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1. One or more non-transitory computer-readable media storing computer-executable instructions that, when executed, cause one or more processors to perform acts comprising: identifying a first set of phrases that exist within a corpus of text, individual ones of the first set of phrases including a substitute word that replaces one word of a source phrase comprising multiple words; generating a second set of phrases by combining substitute words from the first set of phrases with one another such that individual ones of the second set of phrases include a first substitute word from a first phrase of the first set of phrases and a second substitute word from a second phrase of the first set of phrases; generating a personalized user interface including at least a portion of the second set of phrases and at least one rule selector associated with a corresponding rule for using the at least the portion of the second set of phrases; and receiving a user selection of the at least the portion of the second set of phrases and the at least one rule selector associated with the corresponding rule for using the at least the portion of the second set of phrases.
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1. One or more non-transitory computer-readable media storing computer-executable instructions that, when executed, cause one or more processors to perform acts comprising: identifying a first set of phrases that exist within a corpus of text, individual ones of the first set of phrases including a substitute word that replaces one word of a source phrase comprising multiple words; generating a second set of phrases by combining substitute words from the first set of phrases with one another such that individual ones of the second set of phrases include a first substitute word from a first phrase of the first set of phrases and a second substitute word from a second phrase of the first set of phrases; generating a personalized user interface including at least a portion of the second set of phrases and at least one rule selector associated with a corresponding rule for using the at least the portion of the second set of phrases; and receiving a user selection of the at least the portion of the second set of phrases and the at least one rule selector associated with the corresponding rule for using the at least the portion of the second set of phrases. 6. One or more non-transitory computer-readable media as recited in claim 1 , further storing computer-executable instructions that, when executed, cause the one or more processors to perform acts comprising associating the user selection of the at least the portion of the second set of phrases and the at least one rule selector associated with the corresponding rule for using the at least the portion of the second set of phrases with an aspect of a user account of a user.
| 0.696262 |
1. A method implemented by a system of one or more computers, comprising: receiving, by the system of one or more computers, speech utterances encoded in audio data and a transcript having text that represents the speech utterances; extracting, by the system of one or more computers, prosodic contours from the utterances; extracting, by the system of one or more computers and from the transcript, attributes of text associated with the utterances; for pairs of utterances from the speech utterances, determining, by the system of one or more computers, distances between attributes of text associated with the pairs of utterances; for the pairs of utterances from the speech utterances, determining, by the system of one or more computers, distances between prosodic contours for the pairs of utterances; generating, by the system of one or more computers, a model based on the determined distances for the attributes and the prosodic contours, the model adapted to estimate a distance between a determined prosodic contour for a received utterance and a prosodic contour for a synthesized utterance when given a distance between an attribute of text associated with the received utterance and an attribute of text associated with the synthesized utterance; and storing, by the system of one or more computers, the model in a computer-readable memory device.
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1. A method implemented by a system of one or more computers, comprising: receiving, by the system of one or more computers, speech utterances encoded in audio data and a transcript having text that represents the speech utterances; extracting, by the system of one or more computers, prosodic contours from the utterances; extracting, by the system of one or more computers and from the transcript, attributes of text associated with the utterances; for pairs of utterances from the speech utterances, determining, by the system of one or more computers, distances between attributes of text associated with the pairs of utterances; for the pairs of utterances from the speech utterances, determining, by the system of one or more computers, distances between prosodic contours for the pairs of utterances; generating, by the system of one or more computers, a model based on the determined distances for the attributes and the prosodic contours, the model adapted to estimate a distance between a determined prosodic contour for a received utterance and a prosodic contour for a synthesized utterance when given a distance between an attribute of text associated with the received utterance and an attribute of text associated with the synthesized utterance; and storing, by the system of one or more computers, the model in a computer-readable memory device. 10. The method of claim 1 , further comprising selecting pairs of utterances for use in determining distances based on whether the utterances have canonical stress patterns that match.
| 0.571385 |
1. A computer-implemented method, comprising: receiving, by a computing system, text of a message entered by a user into a communication application program, wherein the text represents typed or audibly spoken content input by the user; determining, by a computing system, a level of randomness of characters in a portion of the text; identifying a threshold level of randomness from a plurality of different threshold levels of randomness based at least in part on a particular label of a text entry field into which the portion of the text was input; determining, by a computing system, whether the level of randomness of the characters in the portion of the text satisfies the threshold level of randomness; and responsive to determining that the level of randomness of the characters in the portion of the text satisfies the threshold level of randomness, precluding, by a computing system, a text processing system from performing a spell checking procedure on the portion of the text or from performing a word auto complete procedure on the portion of the text.
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1. A computer-implemented method, comprising: receiving, by a computing system, text of a message entered by a user into a communication application program, wherein the text represents typed or audibly spoken content input by the user; determining, by a computing system, a level of randomness of characters in a portion of the text; identifying a threshold level of randomness from a plurality of different threshold levels of randomness based at least in part on a particular label of a text entry field into which the portion of the text was input; determining, by a computing system, whether the level of randomness of the characters in the portion of the text satisfies the threshold level of randomness; and responsive to determining that the level of randomness of the characters in the portion of the text satisfies the threshold level of randomness, precluding, by a computing system, a text processing system from performing a spell checking procedure on the portion of the text or from performing a word auto complete procedure on the portion of the text. 2. The computer-implemented method of claim 1 , further comprising receiving, by a computing system, the input by the user.
| 0.724565 |
1. A method for analyzing user-generated content, comprising: collecting, by a processor, a review of a business from a website; extracting, by the processor, a sentence from the review; identifying, processor and within the sentence, an attribute-value comprising a noun and a descriptive term describing the noun, wherein the descriptive term comprises an adjective and an adverb corresponding to the adjective; obtaining, by the processor, an adjective score for the adjective from a rating system lookup table; assigning, by the processor, a heuristic rule score to the adverb; and calculating, by the processor, a sentence review score for the sentence by summing the adjective score and the heuristic rule score.
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1. A method for analyzing user-generated content, comprising: collecting, by a processor, a review of a business from a website; extracting, by the processor, a sentence from the review; identifying, processor and within the sentence, an attribute-value comprising a noun and a descriptive term describing the noun, wherein the descriptive term comprises an adjective and an adverb corresponding to the adjective; obtaining, by the processor, an adjective score for the adjective from a rating system lookup table; assigning, by the processor, a heuristic rule score to the adverb; and calculating, by the processor, a sentence review score for the sentence by summing the adjective score and the heuristic rule score. 6. The method of claim 1 , further comprising: collecting a plurality of reviews for the business; calculating a frequency of occurrence of the attribute-value within the plurality of reviews; and displaying the attribute-value according to the frequency of occurrence.
| 0.662433 |
1. A method comprising: storing a plurality of multi-language profiles of a plurality of users; identifying one or more multilingual cognates in each profile of the plurality of multi-language profiles; based on the one or more multilingual cognates identified in each profile of the plurality of multi-language profiles, generating one or more translation models; receiving input that indicates a selection, by a second user, of data that is associated with a first user that is different than the second user, wherein the plurality of users includes users other than the second user and the first user; determining a first language that is associated with the first user; determining a second language that is different than the first language and that is associated with the second user; wherein a plurality of data items in a profile of the first user are in the first language; translating the plurality of data items into the second language using the one or more translation models; in response to receiving the input, causing a translated version of the plurality of data items to be displayed to the second user, wherein the translated version is in the second language; wherein the method is performed by one or more computing devices.
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1. A method comprising: storing a plurality of multi-language profiles of a plurality of users; identifying one or more multilingual cognates in each profile of the plurality of multi-language profiles; based on the one or more multilingual cognates identified in each profile of the plurality of multi-language profiles, generating one or more translation models; receiving input that indicates a selection, by a second user, of data that is associated with a first user that is different than the second user, wherein the plurality of users includes users other than the second user and the first user; determining a first language that is associated with the first user; determining a second language that is different than the first language and that is associated with the second user; wherein a plurality of data items in a profile of the first user are in the first language; translating the plurality of data items into the second language using the one or more translation models; in response to receiving the input, causing a translated version of the plurality of data items to be displayed to the second user, wherein the translated version is in the second language; wherein the method is performed by one or more computing devices. 4. The method of claim 1 , further comprising: identifying a first user profile that includes a first plurality of data items in the first language and a second plurality of data items in the second language, wherein the first plurality of data items correspond to the second plurality of data items; identifying a second user profile that is different than the first user profile and that includes a third plurality of data items in the first language and a fourth plurality of data items in the second language, wherein the third plurality of data items correspond to the fourth plurality of data items; generating multilingual cognates based on a correspondence between the first plurality of data items and the second plurality of data items and based on a correspondence between the third plurality of data items and the fourth plurality of data items.
| 0.535279 |
9. Apparatus for providing expressive user interaction with a multimodal application, the multimodal application operating in a multimodal browser on a multimodal device supporting multiple modes of user interaction including a voice mode and one or more non-voice modes, the multimodal application operatively coupled to a speech engine through a VoiceXML interpreter, the apparatus comprising a computer processor and a computer memory operatively coupled to the computer processor, the computer memory having stored within it computer program instructions which, when executed by the computer processor, cause performance of a method comprising: receiving, by the multimodal browser, user input from a user through a particular mode of user interaction; determining, by the multimodal browser, user output for the user in dependence upon the user input; determining, by the multimodal browser, a style for the user output in dependence upon the user input, the style specifying expressive output characteristics for at least one other mode of user interaction; and rendering, by the multimodal browser, the user output in dependence upon the style, wherein determining the style for the user output in dependence upon the user input comprises performing a determination distinct from determining the user output, wherein determining, by the multimodal browser, a style for the user output in dependence upon the user input comprises determining, by the multimodal browser, a style for the user output in dependence upon meaning of the user input.
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9. Apparatus for providing expressive user interaction with a multimodal application, the multimodal application operating in a multimodal browser on a multimodal device supporting multiple modes of user interaction including a voice mode and one or more non-voice modes, the multimodal application operatively coupled to a speech engine through a VoiceXML interpreter, the apparatus comprising a computer processor and a computer memory operatively coupled to the computer processor, the computer memory having stored within it computer program instructions which, when executed by the computer processor, cause performance of a method comprising: receiving, by the multimodal browser, user input from a user through a particular mode of user interaction; determining, by the multimodal browser, user output for the user in dependence upon the user input; determining, by the multimodal browser, a style for the user output in dependence upon the user input, the style specifying expressive output characteristics for at least one other mode of user interaction; and rendering, by the multimodal browser, the user output in dependence upon the style, wherein determining the style for the user output in dependence upon the user input comprises performing a determination distinct from determining the user output, wherein determining, by the multimodal browser, a style for the user output in dependence upon the user input comprises determining, by the multimodal browser, a style for the user output in dependence upon meaning of the user input. 11. The apparatus of claim 9 wherein: receiving, by the multimodal browser, user input from the user through the particular mode of user interaction further comprises receiving graphical user input from the user through a graphical user interface; determining, by the multimodal browser, user output for the user in dependence upon the user input further comprises determining the user output for the user in dependence upon the graphical user input; and determining, by the multimodal browser, the style for the user output further comprises determining the style for the user output in dependence upon the graphical user input.
| 0.522968 |
1. A system to recommend content to a user, comprising: a computer; a content of interest to the user stored on the computer; a first semantic abstract representing the content of interest stored on the computer, the first semantic abstract including a first plurality of state vectors; a second semantic abstract representing a second content, the second semantic abstract including a second plurality of state vectors; a semantic abstract comparer to compare the first semantic abstract to the second semantic abstract; and a content recommender to recommend the second content if the first semantic abstract is within a threshold distance of the second semantic abstract, wherein: a dictionary includes a directed set of concepts including a maximal element and directed links between pairs of concepts in the directed set, the directed links defining “is a” relationships between the concepts in the pairs of concepts, so that each concept is either a source or a sink of at least one directed link; for each concept in the directed set other than the maximal element, at least one chain in the directed set includes a set of directed links between pairs of concepts connecting the maximal element and the concept; a basis includes a subset of the chains; and each state vector in the first semantic abstract and the second semantic abstract measures how concretely a concept is represented in each chain in the basis by identifying the smallest predecessor in the chain in relation to the concept.
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1. A system to recommend content to a user, comprising: a computer; a content of interest to the user stored on the computer; a first semantic abstract representing the content of interest stored on the computer, the first semantic abstract including a first plurality of state vectors; a second semantic abstract representing a second content, the second semantic abstract including a second plurality of state vectors; a semantic abstract comparer to compare the first semantic abstract to the second semantic abstract; and a content recommender to recommend the second content if the first semantic abstract is within a threshold distance of the second semantic abstract, wherein: a dictionary includes a directed set of concepts including a maximal element and directed links between pairs of concepts in the directed set, the directed links defining “is a” relationships between the concepts in the pairs of concepts, so that each concept is either a source or a sink of at least one directed link; for each concept in the directed set other than the maximal element, at least one chain in the directed set includes a set of directed links between pairs of concepts connecting the maximal element and the concept; a basis includes a subset of the chains; and each state vector in the first semantic abstract and the second semantic abstract measures how concretely a concept is represented in each chain in the basis by identifying the smallest predecessor in the chain in relation to the concept. 6. A system according to claim 1 , further comprising a content library to store at least one of the content of interest and the second content.
| 0.576858 |
53. A computer storage medium encoded with a computer program, the program comprising instructions that when executed by data processing apparatus cause the data processing apparatus to perform operations comprising: receiving a search query; generating first search results that identify resources that a search engine has identified as being responsive to the search query; identifying one or more search modes based on the search query, the resources, or both the search query and the resources; providing a first user interface that presents for display at least a portion of the first search results and a respective search mode selector for each of one or more of the identified one or more search modes; receiving user input selecting a first search mode by selecting one of the search mode selectors, wherein: the first search mode is associated with a first collection of records, all records in the first collection have a common attribute structure of data elements, the first search results identify resources that the search engine has identified from a corpus of resources as being responsive to the search query, and all records in the corpus do not have the common attribute structure of data elements; generating second search results that satisfy the search query and that refer to mode-specific records from the first collection of records that are associated with the first search mode, each of the one or more search modes being associated with a particular collection of records from among multiple collections of records; formatting a plurality of the second search results using a mode-specific presentation template that is associated with the first search mode to generate formatted search results; and providing a second user interface that presents for display the formatted search results.
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53. A computer storage medium encoded with a computer program, the program comprising instructions that when executed by data processing apparatus cause the data processing apparatus to perform operations comprising: receiving a search query; generating first search results that identify resources that a search engine has identified as being responsive to the search query; identifying one or more search modes based on the search query, the resources, or both the search query and the resources; providing a first user interface that presents for display at least a portion of the first search results and a respective search mode selector for each of one or more of the identified one or more search modes; receiving user input selecting a first search mode by selecting one of the search mode selectors, wherein: the first search mode is associated with a first collection of records, all records in the first collection have a common attribute structure of data elements, the first search results identify resources that the search engine has identified from a corpus of resources as being responsive to the search query, and all records in the corpus do not have the common attribute structure of data elements; generating second search results that satisfy the search query and that refer to mode-specific records from the first collection of records that are associated with the first search mode, each of the one or more search modes being associated with a particular collection of records from among multiple collections of records; formatting a plurality of the second search results using a mode-specific presentation template that is associated with the first search mode to generate formatted search results; and providing a second user interface that presents for display the formatted search results. 54. The computer storage medium of claim 53 , wherein all records in each particular collection have a common attribute structure of data elements that pertain to the respective search mode.
| 0.635559 |
3. A method as recited in claim 1 , the method further comprising: determining a satisfaction of a first condition, the first condition being defined by a first class file being the same as a second class file used by the second class loader to define the second class type; and determining the satisfaction of a second condition, the second condition being defined by a loader independent part of the runtime representation of a super class type of the second class type being the same as a loader independent part of a runtime representation of a super class type of the first class type; wherein determining the first condition, the second condition, and the unimplemented method condition enables the runtime representation of the second class type to use the first loader independent part of the runtime representation of the first class type.
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3. A method as recited in claim 1 , the method further comprising: determining a satisfaction of a first condition, the first condition being defined by a first class file being the same as a second class file used by the second class loader to define the second class type; and determining the satisfaction of a second condition, the second condition being defined by a loader independent part of the runtime representation of a super class type of the second class type being the same as a loader independent part of a runtime representation of a super class type of the first class type; wherein determining the first condition, the second condition, and the unimplemented method condition enables the runtime representation of the second class type to use the first loader independent part of the runtime representation of the first class type. 15. A method as recited in claim 3 , wherein determining if the first class file is the same as the second class file comprises: computing a Secure Hash Algorithm-1 (“SHA-1”) digest for the first class file and the second class file; and comparing the SHA-1 digest of the first class file with the SHA-1 digest of the second class file, wherein the first class file is the same as the second class file if the SHA-1 digest of the first class file and the SHA-1 digest of the second class file have equivalent values.
| 0.770177 |
1. A method of interacting with a mobile communication facility comprising: receiving a switch activation from a user to initiate a speech recognition recording session, wherein the speech recognition recording session comprises a voice command from the user followed by the speech to be recognized from the user, wherein the voice command is an application name; recording the speech recognition recording session using a mobile communication facility resident capture facility; storing a dictionary representation including multiple partial pronunciations of the application name at one or more of the mobile communication facility and a speech recognition facility; determining that a first portion of the application name was clipped as a result of the user providing the command before the mobile communication facility resident capture facility was ready to receive; recognizing a second portion of the application name as an indication that user speech for recognition will begin following the end of the second portion of the application name, wherein recognizing the second portion of the application name is through analysis of the dictionary representation, wherein the second portion of the application name is a portion of the application name that was not clipped off during the speech recognition recording session wherein recognizing the second portion of the application name is performed internal to the mobile communication facility; recognizing the recorded speech using a speech recognition facility to produce an external output wherein recognizing the recorded speech is performed external to the mobile communications facility; and using the selected output to perform a function on the mobile communication facility.
