using the created hierarchy further comprises selecting root tags in the created hierarchy to be included in the tag cloud.
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11. The method of claim 1 , wherein the method of using the created hierarchy further comprises selecting root tags in the created hierarchy to be included in the tag cloud. 12. The method of claim 11 , wherein the step of using the created hierarchy further comprises including most popular non-root tags to be included in the tag cloud after including all the root tags.
| 0.940605 |
39. The computer program of claim 23, further comprising instructions to score the blocks of text against a language model for a topic of interest.
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39. The computer program of claim 23, further comprising instructions to score the blocks of text against a language model for a topic of interest. 40. The computer program of claim 39, further comprising instructions to identify segments that correspond to the language model for the topic of interest as corresponding to the topic of interest.
| 0.907925 |
15. The computer-implemented method of claim 10 , wherein the confidence threshold comprises a set of confidence thresholds, the set of confidence thresholds including a first confidence threshold and at least one subsequent confidence threshold that is lower than the first confidence threshold; and the method further comprises: determining that none of the text data is associated with the respective confidence level that exceeds the first confidence threshold; and determining whether any text data is associated with the respective confidence level that exceeds the at least one subsequent confidence threshold.
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15. The computer-implemented method of claim 10 , wherein the confidence threshold comprises a set of confidence thresholds, the set of confidence thresholds including a first confidence threshold and at least one subsequent confidence threshold that is lower than the first confidence threshold; and the method further comprises: determining that none of the text data is associated with the respective confidence level that exceeds the first confidence threshold; and determining whether any text data is associated with the respective confidence level that exceeds the at least one subsequent confidence threshold. 16. The computer-implemented method of claim 15 further comprises: determining that none of the text data is associated with the respective confidence level that exceeds the at least one subsequent confidence threshold; and indicating additional processing is required to translate the raw audio data.
| 0.859773 |
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