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1. A method of interacting with a mobile communication facility comprising: receiving a switch activation from a user to initiate a speech recognition recording session, wherein the speech recognition recording session comprises a voice command from the user followed by the speech to be recognized from the user, wherein the voice command is an application name; recording the speech recognition recording session using a mobile communication facility resident capture facility; storing a dictionary representation including multiple partial pronunciations of the application name at one or more of the mobile communication facility and a speech recognition facility; determining that a first portion of the application name was clipped as a result of the user providing the command before the mobile communication facility resident capture facility was ready to receive; recognizing a second portion of the application name as an indication that user speech for recognition will begin following the end of the second portion of the application name, wherein recognizing the second portion of the application name is through analysis of the dictionary representation, wherein the second portion of the application name is a portion of the application name that was not clipped off during the speech recognition recording session wherein recognizing the second portion of the application name is performed internal to the mobile communication facility; recognizing the recorded speech using a speech recognition facility to produce an external output wherein recognizing the recorded speech is performed external to the mobile communications facility; and using the selected output to perform a function on the mobile communication facility. 10. The method of claim 1 , wherein the voice command is verified by characteristics of the user's voice.
| 0.551994 |
1. A client computer for updating a database stored on a server via a network, the client computer comprising: a network interconnect to communicate with the server via the network to update the database, wherein the database comprises first data items and suffix items, each one of the suffix items describes a suffix of at least one first data item of the first data items, and for each suffix item a first referential connection exists in the database assigning said suffix item to the at least one first data item of the first data items that comprises the suffix of said suffix item, each suffix item is encrypted with a suffix cryptographic key in the database, each first data item is encrypted with a first cryptographic key in the database; a microprocessor to execute an application program stored at a non-transitory processor-readable medium, the application program configured to: a) receive an update first data item, the update first data item comprising a set of successional symbols, b) creating create an update suffix item by removing a number of the set of successional symbols from the left side of the update first data item, the update suffix item being the residual part of the update first data item without the removed symbols, c) encrypt the update suffix item with the suffix cryptographic key for obtaining an encrypted update suffix item and encrypting the update first data item with the first cryptographic key for obtaining an encrypted update first data item, d) provide a storage request to the database, the storage request comprising instructions to store in the database the encrypted update suffix item, the encrypted update first data item and the first referential connection assigning said encrypted update suffix item to the encrypted update first data item, e) repeat steps b)-d) with different numbers of the successional removed symbols, the numbers being in between a minimum and a maximum, wherein the maximum is given by the total number of symbols of the update first data item minus a predefined minimal word length.
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1. A client computer for updating a database stored on a server via a network, the client computer comprising: a network interconnect to communicate with the server via the network to update the database, wherein the database comprises first data items and suffix items, each one of the suffix items describes a suffix of at least one first data item of the first data items, and for each suffix item a first referential connection exists in the database assigning said suffix item to the at least one first data item of the first data items that comprises the suffix of said suffix item, each suffix item is encrypted with a suffix cryptographic key in the database, each first data item is encrypted with a first cryptographic key in the database; a microprocessor to execute an application program stored at a non-transitory processor-readable medium, the application program configured to: a) receive an update first data item, the update first data item comprising a set of successional symbols, b) creating create an update suffix item by removing a number of the set of successional symbols from the left side of the update first data item, the update suffix item being the residual part of the update first data item without the removed symbols, c) encrypt the update suffix item with the suffix cryptographic key for obtaining an encrypted update suffix item and encrypting the update first data item with the first cryptographic key for obtaining an encrypted update first data item, d) provide a storage request to the database, the storage request comprising instructions to store in the database the encrypted update suffix item, the encrypted update first data item and the first referential connection assigning said encrypted update suffix item to the encrypted update first data item, e) repeat steps b)-d) with different numbers of the successional removed symbols, the numbers being in between a minimum and a maximum, wherein the maximum is given by the total number of symbols of the update first data item minus a predefined minimal word length. 6. The client computer of claim 1 , wherein information content is associated with the update first data item, wherein the database further comprises second data items, wherein the second data items are encrypted with a second cryptographic key, wherein a second referential connection exists assigning each encrypted first data item to at least one of the second data items stored encrypted in the database, wherein further information content is comprised in the second data items, wherein the application program is further operable for encrypting the information content associated with the update first data item with the second cryptographic key, wherein the storage request further comprises an instruction to the database to store the encrypted information content associated with the update first data item in the database and to provide the update first data item stored encrypted in the database with a second referential connection to the encrypted information content associated with the update first data item in the database.
| 0.537234 |
8. A computing device comprising: a processor; and a memory macro connected to the processor comprising: a memory array having a plurality of rows, each row of the plurality of rows of the memory array including a plurality of memory words; a plurality of first bits, each first bit of the plurality of first bits associated with a memory word of the plurality of memory words of the each row of the plurality of rows of the memory array, wherein a logic state of the each first bit indicates whether the memory word associated with the each first bit has had a failed bit; a plurality of redundancy rows, each redundancy row of the plurality of redundancy rows including a plurality of redundancy words, each redundancy word of the plurality of redundancy words associated with a corresponding memory word of the plurality of memory words of the each row of the plurality of rows of the memory array; and a corrected data cache having at least one repair word configured to store corrected data and at least one status bit associated with the at least one repair word, the status bit indicating whether the corrected data stored in the repair word is a pending repair, the corrected data cache configured to write the corrected data stored in the repair word to at least one of a counterpart memory word or a counterpart redundancy word.
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8. A computing device comprising: a processor; and a memory macro connected to the processor comprising: a memory array having a plurality of rows, each row of the plurality of rows of the memory array including a plurality of memory words; a plurality of first bits, each first bit of the plurality of first bits associated with a memory word of the plurality of memory words of the each row of the plurality of rows of the memory array, wherein a logic state of the each first bit indicates whether the memory word associated with the each first bit has had a failed bit; a plurality of redundancy rows, each redundancy row of the plurality of redundancy rows including a plurality of redundancy words, each redundancy word of the plurality of redundancy words associated with a corresponding memory word of the plurality of memory words of the each row of the plurality of rows of the memory array; and a corrected data cache having at least one repair word configured to store corrected data and at least one status bit associated with the at least one repair word, the status bit indicating whether the corrected data stored in the repair word is a pending repair, the corrected data cache configured to write the corrected data stored in the repair word to at least one of a counterpart memory word or a counterpart redundancy word. 14. The computing device of claim 8 , further comprising an error correction engine configured to generate an error-repair flag based on the state of the each first bit and an error in the memory word associated with the each first bit; and/or to generate the error-repair flag based on the state of the each second bit and an error in the redundancy word associated with the each second bit.
| 0.562453 |
1. A method for discovering knowledge from a set of text documents using a processor, the method comprising: extracting semi-structured meta-data from the set of text documents using a meta-data extractor, the semi-structured meta-data comprising a plurality of concepts and a plurality of relations between the concepts; filtering the semi-structured meta-data to identify a set of key concepts and a corresponding set of key relations between the key concepts, the set of key concepts corresponding to the plurality of concepts; deriving at least one set of sub-concepts corresponding to the set of key concepts based upon data within a domain knowledge base, using a meta-data transformer; formulating a plurality of training samples, each training sample including a vector representing a sub-concept and a vector representing a key concept; and analyzing the plurality of training samples using an associative discoverer to derive a set of associations between a set of vectors representing a sub-concept and at least one vector representing a key concept, wherein neither the set of text documents nor the semi-structured meta-data mention the set of associations, and wherein the set of associations corresponds to discovered knowledge that is extractable by a knowledge interpreter.
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1. A method for discovering knowledge from a set of text documents using a processor, the method comprising: extracting semi-structured meta-data from the set of text documents using a meta-data extractor, the semi-structured meta-data comprising a plurality of concepts and a plurality of relations between the concepts; filtering the semi-structured meta-data to identify a set of key concepts and a corresponding set of key relations between the key concepts, the set of key concepts corresponding to the plurality of concepts; deriving at least one set of sub-concepts corresponding to the set of key concepts based upon data within a domain knowledge base, using a meta-data transformer; formulating a plurality of training samples, each training sample including a vector representing a sub-concept and a vector representing a key concept; and analyzing the plurality of training samples using an associative discoverer to derive a set of associations between a set of vectors representing a sub-concept and at least one vector representing a key concept, wherein neither the set of text documents nor the semi-structured meta-data mention the set of associations, and wherein the set of associations corresponds to discovered knowledge that is extractable by a knowledge interpreter. 7. The method as in claim 1 , wherein analyzing the plurality of training samples using the associative discoverer comprises the step of analyzing the plurality of training samples using at least one of a neural network, a statistical system, and a symbolic machine learning system.
| 0.622956 |
1. A computer-implemented method for producing a real-time presentation of variable length and inserting media events in same, comprising: displaying a text script on a display; receiving input from a user positioning a plurality of visual images associated with a plurality of media events adjacent to said text script in a scrollable portion of said display to establish a spatial relationship between said plurality of visual images and said text script, such that each visual image's position corresponds to one or more words in said text script with which said associated media event is to begin during a presentation; scrolling said text script on said display while maintaining said spatial relationship between said text script and said visual images; causing said media events associated with said visual images to begin approximately upon the corresponding one or more words of the text script scrolling through a predetermined region of said display during said presentation; and generating said presentation, including audio information corresponding to the user speaking at least a portion of said text script.
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1. A computer-implemented method for producing a real-time presentation of variable length and inserting media events in same, comprising: displaying a text script on a display; receiving input from a user positioning a plurality of visual images associated with a plurality of media events adjacent to said text script in a scrollable portion of said display to establish a spatial relationship between said plurality of visual images and said text script, such that each visual image's position corresponds to one or more words in said text script with which said associated media event is to begin during a presentation; scrolling said text script on said display while maintaining said spatial relationship between said text script and said visual images; causing said media events associated with said visual images to begin approximately upon the corresponding one or more words of the text script scrolling through a predetermined region of said display during said presentation; and generating said presentation, including audio information corresponding to the user speaking at least a portion of said text script. 23. The method of claim 1 , further comprising: receiving input associating a transition with one of the plurality of media events, wherein a transition indicator is displayed in conjunction with the visual image associated with the media event.
| 0.549785 |
13. A computer readable storage medium storing instructions which, when executed by a computer, cause the computer to perform steps of: receiving a textual input comprising a text fragment selected from a plurality of text fragments; accessing a plurality of stored statistical grammatical element prediction models configured to assign grammatical elements comprising one or more of case markers, postpositions, prepositions, articles, function words, and inflections, wherein accessing comprises: accessing a first statistical local model configured to determine whether a grammatical element is to be assigned to the selected text fragment and, if so, to identify at least one of a plurality of different grammatical elements to be assigned to the selected text fragment, wherein the first statistical local model identifies the at least one grammatical element based on the selected text fragment independent of grammatical elements identified to be assigned to other text fragments of the plurality of text fragments; and accessing a second statistical joint model configured to determine whether a grammatical element is to be assigned to the selected text fragment and, if so, to identify at least one of a plurality of different grammatical elements to be assigned to the selected text fragment, wherein the second statistical model identifies the at least one grammatical element based on the selected text fragment and at least one grammatical element identified to be assigned to another text fragment of the plurality of text fragments; predicting, using a processor of the computer, grammatical elements for the selected text fragment using the stored statistical grammatical element prediction models including the first and second statistical models, wherein predicting comprises combining probability distributions of the first and second statistical models; and outputting the selected text fragment along with an indication of the predicted grammatical element.
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13. A computer readable storage medium storing instructions which, when executed by a computer, cause the computer to perform steps of: receiving a textual input comprising a text fragment selected from a plurality of text fragments; accessing a plurality of stored statistical grammatical element prediction models configured to assign grammatical elements comprising one or more of case markers, postpositions, prepositions, articles, function words, and inflections, wherein accessing comprises: accessing a first statistical local model configured to determine whether a grammatical element is to be assigned to the selected text fragment and, if so, to identify at least one of a plurality of different grammatical elements to be assigned to the selected text fragment, wherein the first statistical local model identifies the at least one grammatical element based on the selected text fragment independent of grammatical elements identified to be assigned to other text fragments of the plurality of text fragments; and accessing a second statistical joint model configured to determine whether a grammatical element is to be assigned to the selected text fragment and, if so, to identify at least one of a plurality of different grammatical elements to be assigned to the selected text fragment, wherein the second statistical model identifies the at least one grammatical element based on the selected text fragment and at least one grammatical element identified to be assigned to another text fragment of the plurality of text fragments; predicting, using a processor of the computer, grammatical elements for the selected text fragment using the stored statistical grammatical element prediction models including the first and second statistical models, wherein predicting comprises combining probability distributions of the first and second statistical models; and outputting the selected text fragment along with an indication of the predicted grammatical element. 14. The computer readable storage medium of claim 13 wherein accessing a plurality of stored statistical grammatical element prediction models comprises: accessing a set of local models that determine whether the selected text fragment is to have a grammatical element predicted for it and, if so, to predict a grammatical element, based on features of the selected text fragment itself; and accessing a joint model that predicts a grammatical element for the selected text fragment based on grammatical elements predicted for at least one other text fragment of the plurality of text fragments.
| 0.5 |
7. A system to provide contractual precedents, the system comprising: a server device including a processor module configured to: receive a user selection of one or more predefined queries for contractual information; execute a search of one or more databases including agreements based on the received user selection, the received user selection defining search results; and receive a user-defined filter associated with the contractual information, the user-defined filter being based on at least a first trait, the user-defined filter being associated with an identifier corresponding to the user, the identifier defining access rights of at least one additional user; and execute the user-defined filter thereby filtering the search results; and an access device configured to: display a subset of the search results based on the user-defined filter associated with the identifier corresponding to the user; and transmit at least one contractual provision selected from the subset of the search results into an editable document to facilitate review in an active user interface using an editing application.
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7. A system to provide contractual precedents, the system comprising: a server device including a processor module configured to: receive a user selection of one or more predefined queries for contractual information; execute a search of one or more databases including agreements based on the received user selection, the received user selection defining search results; and receive a user-defined filter associated with the contractual information, the user-defined filter being based on at least a first trait, the user-defined filter being associated with an identifier corresponding to the user, the identifier defining access rights of at least one additional user; and execute the user-defined filter thereby filtering the search results; and an access device configured to: display a subset of the search results based on the user-defined filter associated with the identifier corresponding to the user; and transmit at least one contractual provision selected from the subset of the search results into an editable document to facilitate review in an active user interface using an editing application. 11. The system of claim 7 , wherein the user-defined filter is a predefined filter associated with the identifier corresponding to the user.
| 0.535683 |
1. A method for embedding symbols of a message into a document containing a set of glyphs, comprising: representing a glyph in a document as a distance field; representing a symbol in a message to be embedded in the document as a modification of a subset of values in the distance field; and modifying the subset of values in the distance field according to the modification to produce a modified glyph in a modified document, wherein the symbol in the message is embedded in the modified glyph, wherein steps of the method are performed by a processor.
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1. A method for embedding symbols of a message into a document containing a set of glyphs, comprising: representing a glyph in a document as a distance field; representing a symbol in a message to be embedded in the document as a modification of a subset of values in the distance field; and modifying the subset of values in the distance field according to the modification to produce a modified glyph in a modified document, wherein the symbol in the message is embedded in the modified glyph, wherein steps of the method are performed by a processor. 8. The method of claim 1 , wherein the modification defines new values for the subset of values in the distance field.
| 0.640244 |
4. The printer processing method described in claim 1 , wherein the POS application manages the printer by use of the status information in the second mark-up language document output as XML document.
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4. The printer processing method described in claim 1 , wherein the POS application manages the printer by use of the status information in the second mark-up language document output as XML document. 5. The printer processing method described in claim 4 , wherein the printer prints based on the transaction process.
| 0.973371 |
41. A system, comprising: a first program module for receiving a digitized version of a two-dimensional (20) image containing a 2D representation of a three-dimensional (3D) object; a second program module for obtaining visual information from said digitized version of said 2D image; and a third program module for classifying said 2D image based on a ratio of a plurality of Bayesian networks using said visual information wherein said classifying includes: applying a view-based classifier to said 2D image, wherein said classifier includes a plurality of sub-classifiers; computing a sum of log-likelihood ratios for each of said plurality of sub-classifiers, wherein said log-likelihood ratio is a ratio of two graphical probability models; wherein a graphical probability model comprises a probability distribution over a set of variables where statistical independence and conditional statistical independence exist among various combinations of the variables, wherein the graphical probability model is a probability distribution representation derived from statistical dependencies among image input variables.
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41. A system, comprising: a first program module for receiving a digitized version of a two-dimensional (20) image containing a 2D representation of a three-dimensional (3D) object; a second program module for obtaining visual information from said digitized version of said 2D image; and a third program module for classifying said 2D image based on a ratio of a plurality of Bayesian networks using said visual information wherein said classifying includes: applying a view-based classifier to said 2D image, wherein said classifier includes a plurality of sub-classifiers; computing a sum of log-likelihood ratios for each of said plurality of sub-classifiers, wherein said log-likelihood ratio is a ratio of two graphical probability models; wherein a graphical probability model comprises a probability distribution over a set of variables where statistical independence and conditional statistical independence exist among various combinations of the variables, wherein the graphical probability model is a probability distribution representation derived from statistical dependencies among image input variables. 42. The system of claim 41 , wherein said second program module includes: a fourth program module for computing a wavelet transform of said 20 image, wherein said wavelet transform generates a plurality of transform coefficients, and wherein each transform coefficient represents a portion of said visual information from said 2D image that is localized in space, frequency, and orientation.
| 0.689117 |
8. A system for intelligent event-based data mining, comprising one or more processing units configured for: communication means configured to receive an event from an application, said event having a set of properties; a processor configured to assign each of said properties a respective property weight; a memory suitable for storing a search manager configured to build a query from said properties based on the property weights; said search manager further being configured for: assigning each of a set of search engines a respective search engine weight; selecting at least some of the search engines based on the search engine weights; sending the query to the selected search engines; receiving query results from the selected search engines; storing the query results in a knowledge repository; and adjusting the property weights and the search engine weights based on the query results, wherein the adjusting of said property weights and the search engine weights includes calculating a search result relevance for each query result and using said search result relevance to adjust the property weights and the search engine weights; wherein the calculating of said search result relevance includes, for each of the query results, identifying the number of common text substrings that occur in both the query and said each query result, and using said number of common text substrings to calculate the search result relevance for said each query result; and wherein using said number of common text substrings to calculate the search result relevance for said each query result includes calculating the result relevant using the equation: ( m 1 × m 2 … m n ) ( N × n ) × 100 = Result Relevance where: m n is the number of words in the nth match N is the number of words in the query n is the number of matched subsequences.
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8. A system for intelligent event-based data mining, comprising one or more processing units configured for: communication means configured to receive an event from an application, said event having a set of properties; a processor configured to assign each of said properties a respective property weight; a memory suitable for storing a search manager configured to build a query from said properties based on the property weights; said search manager further being configured for: assigning each of a set of search engines a respective search engine weight; selecting at least some of the search engines based on the search engine weights; sending the query to the selected search engines; receiving query results from the selected search engines; storing the query results in a knowledge repository; and adjusting the property weights and the search engine weights based on the query results, wherein the adjusting of said property weights and the search engine weights includes calculating a search result relevance for each query result and using said search result relevance to adjust the property weights and the search engine weights; wherein the calculating of said search result relevance includes, for each of the query results, identifying the number of common text substrings that occur in both the query and said each query result, and using said number of common text substrings to calculate the search result relevance for said each query result; and wherein using said number of common text substrings to calculate the search result relevance for said each query result includes calculating the result relevant using the equation: ( m 1 × m 2 … m n ) ( N × n ) × 100 = Result Relevance where: m n is the number of words in the nth match N is the number of words in the query n is the number of matched subsequences. 11. The system according to claim 8 , wherein identifying the number of common text substrings that occur in both the query and said each query result includes: identifying the longest common substring in both the query and said each query result; removing said longest common substring from the query to obtain a modified query; and identifying the longest common substring in both the modified query and said query result.
| 0.521645 |
15. The method according to claim 14 , wherein: to query comprises querying the relational database using SPARQL; and to access semantically relevant query results comprises interfacing with the relational database in a manner to obtain SPARQL querying results.
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15. The method according to claim 14 , wherein: to query comprises querying the relational database using SPARQL; and to access semantically relevant query results comprises interfacing with the relational database in a manner to obtain SPARQL querying results. 21. The method according to claim 15 , wherein said at least one ontology comprises: domain ontologies that provide additional knowledge about entities, relations, and instances in the relational database; data-modeling ontologies that describe a semantic model of data described in the relational database and reuses concepts described in domain ontologies; and custom ontologies that define concepts, properties, and individuals required for a specific application.
| 0.831081 |
9. A system comprising: a processor; and a computer-readable storage medium having instructions stored which, when executed by the processor, cause the processor to perform operations comprising: analyzing a first natural language corpus to generate a latent representation for words in the first natural language corpus; calculating, for each word in the latent representation, a Euclidian distance between a left context of the each word and a right context of the each word, to yield a centroid of latent vectors for each word in the latent representation; analyzing a second natural language corpus having a target word, the target word being a word that is not in the first natural language corpus; and predicting a label for the target word based on the latent representation and the centroid of latent vectors for each word in the latent representation.
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9. A system comprising: a processor; and a computer-readable storage medium having instructions stored which, when executed by the processor, cause the processor to perform operations comprising: analyzing a first natural language corpus to generate a latent representation for words in the first natural language corpus; calculating, for each word in the latent representation, a Euclidian distance between a left context of the each word and a right context of the each word, to yield a centroid of latent vectors for each word in the latent representation; analyzing a second natural language corpus having a target word, the target word being a word that is not in the first natural language corpus; and predicting a label for the target word based on the latent representation and the centroid of latent vectors for each word in the latent representation. 11. The system of claim 9 , wherein predicting the label for the target word is further based on a connectionist model.
| 0.558854 |
1. A training module implemented on one or more computers comprised of one or more processors and storage, for training a tagging model, the training module comprising: logic that receives and stores an explicitly-labeled training set in the storage, the explicitly-labeled training set including explicit labels that have been manually selected; logic that receives and stores an implicitly-labeled training set in the storage, the implicitly-labeled training set including implicit labels that have been generated by a labeling system; and logic performed by the one or more processors that trains a tagging model based on the explicitly-labeled training set and the implicitly-labeled training set, and that stores the tagging model in the storage, wherein the logic that trains also maximizes a training objective, wherein the training objective is a function of implicit label information, query information, and state variable information.
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1. A training module implemented on one or more computers comprised of one or more processors and storage, for training a tagging model, the training module comprising: logic that receives and stores an explicitly-labeled training set in the storage, the explicitly-labeled training set including explicit labels that have been manually selected; logic that receives and stores an implicitly-labeled training set in the storage, the implicitly-labeled training set including implicit labels that have been generated by a labeling system; and logic performed by the one or more processors that trains a tagging model based on the explicitly-labeled training set and the implicitly-labeled training set, and that stores the tagging model in the storage, wherein the logic that trains also maximizes a training objective, wherein the training objective is a function of implicit label information, query information, and state variable information. 2. The training module of claim 1 , wherein, for the implicitly-labeled training set, said logic configured to train: treats the implicit labels as fixing state sequence variables that correspond to the implicit labels; and treats any state sequence variable for which there is a missing label as a hidden variable.
| 0.5 |
9. A system for generating an application, comprising: a computer, including: a transceiver for communicating over the network; a memory for storing at least instructions; and a processor device that executes instructions that perform actions, including: responsive to encountering a multi-size type during compilation of an intermediate language version of the application into a machine code version of the application, performing actions, including: determining architecture information of a target computer, wherein the architecture information includes at least a word size of the target computer; determining one or more data types associated with the target computer that corresponds to the multi-sized type based on the architecture information; determining one or more native code calls that perform actions associated with an intermediate language code call, wherein parameters to the one or more native codes call match the one or more data types; generating a machine code version of the intermediate language code call that at least corresponds to the one or more determined native code calls and also corresponds to the architecture information; when the target computer enables just-in-time compiling, executing the generated machine code version of the intermediate language code call with one or more values correspondent to the multi-size type and the one or more data types by executing the one or more determined native code calls using the one or more determined data types that correspond to the architecture information; and when the target computer disables just-in-time compiling, inserting the generated machine code version of the intermediate language code call in the machine code version of the application with one or more values correspondent to the multi-size type and the one or more data types, wherein the generated machine code version includes instructions to execute the one or more determined native code calls; and a network computer, including: a transceiver for communicating over the network; a memory for storing at least instructions; and a processor device that executes instructions that enable actions, including: providing the intermediate language code version of the application to the computer.
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9. A system for generating an application, comprising: a computer, including: a transceiver for communicating over the network; a memory for storing at least instructions; and a processor device that executes instructions that perform actions, including: responsive to encountering a multi-size type during compilation of an intermediate language version of the application into a machine code version of the application, performing actions, including: determining architecture information of a target computer, wherein the architecture information includes at least a word size of the target computer; determining one or more data types associated with the target computer that corresponds to the multi-sized type based on the architecture information; determining one or more native code calls that perform actions associated with an intermediate language code call, wherein parameters to the one or more native codes call match the one or more data types; generating a machine code version of the intermediate language code call that at least corresponds to the one or more determined native code calls and also corresponds to the architecture information; when the target computer enables just-in-time compiling, executing the generated machine code version of the intermediate language code call with one or more values correspondent to the multi-size type and the one or more data types by executing the one or more determined native code calls using the one or more determined data types that correspond to the architecture information; and when the target computer disables just-in-time compiling, inserting the generated machine code version of the intermediate language code call in the machine code version of the application with one or more values correspondent to the multi-size type and the one or more data types, wherein the generated machine code version includes instructions to execute the one or more determined native code calls; and a network computer, including: a transceiver for communicating over the network; a memory for storing at least instructions; and a processor device that executes instructions that enable actions, including: providing the intermediate language code version of the application to the computer. 12. The system of claim 9 , wherein determining the one or more native codes call that is associated with the intermediate language code call, further comprises, determining the one or more native codes call based on a pattern match that includes one or more data types and a name of the one or more one native code calls.
| 0.532491 |
11. A non-transitory computer-readable storage medium containing instructions for controlling a computer system of an electronic marketplace to provide data about an item by a method comprising: storing a plurality of queries previously submitted to the computer system of the electronic marketplace, wherein individual previously-submitted queries are stored with a popularity of submission for the previously-submitted query; responsive to a link-selection request by a user for a web page of a media web site provided by a media content server, receiving an automatically generated request from the media content server to provide information based at least in part on web page content to be displayed on the media web site, the media content server being different than the computer system of the electronic marketplace, the web page content submitted in the generated request having been derived by the media content server; matching the content to the item by at least: identifying a previously-submitted query from the plurality of previously-submitted queries as a matching query, said identifying based at least in part on a relevance of the matching query to the web page content and the popularity of submission of the matching query; and selecting the item from a set of one or more items that have been determined to correspond to the matching query; and providing, asynchronously relative to when the web page content is made available by the media content server, the information about the selected item to be displayed on the media web site along with the web page content.
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11. A non-transitory computer-readable storage medium containing instructions for controlling a computer system of an electronic marketplace to provide data about an item by a method comprising: storing a plurality of queries previously submitted to the computer system of the electronic marketplace, wherein individual previously-submitted queries are stored with a popularity of submission for the previously-submitted query; responsive to a link-selection request by a user for a web page of a media web site provided by a media content server, receiving an automatically generated request from the media content server to provide information based at least in part on web page content to be displayed on the media web site, the media content server being different than the computer system of the electronic marketplace, the web page content submitted in the generated request having been derived by the media content server; matching the content to the item by at least: identifying a previously-submitted query from the plurality of previously-submitted queries as a matching query, said identifying based at least in part on a relevance of the matching query to the web page content and the popularity of submission of the matching query; and selecting the item from a set of one or more items that have been determined to correspond to the matching query; and providing, asynchronously relative to when the web page content is made available by the media content server, the information about the selected item to be displayed on the media web site along with the web page content. 13. The non-transitory computer-readable storage medium of claim 11 , wherein the relevance of the matching query to the web page content is based at least in part on matching phrases in the web page content to the previously-submitted queries.
| 0.607825 |
1. An apparatus for programmatically analyzing a consumer review, the apparatus comprising: a processor configured to programmatically access, via a networked device, one or more consumer reviews for a commercial entity or a commercial object; a consumer review processing engine programmed to programmatically identify an attribute descriptor in the one or more consumer reviews, and programmatically generate a sentiment score associated with the one or more consumer reviews, wherein programmatic generation of the sentiment score comprises: using a natural language processing engine to programmatically parse the consumer review into a set of sentences; using the natural language processing engine to programmatically parse each sentence in the set of sentences into a set of words; for each word in the set of words in each sentence, programmatically generating a word sentiment score; for each sentence in the set of sentences, programmatically generating a sentence sentiment score, the sentence sentiment score generated based on word sentiment scores associated with words in the sentence, wherein programmatically generating the sentence sentiment scores includes applying a machine learning algorithm to determine a relationship between the sentence sentiment score and the word sentiment scores associated with the words in the sentence; and programmatically generating the sentiment score by combining sentence sentiment scores associated with the set of sentences in the consumer review; and a non-transitory computer-readable storage device configured to store the attribute descriptor and the sentiment score in association with the commercial entity or the commercial object.
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1. An apparatus for programmatically analyzing a consumer review, the apparatus comprising: a processor configured to programmatically access, via a networked device, one or more consumer reviews for a commercial entity or a commercial object; a consumer review processing engine programmed to programmatically identify an attribute descriptor in the one or more consumer reviews, and programmatically generate a sentiment score associated with the one or more consumer reviews, wherein programmatic generation of the sentiment score comprises: using a natural language processing engine to programmatically parse the consumer review into a set of sentences; using the natural language processing engine to programmatically parse each sentence in the set of sentences into a set of words; for each word in the set of words in each sentence, programmatically generating a word sentiment score; for each sentence in the set of sentences, programmatically generating a sentence sentiment score, the sentence sentiment score generated based on word sentiment scores associated with words in the sentence, wherein programmatically generating the sentence sentiment scores includes applying a machine learning algorithm to determine a relationship between the sentence sentiment score and the word sentiment scores associated with the words in the sentence; and programmatically generating the sentiment score by combining sentence sentiment scores associated with the set of sentences in the consumer review; and a non-transitory computer-readable storage device configured to store the attribute descriptor and the sentiment score in association with the commercial entity or the commercial object. 4. The apparatus of claim 1 , wherein the sentiment score is associated with the commercial entity or the commercial object but not specifically with the attribute descriptor.
| 0.897555 |
14. A method comprising: receiving by a computer a user query; identifying by the computer one or more trigger words in the user query; selecting by the computer one or more corresponding tags from a landmark database corresponding to the one or more trigger words; supplementing by the computer the user query with the one or more corresponding tags, generating a supplemented user query; retrieving by the computer one or more landmarks based on the supplemented user query; generating by the computer a user interface including the one or more retrieved landmarks; and causing one or more summary lists of visual clusters for the retrieved landmarks to be displayed on the user interface, wherein each summary list corresponds to one of the retrieved landmarks; and causing popularity information to be displayed on the one or more summary lists on the user interface.
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14. A method comprising: receiving by a computer a user query; identifying by the computer one or more trigger words in the user query; selecting by the computer one or more corresponding tags from a landmark database corresponding to the one or more trigger words; supplementing by the computer the user query with the one or more corresponding tags, generating a supplemented user query; retrieving by the computer one or more landmarks based on the supplemented user query; generating by the computer a user interface including the one or more retrieved landmarks; and causing one or more summary lists of visual clusters for the retrieved landmarks to be displayed on the user interface, wherein each summary list corresponds to one of the retrieved landmarks; and causing popularity information to be displayed on the one or more summary lists on the user interface. 16. The method of claim 14 , wherein each summary list includes a link that can be used to retrieve further details about the visual clusters.
| 0.648438 |
1. A system for providing ratings, comprising: a non-transitory computer usable storage medium having a computer readable first program code embodied therein, said computer readable first program code adapted to execute as a browser toolbar in a browser on an end user computing device operable by a first user executable to implement a method comprising: transmitting a request to a remote computer system in response to a web page being loaded into the browser executing on the end user computing device; wherein the request includes an identifier (ID) and a Uniform Resource Locator (URL) of the web page loaded in the browser executing on the end user computing device; in response to said transmitting, receiving a rating value from the remote computer system; displaying the rating value in the toolbar concurrently with the web page; receiving a first rating of the first user from the remote computer system; wherein the first rating comprises a rating previously supplied by the first user prior to the web page being loaded into the browser; displaying the first rating in the toolbar concurrently with the web page; receiving a user input comprising a second rating from the first user; transmitting the second rating to the remote computer system; and displaying the second rating in the toolbar.
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1. A system for providing ratings, comprising: a non-transitory computer usable storage medium having a computer readable first program code embodied therein, said computer readable first program code adapted to execute as a browser toolbar in a browser on an end user computing device operable by a first user executable to implement a method comprising: transmitting a request to a remote computer system in response to a web page being loaded into the browser executing on the end user computing device; wherein the request includes an identifier (ID) and a Uniform Resource Locator (URL) of the web page loaded in the browser executing on the end user computing device; in response to said transmitting, receiving a rating value from the remote computer system; displaying the rating value in the toolbar concurrently with the web page; receiving a first rating of the first user from the remote computer system; wherein the first rating comprises a rating previously supplied by the first user prior to the web page being loaded into the browser; displaying the first rating in the toolbar concurrently with the web page; receiving a user input comprising a second rating from the first user; transmitting the second rating to the remote computer system; and displaying the second rating in the toolbar. 4. The system according to claim 1 , wherein the URL includes a domain and wherein the remote computer system is configured to receive the ID; identify one or more friends of the first user associated with the ID; determine whether a computer associated with any of the one or more friends has received a web page from the domain within a predetermined time period; and transmit a friend notification to the end user computing device if a computer associated with any of the one or more friends has received a web page from the domain within the predetermined time period.
| 0.614888 |
13. The method of claim 1 , further comprising initiating playback of a selected stored record in the detail region, at least in part, by interactively selecting a desired one of the listed traversal records.
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13. The method of claim 1 , further comprising initiating playback of a selected stored record in the detail region, at least in part, by interactively selecting a desired one of the listed traversal records. 14. The method of claim 13 , wherein said interactively selecting a desired traversal record comprises dragging and dropping a graphical element corresponding to the selected record from the worksheet region onto the detail region.
| 0.865475 |
13. A method for determining recommendation data, comprising: extracting a first set of keywords from a set of user action logs that occurred prior to a predetermined time point; extracting a second set of keywords from a set of user action logs that occurred subsequent to the predetermined time point; merging at least a portion of the first set of keywords and at least a portion of the second set of keywords to obtain a third set of keywords; matching, using one or more processors, at least one keyword in the third set of keywords with a database of data that can potentially be recommended to a user; and in the event that a piece of data in the database is determined to match the at least one keyword from the third set of keywords, determining that the piece of data is to be recommended to the user.
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13. A method for determining recommendation data, comprising: extracting a first set of keywords from a set of user action logs that occurred prior to a predetermined time point; extracting a second set of keywords from a set of user action logs that occurred subsequent to the predetermined time point; merging at least a portion of the first set of keywords and at least a portion of the second set of keywords to obtain a third set of keywords; matching, using one or more processors, at least one keyword in the third set of keywords with a database of data that can potentially be recommended to a user; and in the event that a piece of data in the database is determined to match the at least one keyword from the third set of keywords, determining that the piece of data is to be recommended to the user. 14. The method of claim 13 , wherein each user action log includes at least a timestamp, a user operation, and an associated keyword.
| 0.850877 |
3. The system according to claim 2 , wherein the trajectory pattern is created using the pointing device by at least one chosen from the list including: tapping; tracing; and a combination of tapping and tracing.
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3. The system according to claim 2 , wherein the trajectory pattern is created using the pointing device by at least one chosen from the list including: tapping; tracing; and a combination of tapping and tracing. 4. The system according to claim 3 , wherein the LWD has a plurality of classes, each of the classes containing long words having a first letter corresponding to predetermined keys of the keyboard.
| 0.893343 |
51. A computer-implemented system for providing medical consultation services connected to the internet, integrated with human resources provided by professionals affiliated with the system, to form a consensus of opinions by a panel of medical professionals selected by the system but not provided with financial incentives which could bias their decision making, the system comprising: (a) means for creating a database of potential participating medical professionals, along with information about each the potential participating medical professionals, including the qualifications of the potential participating medical professionals; (b) means for user seeking medical consultation services to accesses the system; (c) means for user to electronically submit information to the system regarding the desired medical consultation services such that at least one professional affiliated with the system can review the information submitted by the user and structure one or more questions, based upon the user submitted information, for consideration by a panel of selected medical professionals; (d) means for selecting a panel of medical professionals qualified to provide response(s) to the structured question(s) based upon information in the database; (e) means for providing the structured question(s) to the medical professionals in the selected panel such that medical professionals in the selected panel can review the structured question(s) and create responses thereto; (f) means for electronically forwarding the responses from the medical professionals in the selected panel to the system; (g) means for compiling the responses forwarded to the system by the medical professionals in the selected panel; (h) means for calculating the degree of consensus of the responses; and (i) means for displaying the calculated consensus on a system website accessible to the user.
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51. A computer-implemented system for providing medical consultation services connected to the internet, integrated with human resources provided by professionals affiliated with the system, to form a consensus of opinions by a panel of medical professionals selected by the system but not provided with financial incentives which could bias their decision making, the system comprising: (a) means for creating a database of potential participating medical professionals, along with information about each the potential participating medical professionals, including the qualifications of the potential participating medical professionals; (b) means for user seeking medical consultation services to accesses the system; (c) means for user to electronically submit information to the system regarding the desired medical consultation services such that at least one professional affiliated with the system can review the information submitted by the user and structure one or more questions, based upon the user submitted information, for consideration by a panel of selected medical professionals; (d) means for selecting a panel of medical professionals qualified to provide response(s) to the structured question(s) based upon information in the database; (e) means for providing the structured question(s) to the medical professionals in the selected panel such that medical professionals in the selected panel can review the structured question(s) and create responses thereto; (f) means for electronically forwarding the responses from the medical professionals in the selected panel to the system; (g) means for compiling the responses forwarded to the system by the medical professionals in the selected panel; (h) means for calculating the degree of consensus of the responses; and (i) means for displaying the calculated consensus on a system website accessible to the user. 67. The system of claim 51 further comprising means for creating a graphical display of the consensus of the responses of the medical professionals and for displaying same in real time.
| 0.556988 |
1. A computer system for dynamically identifying bugs as a programmer creates source code, the computer system comprising: a data entry means for creating source code; a memory for storing the source code when created, a first database comprising a plurality of patterns, and a second database comprising a plurality of corrections; and a processing means coupled to the data entry means and the memory, said processing means being configured to: dynamically evaluate the source code as the source code is created by periodically comparing the source code with the plurality of patterns, and responsive to matching a segment of the source code with one of said patterns, display at least one correction; prompt the programmer to select the at least one correction, and responsive to the programmer selecting the at least one correction, cause the text editor to change the source code to conform to the correction.
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1. A computer system for dynamically identifying bugs as a programmer creates source code, the computer system comprising: a data entry means for creating source code; a memory for storing the source code when created, a first database comprising a plurality of patterns, and a second database comprising a plurality of corrections; and a processing means coupled to the data entry means and the memory, said processing means being configured to: dynamically evaluate the source code as the source code is created by periodically comparing the source code with the plurality of patterns, and responsive to matching a segment of the source code with one of said patterns, display at least one correction; prompt the programmer to select the at least one correction, and responsive to the programmer selecting the at least one correction, cause the text editor to change the source code to conform to the correction. 8. The computer system of claim 1 , in which said correction comprises at least one of: replacement text for at least a portion of said source code and additional text for said source code.
| 0.835737 |
14. The method of claim 13, wherein the means for selecting a word at random comprises a plurality of cards, each having printed thereon a different word.
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14. The method of claim 13, wherein the means for selecting a word at random comprises a plurality of cards, each having printed thereon a different word. 15. The method of claim 14, wherein the instruction means for providing the player with instructions comprises an instruction guide listing the instructions (a), (b) and (c).
| 0.967204 |
34. A method for providing similar applications to a user, comprising: receiving from the user a search request; sending to the user, a first list of applications based on the search request; receiving from the user a selection of one of the applications on the first list; finding related applications, based on a similarity matrix and the selection; and sending to the user, a second list of related applications, wherein the similarity matrix is determined by a method comprising: receiving, by a computer, source code for a plurality of applications; associating, for each application, semantic anchors found in the source code for that application with the application, wherein associating semantic anchors comprises building at least one weighted term document matrix from the semantic anchors, the at least one weighted term document matrix comprising at least a first term weighted based on at least a number of the plurality of applications in which a first semantic anchor is present in the source code for those applications; comparing, based on the semantic anchors, a similarity of the first application to a second application; and assigning, based on the comparison, a number representing the similarity of the first and second applications.
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34. A method for providing similar applications to a user, comprising: receiving from the user a search request; sending to the user, a first list of applications based on the search request; receiving from the user a selection of one of the applications on the first list; finding related applications, based on a similarity matrix and the selection; and sending to the user, a second list of related applications, wherein the similarity matrix is determined by a method comprising: receiving, by a computer, source code for a plurality of applications; associating, for each application, semantic anchors found in the source code for that application with the application, wherein associating semantic anchors comprises building at least one weighted term document matrix from the semantic anchors, the at least one weighted term document matrix comprising at least a first term weighted based on at least a number of the plurality of applications in which a first semantic anchor is present in the source code for those applications; comparing, based on the semantic anchors, a similarity of the first application to a second application; and assigning, based on the comparison, a number representing the similarity of the first and second applications. 35. The method of claim 34 , wherein the semantic anchors comprise Application Programming Interface (API) calls.
| 0.65355 |
1. A method for creating universal information object management environment as an executable environment description, the method comprising the steps of: creating descriptions of information sources, associated information objects, object characteristics, and relationships among the information sources, information objects, and object characteristics, where the descriptions are fully functionally described by the descriptions of the information sources, associated information objects, object characteristics, and their relationships; creating universal components that represent information objects, types of sources, types of characteristics, and types of relationships, which collectively represent the services and behavior of the environment, whose configurations are defined by associated descriptions, including descriptions of associated information objects, object characteristics, and their relationships; and assembling at runtime instances of information objects defined on information sources by collecting universal components with associated objects, object characteristics, and relationships that represent their behavior and configuration within the network of their described relationships.
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1. A method for creating universal information object management environment as an executable environment description, the method comprising the steps of: creating descriptions of information sources, associated information objects, object characteristics, and relationships among the information sources, information objects, and object characteristics, where the descriptions are fully functionally described by the descriptions of the information sources, associated information objects, object characteristics, and their relationships; creating universal components that represent information objects, types of sources, types of characteristics, and types of relationships, which collectively represent the services and behavior of the environment, whose configurations are defined by associated descriptions, including descriptions of associated information objects, object characteristics, and their relationships; and assembling at runtime instances of information objects defined on information sources by collecting universal components with associated objects, object characteristics, and relationships that represent their behavior and configuration within the network of their described relationships. 2. The method of claim 1 , wherein the object characteristics include the types of service, property, event, and relationship.
| 0.609558 |
16. A method for providing proximate dataset recommendations comprising: creating of a plurality of metadata records that correspond to a plurality of datasets representing scientific data by a scientific dataset search tool, wherein said plurality of metadata records conform to a standardized structural definition, wherein values for data elements of a metadata record are contained within a corresponding dataset; identifying at least one metadata record from the plurality of metadata records having a value that is proximate to one or more user-entered search parameters, wherein proximity is determined with respect to a range represented by the corresponding user-entered search parameters, wherein one of the search parameters is a geospatial parameter; calculating a proximity score for each identified metadata record, wherein said proximity score expresses a relevance of the corresponding dataset to the user-entered search parameters, wherein calculating the proximity score comprises calculating a geospatial proximity score, wherein calculating the geospatial proximity score further comprises: determining a geospatial distance, d Gdist , from a central point of the user-entered geospatial search parameter for the dataset using the following formula or a variation or derivative thereof: d Gdist = { 0 d Gmax ≤ r ( d Gmax / r - 1 ) 2 2 ( d Gmax - d Gmin ) / r d Gmin ≤ r , d Gmax ≥ r ( d Gmin + d Gmax ) / r - 1 d Gmin > r , wherein r is the radius of the range expressed by the user-entered geospatial search parameter and d Gmin and d Gmax are the minimum and maximum distances within the dataset from the central point; and using the proximity score to filter or order metadata records to create a listing of dataset results.
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16. A method for providing proximate dataset recommendations comprising: creating of a plurality of metadata records that correspond to a plurality of datasets representing scientific data by a scientific dataset search tool, wherein said plurality of metadata records conform to a standardized structural definition, wherein values for data elements of a metadata record are contained within a corresponding dataset; identifying at least one metadata record from the plurality of metadata records having a value that is proximate to one or more user-entered search parameters, wherein proximity is determined with respect to a range represented by the corresponding user-entered search parameters, wherein one of the search parameters is a geospatial parameter; calculating a proximity score for each identified metadata record, wherein said proximity score expresses a relevance of the corresponding dataset to the user-entered search parameters, wherein calculating the proximity score comprises calculating a geospatial proximity score, wherein calculating the geospatial proximity score further comprises: determining a geospatial distance, d Gdist , from a central point of the user-entered geospatial search parameter for the dataset using the following formula or a variation or derivative thereof: d Gdist = { 0 d Gmax ≤ r ( d Gmax / r - 1 ) 2 2 ( d Gmax - d Gmin ) / r d Gmin ≤ r , d Gmax ≥ r ( d Gmin + d Gmax ) / r - 1 d Gmin > r , wherein r is the radius of the range expressed by the user-entered geospatial search parameter and d Gmin and d Gmax are the minimum and maximum distances within the dataset from the central point; and using the proximity score to filter or order metadata records to create a listing of dataset results. 18. The method of claim 16 , further comprising: arranging the at least one identified metadata record in descending order by the calculated proximity rating, wherein said arranged metadata records create a listing of proximate dataset results; and presenting the proximate dataset results within a user interface.
| 0.636469 |
3. The method of claim 1 , wherein storing, in the bookmark database, resource identifiers identified for the first user comprises storing, in the bookmark database, a first set of resource identifiers for the first user, wherein the first set of resource identifiers includes the particular resource identifier.
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3. The method of claim 1 , wherein storing, in the bookmark database, resource identifiers identified for the first user comprises storing, in the bookmark database, a first set of resource identifiers for the first user, wherein the first set of resource identifiers includes the particular resource identifier. 4. The method of claim 3 , wherein storing the particular resource identifier for the second user comprises updating a second set of resource identifiers for the second user to include the particular resource identifier, wherein the second set of resource identifiers is stored in the bookmark database.
| 0.880878 |
1. A computer-implemented method comprising: generating, by a mobile device, an audio recording of (i) a question about an unidentified item of media content that a different device is playing in a vicinity of the mobile device, and (ii) environmental audio; in response to forwarding the audio recording to a front end server of a natural language processing system, receiving an answer to the question that is based on processing different portions of the audio recording by a speech recognition engine server associated with the natural language processing system and a content identification engine server associated with the natural language processing system; and in response to the question, providing, by the mobile device, the answer to the question about the unidentified item of media content.
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1. A computer-implemented method comprising: generating, by a mobile device, an audio recording of (i) a question about an unidentified item of media content that a different device is playing in a vicinity of the mobile device, and (ii) environmental audio; in response to forwarding the audio recording to a front end server of a natural language processing system, receiving an answer to the question that is based on processing different portions of the audio recording by a speech recognition engine server associated with the natural language processing system and a content identification engine server associated with the natural language processing system; and in response to the question, providing, by the mobile device, the answer to the question about the unidentified item of media content. 6. The computer-implemented method of claim 1 , wherein the speech recognition engine server associated with the natural language processing system and the content identification server associated with the natural language processing system are both the same server.
| 0.6 |
7. A non-transitory medium storing computer-executable program code, the program code executable by a computer to: receive first text, the first text comprising a first identifier and first additional text; store the first additional text in a memory area associated with the first identifier; receive second text, the second text comprising the first identifier and second additional text; store the second additional text in the memory area associated with the first identifier; determine that a characteristic of text stored in the memory area has reached a threshold, the text stored in the memory area comprising at least the first additional text and the second additional text; in response to the determination, generate a semantic representation of the text stored in the memory area; and store the semantic representation in association with the first identifier.
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7. A non-transitory medium storing computer-executable program code, the program code executable by a computer to: receive first text, the first text comprising a first identifier and first additional text; store the first additional text in a memory area associated with the first identifier; receive second text, the second text comprising the first identifier and second additional text; store the second additional text in the memory area associated with the first identifier; determine that a characteristic of text stored in the memory area has reached a threshold, the text stored in the memory area comprising at least the first additional text and the second additional text; in response to the determination, generate a semantic representation of the text stored in the memory area; and store the semantic representation in association with the first identifier. 10. A non-transitory medium according to claim 7 , the program code further executable by a computer to: receive third text before the second text is received, the third text comprising a second identifier and third additional text; store the third additional text in a second memory area associated with the second identifier before the second additional text is stored in the memory area associated with the first identifier; receive fourth text after storing the second text in the memory area associated with the first identifier, the fourth text comprising the second identifier and fourth additional text; store the fourth additional text in the second memory area associated with the second identifier; determine that a characteristic of text stored in the second memory area has reached a second threshold, the text stored in the second memory area comprising at least the third additional text and the fourth additional text; in response to the determination, generate a second semantic representation of the text stored in the second memory area; and store the second semantic representation in association with the second identifier.
| 0.623355 |
32. A computer-based system for parameterizing a steady-state model of an in-situ hydrocarbon reservoir, the model having a plurality of model parameters for mapping model input to model output through a stored representation of said reservoir, the system comprising: a computer, comprising: a processor; and a memory medium coupled to the processor; an input coupled to the processor and the memory medium, wherein the input is operable to receive a training data set comprising a plurality of input values and a plurality of target output values, wherein the training data set is representative of production operations of said reservoir; and an output coupled to the processor and the memory medium; wherein the memory medium stores program instructions which are executable by the processor to: receive a next at least one input value of the plurality of input values and a next target output value of the plurality of target output values; parameterize the model with a predetermined algorithm using said next at least one input value and said next target output value, and one or more derivative constraints, wherein the one or more derivative constraints are imposed to constrain relationships between the at least one input value and a resulting model output value, wherein said parameterizing comprises using an optimizer to perform constrained optimization on the plurality of model parameters to satisfy an objective function subject to the derivative constraints; iteratively perform said receiving and said parameterizing using the optimizer to generate a parameterized model, wherein the model comprises a model function, wherein the one or more derivative constraints comprise upper and/or lower bounds on one or more model function derivatives, wherein one or more of the model function derivatives comprise one or more of: a first order derivative of the model function, wherein the first order derivative represents inter-well transmissibilities; a second order derivative of the model function, wherein the second order derivative of the model function represents curvature of the inter-well transmissibilities; and/or a third order derivative of the model function, wherein the third order derivative of the model function represents rate of curvature of the inter-well transmissibilities; and store the parameterized model in the memory medium, wherein the parameterized model is usable to analyze reservoir operations; and wherein the output is operable to provide the parameterized model and/or the resulting model output values to other systems or processes to manage the reservoir operations.
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32. A computer-based system for parameterizing a steady-state model of an in-situ hydrocarbon reservoir, the model having a plurality of model parameters for mapping model input to model output through a stored representation of said reservoir, the system comprising: a computer, comprising: a processor; and a memory medium coupled to the processor; an input coupled to the processor and the memory medium, wherein the input is operable to receive a training data set comprising a plurality of input values and a plurality of target output values, wherein the training data set is representative of production operations of said reservoir; and an output coupled to the processor and the memory medium; wherein the memory medium stores program instructions which are executable by the processor to: receive a next at least one input value of the plurality of input values and a next target output value of the plurality of target output values; parameterize the model with a predetermined algorithm using said next at least one input value and said next target output value, and one or more derivative constraints, wherein the one or more derivative constraints are imposed to constrain relationships between the at least one input value and a resulting model output value, wherein said parameterizing comprises using an optimizer to perform constrained optimization on the plurality of model parameters to satisfy an objective function subject to the derivative constraints; iteratively perform said receiving and said parameterizing using the optimizer to generate a parameterized model, wherein the model comprises a model function, wherein the one or more derivative constraints comprise upper and/or lower bounds on one or more model function derivatives, wherein one or more of the model function derivatives comprise one or more of: a first order derivative of the model function, wherein the first order derivative represents inter-well transmissibilities; a second order derivative of the model function, wherein the second order derivative of the model function represents curvature of the inter-well transmissibilities; and/or a third order derivative of the model function, wherein the third order derivative of the model function represents rate of curvature of the inter-well transmissibilities; and store the parameterized model in the memory medium, wherein the parameterized model is usable to analyze reservoir operations; and wherein the output is operable to provide the parameterized model and/or the resulting model output values to other systems or processes to manage the reservoir operations. 38. The system of claim 32 , wherein said one or more model function derivatives further comprise: one or more fourth or higher order derivatives of the model function.
| 0.538554 |
1. A method for tracking a user, comprising: receiving a depth image that was captured by a depth camera; identifying an estimated location or position of a part of the user in the depth image; adjusting a model of the user based on the estimated location or position of the part of the user; and in response to a failure to identify a location or position of the part of the user in a second depth image, associating the part of the user with a portion of the second depth image based on a location or position of a default position of the part of the user.
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1. A method for tracking a user, comprising: receiving a depth image that was captured by a depth camera; identifying an estimated location or position of a part of the user in the depth image; adjusting a model of the user based on the estimated location or position of the part of the user; and in response to a failure to identify a location or position of the part of the user in a second depth image, associating the part of the user with a portion of the second depth image based on a location or position of a default position of the part of the user. 4. The method of claim 1 , wherein the model comprises a skeletal model having joints and bones.
| 0.673862 |
14. The signal processing apparatus according to claim 9 , wherein the selected representation domain is a transform domain.
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14. The signal processing apparatus according to claim 9 , wherein the selected representation domain is a transform domain. 15. The signal processing apparatus according to claim 14 , wherein the transform domain is any of discrete cosine transform, fast fourier transform, wavelets, and histogram.
| 0.939645 |
4. The method of claim 1 , wherein the item of data is in a mark-up language format.
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4. The method of claim 1 , wherein the item of data is in a mark-up language format. 6. The method of claim 4 , wherein the mobile communication device does not comprise a web browser.
| 0.982187 |
1. A method for matching speech data used to determine the similarity between an input speech data and a sample speech data, the method comprising: segmenting the input speech data into a plurality of input speech frames; segmenting the sample speech data into a plurality of sample speech frames; building a matching matrix, wherein each element of the matching matrix corresponds to one of the input speech frames and one of the sample speech frames and indicates a distance value between the corresponding input speech frame and the corresponding sample speech frame; determining a minimum value of the distance values indicated in each row of elements of the matching matrix, thereby obtaining a plurality of minimum distance values of the respective rows of elements of the matching matrix, determining a second least value of the distance values indicated in each row of elements of the matching matrix, thereby obtaining a plurality of second least distance values of the respective rows of elements of the matching matrix; summing up the minimum distance values and the second least distance value of the distance values indicated in each row of elements of the matching matrix, thereby obtaining a row score, determined by: row score = ∑ j = 1 r min r ⊗ C [ MM ( i , j ) ] + ∑ j = 1 r min r ⊗ C - i c [ MM ( i , j ) ] ; determining a minimum value of the distance values indicated in each column of elements of the matching matrix, thereby obtaining a plurality of another minimum distance values of the respective columns of elements of the matching matrix, determining a second least value of the distance values indicated in each column of elements of the matching matrix, thereby obtaining a plurality of second least distance values of the respective columns of elements of the matching matrix; summing up the another minimum distance values and the second least value of the indicated distance values in each column of elements of the matching matrix distance values, thereby obtaining a column score, wherein: column score = ∑ i = 1 e min j ∈ R [ MM ( i , j ) ] + ∑ i = 1 e min j ∈ R - j k [ MM ( i , j ) ] ; calculating a matching score obtained by combining the distance row score and the column score; and determining whether the input speech data and the sample speech data are similar according to the matching score.
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1. A method for matching speech data used to determine the similarity between an input speech data and a sample speech data, the method comprising: segmenting the input speech data into a plurality of input speech frames; segmenting the sample speech data into a plurality of sample speech frames; building a matching matrix, wherein each element of the matching matrix corresponds to one of the input speech frames and one of the sample speech frames and indicates a distance value between the corresponding input speech frame and the corresponding sample speech frame; determining a minimum value of the distance values indicated in each row of elements of the matching matrix, thereby obtaining a plurality of minimum distance values of the respective rows of elements of the matching matrix, determining a second least value of the distance values indicated in each row of elements of the matching matrix, thereby obtaining a plurality of second least distance values of the respective rows of elements of the matching matrix; summing up the minimum distance values and the second least distance value of the distance values indicated in each row of elements of the matching matrix, thereby obtaining a row score, determined by: row score = ∑ j = 1 r min r ⊗ C [ MM ( i , j ) ] + ∑ j = 1 r min r ⊗ C - i c [ MM ( i , j ) ] ; determining a minimum value of the distance values indicated in each column of elements of the matching matrix, thereby obtaining a plurality of another minimum distance values of the respective columns of elements of the matching matrix, determining a second least value of the distance values indicated in each column of elements of the matching matrix, thereby obtaining a plurality of second least distance values of the respective columns of elements of the matching matrix; summing up the another minimum distance values and the second least value of the indicated distance values in each column of elements of the matching matrix distance values, thereby obtaining a column score, wherein: column score = ∑ i = 1 e min j ∈ R [ MM ( i , j ) ] + ∑ i = 1 e min j ∈ R - j k [ MM ( i , j ) ] ; calculating a matching score obtained by combining the distance row score and the column score; and determining whether the input speech data and the sample speech data are similar according to the matching score. 9. The method as claimed in claim 1 , wherein the matching score is equal to the product of the column score and a fifth weighted value plus the product of the row score, a sixth weighted value and a normalization parameter.
| 0.524429 |
9. 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: determining an initial height and an initial width of an image that is associated with a search result, wherein the image is to be displayed in the search result in line with text associated with the search result, and wherein the search result includes an in-line image region for displaying the image in line with the text, and a text region for displaying the text; determining an initial height of the text region of the search result based at least on (i) the text associated with the search result, and (ii) an initial width of the text region; determining an initial width of the in-line image region of the search result based at least on (i) the initial height of the text region of the search result, (ii) the initial height of the image, and (iii) the initial width of the image; determining that a) space in the in-line image region or space in the text region will include undesired whitespace, or b) text associated with the search result and for presentation in the search result will be removed from the text region; and in response to determining that a) space in the in-line image region or space in the text region will include undesired whitespace, or b) text associated with the search result and for presentation in the search result will be removed from the text region: determining an adjusted height of the text region of the search result based at least on the initial width of the in-line image region of the search result; determining an adjusted width of the in-line image region of the search result based at least on the adjusted height of the text region of the search result; determining an adjusted height and an adjusted width of the image based at least on the adjusted width of the in-line image region of the search result; scaling at least a portion of the image based at least on the adjusted height and the adjusted width of the image; and outputting the scaled image for display in the search result in line with the text.
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9. 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: determining an initial height and an initial width of an image that is associated with a search result, wherein the image is to be displayed in the search result in line with text associated with the search result, and wherein the search result includes an in-line image region for displaying the image in line with the text, and a text region for displaying the text; determining an initial height of the text region of the search result based at least on (i) the text associated with the search result, and (ii) an initial width of the text region; determining an initial width of the in-line image region of the search result based at least on (i) the initial height of the text region of the search result, (ii) the initial height of the image, and (iii) the initial width of the image; determining that a) space in the in-line image region or space in the text region will include undesired whitespace, or b) text associated with the search result and for presentation in the search result will be removed from the text region; and in response to determining that a) space in the in-line image region or space in the text region will include undesired whitespace, or b) text associated with the search result and for presentation in the search result will be removed from the text region: determining an adjusted height of the text region of the search result based at least on the initial width of the in-line image region of the search result; determining an adjusted width of the in-line image region of the search result based at least on the adjusted height of the text region of the search result; determining an adjusted height and an adjusted width of the image based at least on the adjusted width of the in-line image region of the search result; scaling at least a portion of the image based at least on the adjusted height and the adjusted width of the image; and outputting the scaled image for display in the search result in line with the text. 12. The system of claim 9 , wherein determining an adjusted height and an adjusted width of the image comprises: determining that the adjusted height of the text region is outside of a predetermined range of the adjusted height of the image region; and outputting a default height and a default width as the adjusted height and the adjusted width of the image, respectively.
| 0.773058 |
18. The computer-readable storage device of claim 17 , the method further comprising computing an alignment score of a correspondence set as a function of alignment scores of word pairs in the correspondence set.
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18. The computer-readable storage device of claim 17 , the method further comprising computing an alignment score of a correspondence set as a function of alignment scores of word pairs in the correspondence set. 19. The computer-readable storage device of claim 18 , the method further comprising computing an alignment score for consistency of words in the correspondence set.
| 0.832019 |
13. An electronic device for generating a notification, comprising: a sound sensor configured to receive a speech phrase; a speech recognition unit configured to recognize the speech phrase as a command to generate the notification; a sensor unit configured to detect context data of the electronic device; a processor configured to determine a context score associated with the context data and to determine whether to generate the notification at least based on a comparison of the context score to a threshold value; and an output unit configured to generate the notification based on the comparison.
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13. An electronic device for generating a notification, comprising: a sound sensor configured to receive a speech phrase; a speech recognition unit configured to recognize the speech phrase as a command to generate the notification; a sensor unit configured to detect context data of the electronic device; a processor configured to determine a context score associated with the context data and to determine whether to generate the notification at least based on a comparison of the context score to a threshold value; and an output unit configured to generate the notification based on the comparison. 18. The electronic device of claim 13 , wherein the sound sensor is further configured to receive a first speech phrase and a second speech phrase as the speech phrase, and wherein the speech recognition unit is further configured to recognize the speech phrase as a command to generate the notification in response to determining that the first speech phrase and the second speech phrase are received within a predetermined time period.
| 0.5 |
6. A computer program product for displaying search results for a search query comprising a plurality of search terms, said computer program product comprising a non-transitory computer readable medium having computer readable program code embodied therewith, said computer readable program code comprising: computer readable program code configured to receive said search query; computer readable program code configured to obtain at least one document satisfying said search query; computer readable program code configured to determine a relative position of at least two of said search terms in said at least one document; and computer readable program code configured to present at least a portion of said at least one document with a visual indication of said relative position of said at least two search terms in said at least one document, wherein said visual indication indicates a count of one or more intervening elements between said at least two search terms, wherein said relative position is indicated using a predefined character to indicate said one or more intervening elements between said at least two search terms and wherein said one or more intervening elements indicated by each predefined character comprises a given one of a character, word, line, paragraph and page.
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6. A computer program product for displaying search results for a search query comprising a plurality of search terms, said computer program product comprising a non-transitory computer readable medium having computer readable program code embodied therewith, said computer readable program code comprising: computer readable program code configured to receive said search query; computer readable program code configured to obtain at least one document satisfying said search query; computer readable program code configured to determine a relative position of at least two of said search terms in said at least one document; and computer readable program code configured to present at least a portion of said at least one document with a visual indication of said relative position of said at least two search terms in said at least one document, wherein said visual indication indicates a count of one or more intervening elements between said at least two search terms, wherein said relative position is indicated using a predefined character to indicate said one or more intervening elements between said at least two search terms and wherein said one or more intervening elements indicated by each predefined character comprises a given one of a character, word, line, paragraph and page. 8. The computer program product of claim 6 , further comprising the step of presenting a relevance ranking that is based on said relative position of said at least two search terms.
| 0.504154 |
8. The method of claim 6 , wherein removing the at least one word further comprises determining that a last web page visited is from a same site as the current web page; identifying at least one word in the title of the last web page that is common with a word in the title of the current web page; removing the at least one common word from the beginning of the title of the current page; determining that a modified title does not fit into the title display area; and removing at least another common word from the beginning of the title.
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8. The method of claim 6 , wherein removing the at least one word further comprises determining that a last web page visited is from a same site as the current web page; identifying at least one word in the title of the last web page that is common with a word in the title of the current web page; removing the at least one common word from the beginning of the title of the current page; determining that a modified title does not fit into the title display area; and removing at least another common word from the beginning of the title. 10. The method of claim 8 , wherein removing the at least one word further comprises: determining that the modified title does not fit into the title display area; determining that no more words in common are present in the modified title; and removing at least one character from an end word of the modified title.
| 0.711872 |
10. A computer storage device storing computer-executable instructions that, when executed on one or more processors, perform operations comprising: segmenting an image into objects; assigning a value to each of the segmented objects, wherein the value is corresponds to a likelihood of an object being recognized by humans; identifying an object such that the image surrounding the object visually associates the object as being a part of the image; selecting the object for the object recognition; cropping the object from the image, wherein the object is expanded beyond a boundary of the image without conveying a contour of the object; filling a region on the image where the object has been cropped; and generating candidate objects that have similar low level features to the object cropped from the image, wherein the low level features are features that are determined to be recognizable by a computer.
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10. A computer storage device storing computer-executable instructions that, when executed on one or more processors, perform operations comprising: segmenting an image into objects; assigning a value to each of the segmented objects, wherein the value is corresponds to a likelihood of an object being recognized by humans; identifying an object such that the image surrounding the object visually associates the object as being a part of the image; selecting the object for the object recognition; cropping the object from the image, wherein the object is expanded beyond a boundary of the image without conveying a contour of the object; filling a region on the image where the object has been cropped; and generating candidate objects that have similar low level features to the object cropped from the image, wherein the low level features are features that are determined to be recognizable by a computer. 14. The computer storage device of claim 10 , wherein selecting the object comprises at least one of: identifying the object based at least in part on an equal probability that the objects have a same probability of being selected; identifying the object having a significant value based at least in part on a weighted probability that the object with a higher significance value has a greater probability of being selected; and identifying the object having the significant value based at least in part on a probability that the object to be identified follows a probability distribution of significant values of the objects.
| 0.712917 |
1. A method for integrating objects defined by different type systems into a single integrated object oriented system, said method comprising the steps of: providing an integrated object oriented system comprising, an integrated type system that supports a superset of features from a plurality of foreign object systems, said foreign object systems comprising a plurality of foreign objects defined by foreign type systems that are different from said integrated type system, said foreign objects including at least one method; receiving into said integrated object oriented system a plurality of said foreign objects from said different foreign object systems; converting said foreign objects into uniform object model objects defined by said integrated type system; and executing said foreign objects as uniform object model objects in a run time environment without loss of features provided by said foreign objects.
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1. A method for integrating objects defined by different type systems into a single integrated object oriented system, said method comprising the steps of: providing an integrated object oriented system comprising, an integrated type system that supports a superset of features from a plurality of foreign object systems, said foreign object systems comprising a plurality of foreign objects defined by foreign type systems that are different from said integrated type system, said foreign objects including at least one method; receiving into said integrated object oriented system a plurality of said foreign objects from said different foreign object systems; converting said foreign objects into uniform object model objects defined by said integrated type system; and executing said foreign objects as uniform object model objects in a run time environment without loss of features provided by said foreign objects. 9. The method as set forth in claim 1, further comprising the step of storing said uniform object model objects in at least one data source to provide persistence for all of said uniform object model objects.
| 0.713836 |
4. The method of claim 1 , wherein obtaining the translation of the text comprises: providing the text to a translation module in communication with the operating system; and receiving, from the translation module, the translation of the text into the second language.
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4. The method of claim 1 , wherein obtaining the translation of the text comprises: providing the text to a translation module in communication with the operating system; and receiving, from the translation module, the translation of the text into the second language. 5. The method of claim 4 , wherein obtaining the voice output comprises utilizing a speech synthesizer to convert the translation of the text into a synthesized voice in the second language.
| 0.926026 |
21. A system for processing and classifying a communication involving at least one human, said system comprising: means for processing said communication to detect one or more composite words in a given context C, wherein each composite word is a predefined sequence of words S pre-programmed as an atomic unit to prevent partial detection; means for estimating a posterior probability P(S|C,X), wherein X is an acoustics vector sequence and wherein said posterior probability P(S|C,X)=P(X|C,S)P(S)/P(X C,S)+P(X|C,S)), wherein S is an anti model of S; means for determining an overall confidence level of events expressed in said one or more composite words occurring in said communication based on said posterior probability; and means for classifying said communication into one or more categories based on detected events in accordance with said overall confidence level.
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21. A system for processing and classifying a communication involving at least one human, said system comprising: means for processing said communication to detect one or more composite words in a given context C, wherein each composite word is a predefined sequence of words S pre-programmed as an atomic unit to prevent partial detection; means for estimating a posterior probability P(S|C,X), wherein X is an acoustics vector sequence and wherein said posterior probability P(S|C,X)=P(X|C,S)P(S)/P(X C,S)+P(X|C,S)), wherein S is an anti model of S; means for determining an overall confidence level of events expressed in said one or more composite words occurring in said communication based on said posterior probability; and means for classifying said communication into one or more categories based on detected events in accordance with said overall confidence level. 29. The system of claim 21 , further comprising: means for defining said events by examples, words, recordings, or user input.
| 0.629691 |
7. The computer system of claim 1 , wherein the potential data quality problem is multi-valued properties; and said determining potential data quality problems further comprises identifying respective objects that have multiple property values for individual properties.
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7. The computer system of claim 1 , wherein the potential data quality problem is multi-valued properties; and said determining potential data quality problems further comprises identifying respective objects that have multiple property values for individual properties. 8. The computer system of claim 7 , wherein the resolution comprises an indication of one of the multiple property values that is correct for ones of the properties; and said implementing the resolution further comprises removing all of the multiple property values except for the one of the multiple property values that is correct for respective properties.
| 0.881579 |
10. A method, implemented by a computing system comprising one or more processors, the method comprising: providing, using one or more of the processors, a corpus in a language by non-native users of the language; measuring, using one or more of the processors, characteristics of the corpus; using the characteristics to create, using one or more of the processors, a classifier for indicating non-native usage of the language; receiving an input, and using the classifier on the input for indicating non-native usage; adding the input indicated as non-native usage to the corpus; re-measuring the characteristics of the corpus; and using the re-measured characteristics to create a refined classifier for indicating non-native usage of the language; in which at least one of the first classifier and the refined classifier is prepared by a classification algorithm that comprises a support vector machine.
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10. A method, implemented by a computing system comprising one or more processors, the method comprising: providing, using one or more of the processors, a corpus in a language by non-native users of the language; measuring, using one or more of the processors, characteristics of the corpus; using the characteristics to create, using one or more of the processors, a classifier for indicating non-native usage of the language; receiving an input, and using the classifier on the input for indicating non-native usage; adding the input indicated as non-native usage to the corpus; re-measuring the characteristics of the corpus; and using the re-measured characteristics to create a refined classifier for indicating non-native usage of the language; in which at least one of the first classifier and the refined classifier is prepared by a classification algorithm that comprises a support vector machine. 13. The method of claim 10 , wherein the characteristics measured comprise parse tree segments.
| 0.771079 |
13. The non-transitory computer-readable storage medium of claim 12 , wherein searching for the non-standard payee name in the payee data store involves: responsive to the non-standard payee name being a user-defined payee name, determining whether a normalized payee name which meets specified criteria is associated with the user-defined payee name; and responsive to the normalized payee name being associated with the user-defined payee name, using the determined normalized payee name to recursively search through the plurality of non-standard payee names associated with the standard payee name to determine whether the non-standard payee name that is associated with the standard payee name.
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13. The non-transitory computer-readable storage medium of claim 12 , wherein searching for the non-standard payee name in the payee data store involves: responsive to the non-standard payee name being a user-defined payee name, determining whether a normalized payee name which meets specified criteria is associated with the user-defined payee name; and responsive to the normalized payee name being associated with the user-defined payee name, using the determined normalized payee name to recursively search through the plurality of non-standard payee names associated with the standard payee name to determine whether the non-standard payee name that is associated with the standard payee name. 14. The non-transitory computer-readable storage medium of claim 13 , wherein responsive to a normalized payee name which meets the specified criteria does not exist, the method further comprises using the user-defined payee name to recursively search through the plurality of non-standard payee names associated with a standard payee name to determine whether the non-standard payee name is associated with the standard payee name.
| 0.721381 |
16. The system of claim 13 , wherein the correlator component causes the first query and the second query to be correlated in a database of substantially similar queries.
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16. The system of claim 13 , wherein the correlator component causes the first query and the second query to be correlated in a database of substantially similar queries. 17. The system of claim 16 , the plurality of components further comprising: a search component that receives the first query from a user, accesses the database of substantially similar queries and determines that the second query is a substantially similar query to the first query, and automatically executes a search using the second query.
| 0.912999 |
1. A computer-implemented method to generate cumulative metric data for a test in a test environment, the method comprising: specifying, from within the test environment, a plurality of test elements within an iterations part of the test, the plurality of test elements including: a first test element being a simulation model having a block property, the simulation model executed from within a second environment that is separate from the test environment, and a second test element that is not a simulation model, the second test element being executable; generating, from within the test environment, at least one test condition for the test, the at least one test condition specifying a number of values for the block property of the simulation model; specifying at least one metric setting for the simulation model; defining a test variable within the test environment; mapping, from within the test environment, the at least one metric setting of the simulation model to the test variable; running, by a computer, the test from within the test environment such that the simulation model is executed within the second environment for a plurality of iterations based on the number of block property values specified in the at least one test condition, and the second test element is executed for the plurality of iterations; generating, during each iteration of the simulation model, metric data based on the at least one metric setting for the simulation model, the metric data generated within the second environment; assigning the metric data generated in the second environment to the test variable of the test environment as a result of the mapping of the at least one metric setting of the simulation model to the test variable so that the metric data from the iterations of the simulation model is accumulated; and providing access to the accumulated metric data from within the test environment.
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1. A computer-implemented method to generate cumulative metric data for a test in a test environment, the method comprising: specifying, from within the test environment, a plurality of test elements within an iterations part of the test, the plurality of test elements including: a first test element being a simulation model having a block property, the simulation model executed from within a second environment that is separate from the test environment, and a second test element that is not a simulation model, the second test element being executable; generating, from within the test environment, at least one test condition for the test, the at least one test condition specifying a number of values for the block property of the simulation model; specifying at least one metric setting for the simulation model; defining a test variable within the test environment; mapping, from within the test environment, the at least one metric setting of the simulation model to the test variable; running, by a computer, the test from within the test environment such that the simulation model is executed within the second environment for a plurality of iterations based on the number of block property values specified in the at least one test condition, and the second test element is executed for the plurality of iterations; generating, during each iteration of the simulation model, metric data based on the at least one metric setting for the simulation model, the metric data generated within the second environment; assigning the metric data generated in the second environment to the test variable of the test environment as a result of the mapping of the at least one metric setting of the simulation model to the test variable so that the metric data from the iterations of the simulation model is accumulated; and providing access to the accumulated metric data from within the test environment. 8. The method as in claim 1 , further comprising: designating a portion of the simulation model for which the metric data is to be generated.
| 0.847312 |
1. An information processing apparatus comprising: a calculation section configured to calculate similarity among a plurality of documents; an identification section configured to identify, in response to a change made to a first document, a second document having a similarity greater than or equal to a first criterion as compared to the first document before the change; a notification section configured to notify a user associated with the second document that the first document has been changed, in response to the change to the first document; and a necessity acquiring section configured to acquire necessity information indicating whether one or more other documents need to be changed due to the change to the first document, wherein the notification section further notifies the user associated with the second document of the necessity information.
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1. An information processing apparatus comprising: a calculation section configured to calculate similarity among a plurality of documents; an identification section configured to identify, in response to a change made to a first document, a second document having a similarity greater than or equal to a first criterion as compared to the first document before the change; a notification section configured to notify a user associated with the second document that the first document has been changed, in response to the change to the first document; and a necessity acquiring section configured to acquire necessity information indicating whether one or more other documents need to be changed due to the change to the first document, wherein the notification section further notifies the user associated with the second document of the necessity information. 3. The information processing apparatus according to claim 1 , further comprising a group generation section configured to group together into a group two or more documents, among the plurality of documents, having a similarity greater than or equal to the first criterion, wherein the identification section is configured to identify the second document that belongs to the same group as that of the first document in response to the change made to the first document.
| 0.522173 |
1. A method for dynamically detecting topics during one of a speech and a call center conversation, comprising: predefining one or more keywords; associating information with the one or more keywords; detecting at least one of the one or more keywords during the speech or the call center conversation; after the predefining, the associating and detecting, checking whether one or more rules are associated with the one or more detected keywords; if so, processing the one or more rules; and further comprising one of: during the speech, utilizing an intelligent teleprompter and displaying the detected keywords and the information associated with the keywords and dynamically altering one or more panes of information of the intelligent teleprompter based upon the detected one or more keywords; and during the call center conversation, displaying the detected keywords and the information associated with the keywords uttered by a caller to a call center agent and displaying an indication that a topic area has been covered by removing or graying out a displayed word.
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1. A method for dynamically detecting topics during one of a speech and a call center conversation, comprising: predefining one or more keywords; associating information with the one or more keywords; detecting at least one of the one or more keywords during the speech or the call center conversation; after the predefining, the associating and detecting, checking whether one or more rules are associated with the one or more detected keywords; if so, processing the one or more rules; and further comprising one of: during the speech, utilizing an intelligent teleprompter and displaying the detected keywords and the information associated with the keywords and dynamically altering one or more panes of information of the intelligent teleprompter based upon the detected one or more keywords; and during the call center conversation, displaying the detected keywords and the information associated with the keywords uttered by a caller to a call center agent and displaying an indication that a topic area has been covered by removing or graying out a displayed word. 7. The method of claim 1 , wherein the one or more keywords comprise one or more phrases and the detecting detects the one or more phrases.
| 0.555042 |
8. A system, comprising: at least one processor; a computer-readable storage medium coupled to the at least one processor having instructions stored thereon which, when executed by the at least one processor, cause the at least one processor to perform operations comprising: identifying, by the at least one processor, at a location remote from a first application, a request for localization associated with a particular user of a string value associated with the first application from a source language to a target language, the request received via a network connection; transmitting, via the network connection, the string value to a translation request buffer in response to a determination that the localization of the string value in the target language is unavailable, wherein the translation is delayed until a determination of satisfaction of at least one heuristic analysis; and automatically triggering a translation process of the string value from the source language into the target language, the string value retrieved from the translation request buffer, the triggering performed in response to satisfaction of at least one heuristic analysis, where a first heuristic analysis is satisfied based on receiving the identified request and having previously received at least one prior request for localization of the same string value from the source language to the target language, where the number of received requests exceeds a predetermined threshold value of requests, and wherein a second heuristic analysis is satisfied based on a determination that an identity or role of the particular user associated with the request for localization corresponds to a priority translation, wherein the second heuristic analysis causes the automatic triggering of the translation process regardless of whether the first heuristic analysis is satisfied; and wherein automatically triggering the translation process of the string value from the source language into the target language in response to the satisfaction of the at least one heuristic analysis further includes automatically triggering the translation process of the string value from the source language into the target language and at least one other particular language different than the target language based on the automatic triggering of the translation process in the target language, where the at least one other particular language is not included in the identified request for localization.
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8. A system, comprising: at least one processor; a computer-readable storage medium coupled to the at least one processor having instructions stored thereon which, when executed by the at least one processor, cause the at least one processor to perform operations comprising: identifying, by the at least one processor, at a location remote from a first application, a request for localization associated with a particular user of a string value associated with the first application from a source language to a target language, the request received via a network connection; transmitting, via the network connection, the string value to a translation request buffer in response to a determination that the localization of the string value in the target language is unavailable, wherein the translation is delayed until a determination of satisfaction of at least one heuristic analysis; and automatically triggering a translation process of the string value from the source language into the target language, the string value retrieved from the translation request buffer, the triggering performed in response to satisfaction of at least one heuristic analysis, where a first heuristic analysis is satisfied based on receiving the identified request and having previously received at least one prior request for localization of the same string value from the source language to the target language, where the number of received requests exceeds a predetermined threshold value of requests, and wherein a second heuristic analysis is satisfied based on a determination that an identity or role of the particular user associated with the request for localization corresponds to a priority translation, wherein the second heuristic analysis causes the automatic triggering of the translation process regardless of whether the first heuristic analysis is satisfied; and wherein automatically triggering the translation process of the string value from the source language into the target language in response to the satisfaction of the at least one heuristic analysis further includes automatically triggering the translation process of the string value from the source language into the target language and at least one other particular language different than the target language based on the automatic triggering of the translation process in the target language, where the at least one other particular language is not included in the identified request for localization. 10. The system of claim 8 , the operations further comprising storing the translated string value in the target language to a translation repository, the translated string value in the target language associated with the string value in the source language in the translation repository.
| 0.695958 |
13. A non-transitory computer readable medium having stored thereon comp r executable instructions for testing a computer application, the instructions comprising: identifying components of a version of the application, said components including one or more components that are one of new and modified, wherein each of the one or more components corresponds to a keyword; generating a keyword matrix of the identified application components, the keyword matrix having a set of all identified application components as a first dimension and a set of the one or more components that are one of new and modified as a second dimension, wherein the keyword matrix comprises the keywords; performing a search in a test script repository with respect to components listed as at least one of the first and second dimensions, said test script repository including test scripts referencing at least some of the identified components, and determining a result of the search; populating the keyword matrix with test case identification numbers in the search result, the test case identification numbers corresponding to test scripts that refer to the at least some of the identified components of the application; and based on the populated keyword matrix, identifying one or more of (a) gaps in test case coverage for the version of the application, and (b) one or more test cases covering the version of the application.
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13. A non-transitory computer readable medium having stored thereon comp r executable instructions for testing a computer application, the instructions comprising: identifying components of a version of the application, said components including one or more components that are one of new and modified, wherein each of the one or more components corresponds to a keyword; generating a keyword matrix of the identified application components, the keyword matrix having a set of all identified application components as a first dimension and a set of the one or more components that are one of new and modified as a second dimension, wherein the keyword matrix comprises the keywords; performing a search in a test script repository with respect to components listed as at least one of the first and second dimensions, said test script repository including test scripts referencing at least some of the identified components, and determining a result of the search; populating the keyword matrix with test case identification numbers in the search result, the test case identification numbers corresponding to test scripts that refer to the at least some of the identified components of the application; and based on the populated keyword matrix, identifying one or more of (a) gaps in test case coverage for the version of the application, and (b) one or more test cases covering the version of the application. 14. The computer readable medium of claim 13 wherein the instructions further comprise executing the one or more test cases covering the version of the application.
| 0.570213 |
20. The method of claim 1 wherein the chain of nodes begins with an origin node comprising a term of interest.
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20. The method of claim 1 wherein the chain of nodes begins with an origin node comprising a term of interest. 21. The method of claim 20 wherein the nodes are added to the chain of nodes until at least one of the following occurs: a user halts correlation; a set time expires; said chain of nodes comprises a number of nodes greater than a specified number; no further nodes in the node pool can be associated with the chain of nodes; a pre-selected term from a target node is added to the correlation; and a pre-selected target node is added to the correlation.
| 0.849743 |
17. The method of claim 1 : wherein the indices are stored within a predictive database system of a host organization; and wherein the method further comprises: receiving a plurality of access requests for indices stored within the predictive database system of the host organization, each of the access requests originating from one or more client devices of a plurality of customer organizations, wherein each customer organization is selected from the group consisting of: a separate and distinct remote organization, an organizational group within the host organization, a business partner of the host organization, or a customer organization that subscribes to cloud computing services provided by the host organization.
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17. The method of claim 1 : wherein the indices are stored within a predictive database system of a host organization; and wherein the method further comprises: receiving a plurality of access requests for indices stored within the predictive database system of the host organization, each of the access requests originating from one or more client devices of a plurality of customer organizations, wherein each customer organization is selected from the group consisting of: a separate and distinct remote organization, an organizational group within the host organization, a business partner of the host organization, or a customer organization that subscribes to cloud computing services provided by the host organization. 18. The method of claim 17 , wherein the predictive database system is operationally integrated with a multi-tenant database system provided by the host organization, the multi-tenant database system having elements of hardware and software that are shared by a plurality of separate and distinct customer organizations, each of the separate and distinct customer organizations being remotely located from the host organization having the predictive database system and the multi-tenant database system operating therein.
| 0.788779 |
5. Apparatus for generating signals for operating a first computer system to recognize spoken text, comprising: conversion means for converting the spoken text uttered by a speaker into first digital text data which represent the spoken text; a speech recognition unit, including: lexicon data means for storing lexicon data which represent a lexicon stored in the lexicon data device, and; language model data means for storing language model data which represent a language model; reference data means for storing reference data which represent phonemes; speech recognition means to generate second digital text data which represent recognized text, in a speech recognition process depending on the first digital text data, the lexicon data, the language model data, and the reference data; means for obtaining third digital text data representing error correction data; and error correction means for correcting the recognized text represented by the second digital text data depending on the third digital text data, by changing a part of the second digital text data depending on the third digital text data, and to generate fourth digital text data which represent corrected text; adaptation means for adapting the speech recognition unit based on digital text data, including: means for adapting the lexicon data to the speaker depending on digital text data and storing the adapted lexicon data in the lexicon data means; means for adapting the language model data to the speaker depending on digital text data and storing the adapted language model data in the language model data means: and means for adapting the reference data to the speaker depending on digital text data and storing the adapted reference data in the reference data means; and re-adaption means, including: means for converting the first digital data into fifth digital data which represent newly recognized text, using the speech recognition unit after the adaptation means had adapted the speech recognition data depending on the fourth digital data; and the adaptation means being for adapting the available reference data to the speaker of the spoken text depending on the fifth digital data and the first digital data.
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5. Apparatus for generating signals for operating a first computer system to recognize spoken text, comprising: conversion means for converting the spoken text uttered by a speaker into first digital text data which represent the spoken text; a speech recognition unit, including: lexicon data means for storing lexicon data which represent a lexicon stored in the lexicon data device, and; language model data means for storing language model data which represent a language model; reference data means for storing reference data which represent phonemes; speech recognition means to generate second digital text data which represent recognized text, in a speech recognition process depending on the first digital text data, the lexicon data, the language model data, and the reference data; means for obtaining third digital text data representing error correction data; and error correction means for correcting the recognized text represented by the second digital text data depending on the third digital text data, by changing a part of the second digital text data depending on the third digital text data, and to generate fourth digital text data which represent corrected text; adaptation means for adapting the speech recognition unit based on digital text data, including: means for adapting the lexicon data to the speaker depending on digital text data and storing the adapted lexicon data in the lexicon data means; means for adapting the language model data to the speaker depending on digital text data and storing the adapted language model data in the language model data means: and means for adapting the reference data to the speaker depending on digital text data and storing the adapted reference data in the reference data means; and re-adaption means, including: means for converting the first digital data into fifth digital data which represent newly recognized text, using the speech recognition unit after the adaptation means had adapted the speech recognition data depending on the fourth digital data; and the adaptation means being for adapting the available reference data to the speaker of the spoken text depending on the fifth digital data and the first digital data. 7. The apparatus of claim 5, comprising a network including a second computer system and means for communication in between the first computer system and second computer system.
| 0.542975 |
1. A computer-implemented method comprising: generating a suggested alert definition for a notification application, the notification application configured to maintain active alert definitions for a user, wherein an active alert definition of the notification application specifies data to monitor and an alert trigger condition to cause the notification application to generate a corresponding alert notification for the user, wherein providing the suggested alert definition comprises: accessing a first data set of captured user interactions with a client computing device from electronic memory storage, wherein the first data set comprises information regarding user interaction with an application of the client computing device that is independent of the notification application, and using a processor to analyze the first data set, and generate a suggested alert definition for the user based on the analysis of the first data set, the suggested alert definition specifying data to monitor and a trigger condition for suggested conversion to an active alert of the notification application; and providing computer-readable code to display the suggested alert definition on a computing device display.
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1. A computer-implemented method comprising: generating a suggested alert definition for a notification application, the notification application configured to maintain active alert definitions for a user, wherein an active alert definition of the notification application specifies data to monitor and an alert trigger condition to cause the notification application to generate a corresponding alert notification for the user, wherein providing the suggested alert definition comprises: accessing a first data set of captured user interactions with a client computing device from electronic memory storage, wherein the first data set comprises information regarding user interaction with an application of the client computing device that is independent of the notification application, and using a processor to analyze the first data set, and generate a suggested alert definition for the user based on the analysis of the first data set, the suggested alert definition specifying data to monitor and a trigger condition for suggested conversion to an active alert of the notification application; and providing computer-readable code to display the suggested alert definition on a computing device display. 10. The method of claim 1 , further comprising: prior to delivering the suggested alert definition to the notification application, querying the user regarding the trigger condition and receiving a trigger condition user input establishing the trigger condition.
| 0.683281 |
1. A computer implemented method for retrieving solutions that solve a problem experienced by a user, the computer implemented method comprising: generating, by a computer, a candidate solution document set for solving the problem, wherein a customized solution procedure for solving the problem is generated by the computer from a plurality of stored solution documents, and wherein a modified solution procedure with another set of instruction steps is generated by the computer for solving the problem based on the computer receiving an input rejecting one or more instruction steps included in the customized solution procedure; generating, by the computer, a document object model tree for the generated candidate solution document set; simplifying, by the computer, the generated document object model tree for the generated candidate solution document set by filtering out nodes in the generated document object model tree that do not have structural effects; generating, by the computer, a template based on the simplified document object model tree; calculating, by the computer, a structural similarity score for solution documents by comparing document object model trees of the solution documents with the generated template; determining, by the computer, whether the structural similarity score for the solution documents is greater than a predetermined threshold; responsive to the computer determining that the structural similarity score is greater than the predetermined threshold, storing, by the computer, the solution documents with structural similarity scores greater than the predetermined threshold; responsive to the computer receiving a query describing the problem, sending, by the computer, relevant candidate solutions to the problem, wherein the relevant candidate solutions include unstructured hypertext markup language solution documents found on a world wide web, and wherein the unstructured hypertext markup language solution documents include solution data found in web logs, instant messaging chat sessions, and online message boards; responsive to the computer receiving a selection of one relevant candidate solution from the relevant candidate solutions, analyzing, by the computer, instructions steps within the one relevant candidate solution selected; calculating, by the computer, an instruction step similarity between the instruction steps within the one relevant candidate solution selected and other instructions steps within the stored solution documents; and sending, by the computer, similar solutions containing similar instruction steps to the instruction steps contained within the one relevant candidate solution selected based on the calculated instruction step similarity.
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1. A computer implemented method for retrieving solutions that solve a problem experienced by a user, the computer implemented method comprising: generating, by a computer, a candidate solution document set for solving the problem, wherein a customized solution procedure for solving the problem is generated by the computer from a plurality of stored solution documents, and wherein a modified solution procedure with another set of instruction steps is generated by the computer for solving the problem based on the computer receiving an input rejecting one or more instruction steps included in the customized solution procedure; generating, by the computer, a document object model tree for the generated candidate solution document set; simplifying, by the computer, the generated document object model tree for the generated candidate solution document set by filtering out nodes in the generated document object model tree that do not have structural effects; generating, by the computer, a template based on the simplified document object model tree; calculating, by the computer, a structural similarity score for solution documents by comparing document object model trees of the solution documents with the generated template; determining, by the computer, whether the structural similarity score for the solution documents is greater than a predetermined threshold; responsive to the computer determining that the structural similarity score is greater than the predetermined threshold, storing, by the computer, the solution documents with structural similarity scores greater than the predetermined threshold; responsive to the computer receiving a query describing the problem, sending, by the computer, relevant candidate solutions to the problem, wherein the relevant candidate solutions include unstructured hypertext markup language solution documents found on a world wide web, and wherein the unstructured hypertext markup language solution documents include solution data found in web logs, instant messaging chat sessions, and online message boards; responsive to the computer receiving a selection of one relevant candidate solution from the relevant candidate solutions, analyzing, by the computer, instructions steps within the one relevant candidate solution selected; calculating, by the computer, an instruction step similarity between the instruction steps within the one relevant candidate solution selected and other instructions steps within the stored solution documents; and sending, by the computer, similar solutions containing similar instruction steps to the instruction steps contained within the one relevant candidate solution selected based on the calculated instruction step similarity. 2. The computer implemented method of claim 1 , further comprising: responsive to the computer receiving the query describing the problem, retrieving, by the computer, a query history associated with the user; determining, by the computer, whether a problem topic taxonomy is available; responsive to the computer determining that a problem topic taxonomy is available, determining, by the computer, a taxonomy-based context for the problem based on a description of the problem within the query and the retrieved query history associated with the user; generating, by the computer, a context for the problem; translating, by the computer, the query into an internal form that includes original terms in the query and the generated context for the problem; and accessing, by the computer, full-text indices using the translated query to determine a textual relevance of candidate solutions to the problem.
| 0.540456 |
10. The short film generation/reproduction apparatus according to claim 9 , wherein the scenario generation unit includes: an effect arrangement unit operable to select effects one by one from among the predetermined number of effects included in the style, and to arrange the selected effects one by one in a time domain; a still picture assignment unit operable to assign a still picture to each of the effects arranged in the time domain by the effect arrangement unit on the basis of the object information, the still picture satisfying a picture feature required by the respective effects; and a parameter setting unit operable to generate the scenario by describing a parameter indicating processing to be performed on the object suitable for each of the effects arranged in the time domain by the effect arrangement unit, and to store the generated scenario in the database unit.
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10. The short film generation/reproduction apparatus according to claim 9 , wherein the scenario generation unit includes: an effect arrangement unit operable to select effects one by one from among the predetermined number of effects included in the style, and to arrange the selected effects one by one in a time domain; a still picture assignment unit operable to assign a still picture to each of the effects arranged in the time domain by the effect arrangement unit on the basis of the object information, the still picture satisfying a picture feature required by the respective effects; and a parameter setting unit operable to generate the scenario by describing a parameter indicating processing to be performed on the object suitable for each of the effects arranged in the time domain by the effect arrangement unit, and to store the generated scenario in the database unit. 13. The short film generation/reproduction apparatus according to claim 10 , further comprising a feature point extraction unit operable to extract, from the object, a feature point indicating a characteristic part of the object, and to store the extracted feature point in the object information, wherein the parameter setting unit generates the scenario by describing a parameter indicating processing to be performed on a position where the feature point of the object is located.
| 0.818428 |
8. A computer-readable storage medium for testing software processes in a computer system, including: computer-readable instructions, the computer-readable instructions including instructions that when executed by at least one processor cause the at least one processor to perform the following acts: interfacing with a memory component that stores at least one portion of a software process as at least one model; retrieving the at least one model from the memory component; performing at least one test function on the at least one model, wherein the at least one test function is performed on the at least one model according to at least one dynamic influence, and wherein the at least one dynamic influence dynamically alters the at least one model at run-time; and enabling a reproduction of a specified test sequence of the at least one test function during run-time.
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8. A computer-readable storage medium for testing software processes in a computer system, including: computer-readable instructions, the computer-readable instructions including instructions that when executed by at least one processor cause the at least one processor to perform the following acts: interfacing with a memory component that stores at least one portion of a software process as at least one model; retrieving the at least one model from the memory component; performing at least one test function on the at least one model, wherein the at least one test function is performed on the at least one model according to at least one dynamic influence, and wherein the at least one dynamic influence dynamically alters the at least one model at run-time; and enabling a reproduction of a specified test sequence of the at least one test function during run-time. 9. The computer-readable storage medium of claim 8 , wherein the at least one dynamic influence includes at least one of a first change or a second change, wherein the first change corresponds to a change in a rule or a method of the at least one model, and wherein the second change corresponds to a change to a property of an attribute of the at least one model.
| 0.5 |
1. A method of crawling web pages and categorizing the web pages in a two-pass process used for delivering targeted content to a user equipment (UE) based on categories of web sites browsed by the UE, comprising: for each uniform resource locater (URL) stored in a pool of unvalidated URLs; removing a URL from the pool of unvalidated URLs by an application executing on a server computer; navigating to a web page referenced by the URL by the application, if navigating to the URL results in a redirect; discarding the URL, removed from the pool of unvalidated URLs by the application; searching for keywords in the URL and in a title of the web page referenced by the URL by the application; searching for URLs embedded in the web page referenced by the URL by the application; adding discovered embedded URLs to the pool of unvalidated URLs by the application; executing a plurality of primary web site categorization rules on the keywords associated with the web page referenced by the URL by the application, where each category primary rule is associated with a single category of web page, different category primary rules associate to different categories of web pages, and each category primary rue comprises a logical statement that is true if the keywords discovered in the title of the web page referenced by the URL or in the URL make the logical statement true; associating the URL to each category the category primary rule evaluates true by the application; executing a plurality of secondary web site categorization rules on the keywords associated with the web page referenced by the URL by the application, where each category secondary rule is associated with a single category of web page, different category secondary rules associate to different categories of web pages, and each category secondary rule comprises a logical statement that is true if the keywords discovered in the title of the web page referenced by the URL or in the URL make the logical statement true; associating the URL to each category the category secondary rule evaluates true by the application; and saving the URL the keywords associated with the web page referenced by the URL, and identities of categories to which the URL is associated in a pool of validated URLs by the application; for each category of web page; counting the number of occurrences of different keywords in the URLs stored in the pool of validated URLs that are associated to the category by the application; counting the number of URLs stored in the pool of validated URLs that are associated to the category by the application; for each different keyword that is not identified in a category primary rule or in a category secondary rule, determining by the application a percentage of the URLs that are associated to the category that are associated to the different keyword not identified in a rule versus the number of URLs stored in the pool of validated URLs that are associated to the category and for each said percentage that exceeds a predefined threshold percentage extending the logic of the category secondary rule by including the keyword associated with that said percentage; analyzing a web browsing history of a UE to determine URLs accessed by the UE that ace stored in the pool of validated URLs; associating the to one or more categories associated to the browsed URLs that are stored in the pool of validated URLs; and sending content that is targeted to the UE based on the one or more categories associated to the UE.
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1. A method of crawling web pages and categorizing the web pages in a two-pass process used for delivering targeted content to a user equipment (UE) based on categories of web sites browsed by the UE, comprising: for each uniform resource locater (URL) stored in a pool of unvalidated URLs; removing a URL from the pool of unvalidated URLs by an application executing on a server computer; navigating to a web page referenced by the URL by the application, if navigating to the URL results in a redirect; discarding the URL, removed from the pool of unvalidated URLs by the application; searching for keywords in the URL and in a title of the web page referenced by the URL by the application; searching for URLs embedded in the web page referenced by the URL by the application; adding discovered embedded URLs to the pool of unvalidated URLs by the application; executing a plurality of primary web site categorization rules on the keywords associated with the web page referenced by the URL by the application, where each category primary rule is associated with a single category of web page, different category primary rules associate to different categories of web pages, and each category primary rue comprises a logical statement that is true if the keywords discovered in the title of the web page referenced by the URL or in the URL make the logical statement true; associating the URL to each category the category primary rule evaluates true by the application; executing a plurality of secondary web site categorization rules on the keywords associated with the web page referenced by the URL by the application, where each category secondary rule is associated with a single category of web page, different category secondary rules associate to different categories of web pages, and each category secondary rule comprises a logical statement that is true if the keywords discovered in the title of the web page referenced by the URL or in the URL make the logical statement true; associating the URL to each category the category secondary rule evaluates true by the application; and saving the URL the keywords associated with the web page referenced by the URL, and identities of categories to which the URL is associated in a pool of validated URLs by the application; for each category of web page; counting the number of occurrences of different keywords in the URLs stored in the pool of validated URLs that are associated to the category by the application; counting the number of URLs stored in the pool of validated URLs that are associated to the category by the application; for each different keyword that is not identified in a category primary rule or in a category secondary rule, determining by the application a percentage of the URLs that are associated to the category that are associated to the different keyword not identified in a rule versus the number of URLs stored in the pool of validated URLs that are associated to the category and for each said percentage that exceeds a predefined threshold percentage extending the logic of the category secondary rule by including the keyword associated with that said percentage; analyzing a web browsing history of a UE to determine URLs accessed by the UE that ace stored in the pool of validated URLs; associating the to one or more categories associated to the browsed URLs that are stored in the pool of validated URLs; and sending content that is targeted to the UE based on the one or more categories associated to the UE. 3. The method of claim 1 , wherein at least some of the URLs in the pool of validated URLs are associated with two or more categories.
| 0.527363 |
7. A method of processing speech, the method comprising: receiving a spoken utterance of a plurality of uttered characters; determining an identified character sequence by determining corresponding identified characters for individual ones of the plurality of uttered characters; selecting a plurality of known character sequences that potentially correspond to the identified character sequence; and for each selected known character sequence, scoring such known character sequence, using a processor, based at least in part on a weighting of individual characters that comprise the known character sequence, wherein scoring the known character sequence comprises: responsive to determining that a second character of the known character sequence matches a first identified character of the identified character sequence, selecting a value that corresponds to the second character of the known character sequence; and adding the selected value to a cumulative score associated with the known character sequence.
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7. A method of processing speech, the method comprising: receiving a spoken utterance of a plurality of uttered characters; determining an identified character sequence by determining corresponding identified characters for individual ones of the plurality of uttered characters; selecting a plurality of known character sequences that potentially correspond to the identified character sequence; and for each selected known character sequence, scoring such known character sequence, using a processor, based at least in part on a weighting of individual characters that comprise the known character sequence, wherein scoring the known character sequence comprises: responsive to determining that a second character of the known character sequence matches a first identified character of the identified character sequence, selecting a value that corresponds to the second character of the known character sequence; and adding the selected value to a cumulative score associated with the known character sequence. 11. The method of claim 7 , wherein the weighting is based at least in part on frequencies of utterance of the individual characters.
| 0.768041 |
1. A system for generating a personalized query result for a specific user, the system comprising: memory; a processor; an interface stored in the memory that receives at least one of a portion of a text query to be searched or a portion of personalized content related to a user that submits the portion of the text query; a personalization component stored in the memory and executed by the processor that compares the portion of personalized content related to the user with a portion of personalized content related to one or more disparate users to create a group of users having group personalized content, the group personalized content is compared with the portion of the text query to identify a relationship there between to generate the personalized query result based in part on the relationship; and a shared web task engine that provides a collaborative web search utilizing a smart splitting technique, the smart splitting technique receives the portion of the text query and distributes query results based on the text query to members of the group to enable parallel evaluation by the members of the group, the personalized query result being generated further based in part on the parallel evaluation.
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1. A system for generating a personalized query result for a specific user, the system comprising: memory; a processor; an interface stored in the memory that receives at least one of a portion of a text query to be searched or a portion of personalized content related to a user that submits the portion of the text query; a personalization component stored in the memory and executed by the processor that compares the portion of personalized content related to the user with a portion of personalized content related to one or more disparate users to create a group of users having group personalized content, the group personalized content is compared with the portion of the text query to identify a relationship there between to generate the personalized query result based in part on the relationship; and a shared web task engine that provides a collaborative web search utilizing a smart splitting technique, the smart splitting technique receives the portion of the text query and distributes query results based on the text query to members of the group to enable parallel evaluation by the members of the group, the personalized query result being generated further based in part on the parallel evaluation. 5. The system of claim 1 , the personalization component employs a groupized ranking by at least one of the following: calculating a personalization score for each member of the group and calculating a group score for each query result by combining the personalization score from each member, and re-ranking the query results based on the group score; computing the group personalization score from a combination of the group member's profiles and re-ranking the query results based on the group score; or utilizing an algorithm to combine personalization scores for members of the group into the group score.
| 0.5 |
1. A handwritten messaging system operable on mobile devices connected to a data transmission network comprising: (a) a server component operable on a server computer connected to a data transmission network for receiving a handwritten message in electronic format sent from a user of a mobile device and delivering the handwritten message in real time to a mobile device of another recipient to whom it is addressed; (b) the mobile device having a handwriting messaging component operable with a messaging client of the mobile device having a connection to the data transmission network, wherein said handwriting messaging component sets up a handwriting data capture area within said messaging client into which the user can enter handwritten input through a suitable manual input device, and said handwriting data capture area operates to capture the handwritten input as graphical data and send it as a message in electronic format on the data transmission network; and (c) the mobile device being connected to the data transmission network having a messaging client for sending and receiving the handwritten electronic message sent from the mobile device of the user to that of the recipient via the network and for inputting it and then viewing it as a handwritten message via the messaging clients of the respective devices.
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1. A handwritten messaging system operable on mobile devices connected to a data transmission network comprising: (a) a server component operable on a server computer connected to a data transmission network for receiving a handwritten message in electronic format sent from a user of a mobile device and delivering the handwritten message in real time to a mobile device of another recipient to whom it is addressed; (b) the mobile device having a handwriting messaging component operable with a messaging client of the mobile device having a connection to the data transmission network, wherein said handwriting messaging component sets up a handwriting data capture area within said messaging client into which the user can enter handwritten input through a suitable manual input device, and said handwriting data capture area operates to capture the handwritten input as graphical data and send it as a message in electronic format on the data transmission network; and (c) the mobile device being connected to the data transmission network having a messaging client for sending and receiving the handwritten electronic message sent from the mobile device of the user to that of the recipient via the network and for inputting it and then viewing it as a handwritten message via the messaging clients of the respective devices. 4. A handwritten messaging system according to claim 1 , wherein the mobile device is a portable game console or game player device.
| 0.890476 |
1. A method for graphically building a message format, the method comprising: receiving a message; extracting at least one format element from the message; building the message format using the at least one format element; graphically representing a physical appearance of the message format in a horizontal format, wherein the horizontal format comprises an accordion style container of format elements, wherein the accordion style container include plurality of sub-containers, wherein each sub-container is delimited on both sides of the horizontal format of the sub-container, and wherein the accordion style container are expandable and collapsible in a horizontal direction; expanding the accordion style containers or sub containers; and in response to the expanding, substituting each data field of the accordion style container or sub containers with a value.
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1. A method for graphically building a message format, the method comprising: receiving a message; extracting at least one format element from the message; building the message format using the at least one format element; graphically representing a physical appearance of the message format in a horizontal format, wherein the horizontal format comprises an accordion style container of format elements, wherein the accordion style container include plurality of sub-containers, wherein each sub-container is delimited on both sides of the horizontal format of the sub-container, and wherein the accordion style container are expandable and collapsible in a horizontal direction; expanding the accordion style containers or sub containers; and in response to the expanding, substituting each data field of the accordion style container or sub containers with a value. 6. The method of claim 1 , wherein building the message format comprises at least one of: inserting the at least one format element into the message format; moving the at least one format element within the message format; or deleting the at least one format element from the message format.
| 0.63031 |
5. A method of prioritizing for automated translation from a first human language to a second human language communications relating to at least one predetermined topic, the method comprising: capturing and inputting into a data processing system a translation-candidate communication rendered in the first human language and storing in computer memory associated with the data processing system, in a predetermined machine-readable format, a first data set representative of the contents of the translation-candidate communication in the first human language; and maintaining in computer memory a first-language prioritization protocol including data indicative of first-language extraction rules according to which a selected first-data-set sub-portion representative of a communication sub-portion of the translation-candidate communication is algorithmically one of extracted and rejected for translation depending on whether the selected communication sub-portion exceeds a first relevancy threshold indicative of the relatedness of the communication sub-portion to the at least one predetermined topic, wherein the first-language prioritizer renders a selection output indicative of whether the first-language prioritizer has one of selected and rejected for translation the first-data-set sub-portion; wherein, as to a communication sub-portion selected for translation, the method further comprises providing at least two automated translators, each automated translator being programmed to translate the selected first-data-set sub-portion representative of the communication sub-portion in the first human language to a translated-data-set sub-portion representative, in a machine-readable format, of the communication sub-portion in the second human language; causing the first-data-set sub-portion representative of the relevant communication sub-portion in the first human language to be translated by each of the at least two automated translators to a translated-data-set sub-portion representative, in a machine-readable format, of the relevant communication sub-portion in the second human language; providing an automated translation arbitrator that, relative to the first-data-set sub-portion, algorithmically analyzes the plural translated-data-set sub-portions of the at least two automated translators and renders a determination as to which translation from among the plural translations is the most accurate; converting at least a portion of the translated-data-set sub-portion determined to be most accurate into a converted-data-set sub-portion representative of at least a portion of the translated-data-set sub-portion in a human-intelligible format; and outputting through a machine-to-human interface the converted-data-set sub-portion.
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5. A method of prioritizing for automated translation from a first human language to a second human language communications relating to at least one predetermined topic, the method comprising: capturing and inputting into a data processing system a translation-candidate communication rendered in the first human language and storing in computer memory associated with the data processing system, in a predetermined machine-readable format, a first data set representative of the contents of the translation-candidate communication in the first human language; and maintaining in computer memory a first-language prioritization protocol including data indicative of first-language extraction rules according to which a selected first-data-set sub-portion representative of a communication sub-portion of the translation-candidate communication is algorithmically one of extracted and rejected for translation depending on whether the selected communication sub-portion exceeds a first relevancy threshold indicative of the relatedness of the communication sub-portion to the at least one predetermined topic, wherein the first-language prioritizer renders a selection output indicative of whether the first-language prioritizer has one of selected and rejected for translation the first-data-set sub-portion; wherein, as to a communication sub-portion selected for translation, the method further comprises providing at least two automated translators, each automated translator being programmed to translate the selected first-data-set sub-portion representative of the communication sub-portion in the first human language to a translated-data-set sub-portion representative, in a machine-readable format, of the communication sub-portion in the second human language; causing the first-data-set sub-portion representative of the relevant communication sub-portion in the first human language to be translated by each of the at least two automated translators to a translated-data-set sub-portion representative, in a machine-readable format, of the relevant communication sub-portion in the second human language; providing an automated translation arbitrator that, relative to the first-data-set sub-portion, algorithmically analyzes the plural translated-data-set sub-portions of the at least two automated translators and renders a determination as to which translation from among the plural translations is the most accurate; converting at least a portion of the translated-data-set sub-portion determined to be most accurate into a converted-data-set sub-portion representative of at least a portion of the translated-data-set sub-portion in a human-intelligible format; and outputting through a machine-to-human interface the converted-data-set sub-portion. 8. The method of claim 5 wherein, as to a communication sub-portion rejected for translation, the method further comprises one of deleting from and archiving in computer memory the first-data-set sub-portion representative of that communication sub-portion.
| 0.545082 |
16. The method of claim 11 , wherein explicit key input is received by any combination of: 2-key explicit entry comprising two keys entered in sequence, wherein the combination of the two keys entered in sequence identifies a single unambiguous intended character; long pressing a key to display and cycle through a character sequence; long pressing on a key to enter a number/digit explicitly; changing to a numbers mode and pressing a key to enter a number/digit explicitly; changing to a multi-tap mode and pressing a key repeatedly to enter a character explicitly; interpreting ambiguous mode key presses as an explicit character, either by grouping each pair of key presses as a 2-key explicit entry, or by grouping repeated presses of the same key as a multi-tap entry; using multi-switch keys, thereby permitting ambiguous entry on a simple press and an explicit character entry on a different kind of press; chording by pressing more than one key simultaneously, with a primary key indicating an ambiguous set of characters and a secondary key indicating which character in the set to select; and using a softkey as a secondary means for offering any character assigned to a key based on analysis of most likely character associated with a preceding keystroke or based on words in a current word candidate list.
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16. The method of claim 11 , wherein explicit key input is received by any combination of: 2-key explicit entry comprising two keys entered in sequence, wherein the combination of the two keys entered in sequence identifies a single unambiguous intended character; long pressing a key to display and cycle through a character sequence; long pressing on a key to enter a number/digit explicitly; changing to a numbers mode and pressing a key to enter a number/digit explicitly; changing to a multi-tap mode and pressing a key repeatedly to enter a character explicitly; interpreting ambiguous mode key presses as an explicit character, either by grouping each pair of key presses as a 2-key explicit entry, or by grouping repeated presses of the same key as a multi-tap entry; using multi-switch keys, thereby permitting ambiguous entry on a simple press and an explicit character entry on a different kind of press; chording by pressing more than one key simultaneously, with a primary key indicating an ambiguous set of characters and a secondary key indicating which character in the set to select; and using a softkey as a secondary means for offering any character assigned to a key based on analysis of most likely character associated with a preceding keystroke or based on words in a current word candidate list. 17. The method of claim 16 , wherein explicit key input by 2-key explicit entry comprises use of at least one of: a matrix display; a label including a subset of predetermined sets of associated characters; and a scrolling list.
| 0.751141 |
22. A system comprising: distributed computing devices represented as leaf nodes and a root node; an index of documents, the index being distributed across the leaf nodes, the documents being assigned to respective leaf nodes, and wherein a first leaf node includes: memory storing document-sharded posting lists for some or all terms associated with documents in a first set of documents that are assigned to the first leaf node, and memory storing term-sharded posting lists for terms assigned to the first leaf node without regard to leaf node assignments for documents identified in the term-sharded posting lists, wherein the first leaf node includes: at least one processor, memory storing instructions that, when executed by the at least one processor, cause the first leaf node to: receive an update for documents in the first set of documents and, responsive to the receiving, update at least some of the document-sharded posting lists; receive an updated term-sharded posting list portion for a first term from a second leaf node, the first term being assigned to the first leaf node; receive an updated term-sharded posting list portion for the first term from a third leaf node; and generate a new term-sharded posting list for the first term using the portion from the third leaf node and the portion from the second leaf node.
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22. A system comprising: distributed computing devices represented as leaf nodes and a root node; an index of documents, the index being distributed across the leaf nodes, the documents being assigned to respective leaf nodes, and wherein a first leaf node includes: memory storing document-sharded posting lists for some or all terms associated with documents in a first set of documents that are assigned to the first leaf node, and memory storing term-sharded posting lists for terms assigned to the first leaf node without regard to leaf node assignments for documents identified in the term-sharded posting lists, wherein the first leaf node includes: at least one processor, memory storing instructions that, when executed by the at least one processor, cause the first leaf node to: receive an update for documents in the first set of documents and, responsive to the receiving, update at least some of the document-sharded posting lists; receive an updated term-sharded posting list portion for a first term from a second leaf node, the first term being assigned to the first leaf node; receive an updated term-sharded posting list portion for the first term from a third leaf node; and generate a new term-sharded posting list for the first term using the portion from the third leaf node and the portion from the second leaf node. 23. The system of claim 22 , wherein generating the new term-sharded posting list includes: concatenating the portion from the second leaf node and the portion from the third leaf node with a portion generated by the first leaf node.
| 0.675735 |
11. A system comprising: means for associating a parent component and a child component in an executing source application, where an output of the child component is connected with an input of the parent component, and where the parent and child components are used in a presentation of document data during navigation among documents; means for presenting a first navigation user interface in a source application window, including displaying a first representation of a source document in accordance with the parent and child components of the source application; means for receiving input adding the first representation of the source document from the source application window to a target application window of an executing target application; means for determining, in response to receiving the first representation of the source document, a relationship between the parent and child components associated with the first representation, and then inserting one or more computer-readable instructions associated with the parent component and one or more computer-readable instructions associated with the child component into the target application; means for presenting a second navigation user interface in the target application window, including displaying a second representation of a target document in accordance with the relationship between the parent and child components associated with the first representation; and means for determining, in response to receiving the first representation of the source document, that the output of the grandchild component is connected with the input of the child component, and then inserting one or more computer-readable instructions associated with the grandchild component into the target application; wherein associating the parent and child components further comprises associating a grandchild component with the child component in the executing source application, where an output of the grandchild component is connected with an input of the child component and where the parent and child and grandchild components are used in the presentation of document data during navigation among documents; and wherein presenting the second navigation interface includes displaying the second representation of the target document according to the parent component, the child component, and the grandchild component.
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11. A system comprising: means for associating a parent component and a child component in an executing source application, where an output of the child component is connected with an input of the parent component, and where the parent and child components are used in a presentation of document data during navigation among documents; means for presenting a first navigation user interface in a source application window, including displaying a first representation of a source document in accordance with the parent and child components of the source application; means for receiving input adding the first representation of the source document from the source application window to a target application window of an executing target application; means for determining, in response to receiving the first representation of the source document, a relationship between the parent and child components associated with the first representation, and then inserting one or more computer-readable instructions associated with the parent component and one or more computer-readable instructions associated with the child component into the target application; means for presenting a second navigation user interface in the target application window, including displaying a second representation of a target document in accordance with the relationship between the parent and child components associated with the first representation; and means for determining, in response to receiving the first representation of the source document, that the output of the grandchild component is connected with the input of the child component, and then inserting one or more computer-readable instructions associated with the grandchild component into the target application; wherein associating the parent and child components further comprises associating a grandchild component with the child component in the executing source application, where an output of the grandchild component is connected with an input of the child component and where the parent and child and grandchild components are used in the presentation of document data during navigation among documents; and wherein presenting the second navigation interface includes displaying the second representation of the target document according to the parent component, the child component, and the grandchild component. 15. The system of claim 11 , further comprising: means for causing a virtual machine associated with the target application to execute the computer readable instructions.
| 0.878031 |
11. A computer system for capturing information from physical tokens with a portable computing device, the system comprising: means for creating a first image of a first side of a token with a portable computing device; means for identifying a position of the token contained within the first image; means for conducting a broad recognition scan of the first side of the token to determine whether the token is supported by a mobile wallet system; means for conducting optical character recognition of the first image of the first side of the token, if the token is supported by the mobile wallet system, to retrieve consumer information based on a source identified for the token and store the retrieved consumer information on the portable computing device; and means for creating a second image of a second side of the token, if the token is not supported by the mobile wallet system, and transmitting the first and second images of the respective first and second sides of the unsupported token to a server associated with the mobile wallet system.
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11. A computer system for capturing information from physical tokens with a portable computing device, the system comprising: means for creating a first image of a first side of a token with a portable computing device; means for identifying a position of the token contained within the first image; means for conducting a broad recognition scan of the first side of the token to determine whether the token is supported by a mobile wallet system; means for conducting optical character recognition of the first image of the first side of the token, if the token is supported by the mobile wallet system, to retrieve consumer information based on a source identified for the token and store the retrieved consumer information on the portable computing device; and means for creating a second image of a second side of the token, if the token is not supported by the mobile wallet system, and transmitting the first and second images of the respective first and second sides of the unsupported token to a server associated with the mobile wallet system. 13. The system of claim 11 , further comprising means for retrieving a relative position of one or more fields of interest for the token, the relative position corresponding to a match in a database for the source of the token.
| 0.689555 |
27. A computer readable medium encoded with a computer program as claimed in claim 23 , wherein the adjustment step comprises adding one or more gaps in the phoneme string.
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27. A computer readable medium encoded with a computer program as claimed in claim 23 , wherein the adjustment step comprises adding one or more gaps in the phoneme string. 28. A computer readable medium encoded with a computer program as claimed in claim 27 , wherein the one or more gaps made be added to the beginning or end of a phoneme string.
| 0.922508 |
1. A machine translation method comprising: converting, using a processor, a source sentence written in a first language to language-independent information using an encoder for the first language, information for which is stored in non-volatile and volatile memory; and converting, using the processor, the language-independent information to a target sentence corresponding to the source sentence and written in a second language different from the first language using a decoder for the second language, information for which is stored in the nonvolatile and volatile memory, in response to a similarity being higher than a preset threshold, wherein the similarity is determined from a comparison between pixel values of the language-independent information of the source sentence and pixel values of the language-independent information of the target sentence; wherein the encoder for the first language is trained to output language-independent information corresponding to the target sentence in response to an input of the source sentence, using the processor, wherein the processor communicates with the nonvolatile and volatile memory via a bus.
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1. A machine translation method comprising: converting, using a processor, a source sentence written in a first language to language-independent information using an encoder for the first language, information for which is stored in non-volatile and volatile memory; and converting, using the processor, the language-independent information to a target sentence corresponding to the source sentence and written in a second language different from the first language using a decoder for the second language, information for which is stored in the nonvolatile and volatile memory, in response to a similarity being higher than a preset threshold, wherein the similarity is determined from a comparison between pixel values of the language-independent information of the source sentence and pixel values of the language-independent information of the target sentence; wherein the encoder for the first language is trained to output language-independent information corresponding to the target sentence in response to an input of the source sentence, using the processor, wherein the processor communicates with the nonvolatile and volatile memory via a bus. 2. The machine translation method of claim 1 , wherein the converting of the source sentence to the language-independent information comprises converting the source sentence to language-independent information having a similarity to the language-independent information corresponding to the target sentence higher than a preset threshold.
| 0.71285 |
18. The computer-readable storage medium of claim 17 , the apparatus is caused, at least in part, to further perform the following steps: determining whether the user consents to the legal text; and in response to determining that the user consents to the legal text, transmitting a purchase message to a content provider.
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18. The computer-readable storage medium of claim 17 , the apparatus is caused, at least in part, to further perform the following steps: determining whether the user consents to the legal text; and in response to determining that the user consents to the legal text, transmitting a purchase message to a content provider. 19. The computer-readable storage medium of claim 18 , wherein the purchase message includes the user consent response.
| 0.867094 |
1. A client device capable of data communication with one or more communication networks, said client device comprising: a data capture apparatus; at least one interface configured to communicate with one or more entities of said one or more communication networks; and a digital processor, said processor having at least one computer program configured to run thereon, said at least one computer program comprising a plurality of instructions which are configured to, when executed: enable a user of said client device to generate a request for information, said request for information comprising at least one element configured to identify at least one item for auction, said at least one element obtained from information scanned or captured via an application configured to run on said client device; transmit said request to said one or more entities of said communication network via said at least one interface; and in response to said request, receive information regarding said at least one item for auction.
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1. A client device capable of data communication with one or more communication networks, said client device comprising: a data capture apparatus; at least one interface configured to communicate with one or more entities of said one or more communication networks; and a digital processor, said processor having at least one computer program configured to run thereon, said at least one computer program comprising a plurality of instructions which are configured to, when executed: enable a user of said client device to generate a request for information, said request for information comprising at least one element configured to identify at least one item for auction, said at least one element obtained from information scanned or captured via an application configured to run on said client device; transmit said request to said one or more entities of said communication network via said at least one interface; and in response to said request, receive information regarding said at least one item for auction. 3. The client device of claim 1 , further comprising an optical character recognition (OCR) algorithm configured to convert image data obtained via said application configured to run on said client device into an appropriate format for inclusion as said at least one element in said request for information.
| 0.671252 |
15. A method according to claim 14 , wherein locating a first layout strings file and a second layout strings file from a plurality of layout strings files includes locating the one of the plurality of layout strings files storing the first layout string in a selected language.
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15. A method according to claim 14 , wherein locating a first layout strings file and a second layout strings file from a plurality of layout strings files includes locating the one of the plurality of layout strings files storing the first layout string in a selected language. 17. A computer implemented method according to claim 15 , the method further comprising: receiving a ranked list of languages from the user, the ranked list comprising a plurality of languages in an order based on preferences of the user; accessing a resource file map listing recognized combinations of layout information files and languages in which the layout strings file store the layout string; and identifying the selected language from the resource file map based on the ranked list of languages.
| 0.78275 |
1. A method for constructing a search database using an ontology, the method comprising: via at least one processor: obtaining a list of items, each item corresponding to a potential search result; obtaining a list of terms from an ontology that describes all of the items, each term being an attribute of a corresponding item; dividing the terms into user-selectable search queries and non-selectable terms; activating the items and the terms as nodes in a network, wherein: the nodes are connected based on semantic relationships specified by the ontology, the network has three layers, a first layer including a node for each of the items, a second layer including a node for each search query, and a third layer including a node for each non-selectable term, the second layer nodes correspond to user-observed states of their respective terms, and the third layer nodes correspond to hidden states of the terms represented in the second layer; and configuring the network such that a probability of nodes in the first layer is determined by updating states of nodes in the third layer: deterministically based on the semantic relationships, and probabilistically based on at least one of (ii) frequency information indicating a frequency with which a term of a node in the third layer is known to occur when an associated item of a node in the first layer also occurs and (ii) a predefined false positive or false negative rate.
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1. A method for constructing a search database using an ontology, the method comprising: via at least one processor: obtaining a list of items, each item corresponding to a potential search result; obtaining a list of terms from an ontology that describes all of the items, each term being an attribute of a corresponding item; dividing the terms into user-selectable search queries and non-selectable terms; activating the items and the terms as nodes in a network, wherein: the nodes are connected based on semantic relationships specified by the ontology, the network has three layers, a first layer including a node for each of the items, a second layer including a node for each search query, and a third layer including a node for each non-selectable term, the second layer nodes correspond to user-observed states of their respective terms, and the third layer nodes correspond to hidden states of the terms represented in the second layer; and configuring the network such that a probability of nodes in the first layer is determined by updating states of nodes in the third layer: deterministically based on the semantic relationships, and probabilistically based on at least one of (ii) frequency information indicating a frequency with which a term of a node in the third layer is known to occur when an associated item of a node in the first layer also occurs and (ii) a predefined false positive or false negative rate. 2. The method of claim 1 , further comprising: adding intra-layer connections to the second and the third layers to control propagation of annotations throughout the network.
| 0.663071 |
1. A computer-implemented system including a computer processor configured to create an advertisement, the system comprising: a. an administrative toolkit configured to facilitate a template creator at a first location to create a template comprising a plurality of template portions, and to input content components to populate the template, the administrative toolkit comprising: i. a template definition and editing module configured to: 1. create the template at a development site accessible to the template creator and not accessible to an end user; and 2. establish rules governing content to be placed in one or more of the template portions; and ii. a template inventory management module configured to move a copy of the template from the development site to a production site accessible to the end user, thereby updating the production site; and b. an end-user interface configured to facilitate the end user at a second location to: i. access the copy of the template over an information exchange network after the copy of the template has been moved to the production site; and ii. populate one or more of the portions with content in accordance with the rules established by the template creator, whereby the end user can create a customized advertisement that conforms to standards set by the template creator, wherein: the administrative toolkit is different and separate from the end-user interface.
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1. A computer-implemented system including a computer processor configured to create an advertisement, the system comprising: a. an administrative toolkit configured to facilitate a template creator at a first location to create a template comprising a plurality of template portions, and to input content components to populate the template, the administrative toolkit comprising: i. a template definition and editing module configured to: 1. create the template at a development site accessible to the template creator and not accessible to an end user; and 2. establish rules governing content to be placed in one or more of the template portions; and ii. a template inventory management module configured to move a copy of the template from the development site to a production site accessible to the end user, thereby updating the production site; and b. an end-user interface configured to facilitate the end user at a second location to: i. access the copy of the template over an information exchange network after the copy of the template has been moved to the production site; and ii. populate one or more of the portions with content in accordance with the rules established by the template creator, whereby the end user can create a customized advertisement that conforms to standards set by the template creator, wherein: the administrative toolkit is different and separate from the end-user interface. 3. The system of claim 1 , wherein the content components comprise text.
| 0.572789 |
11. An apparatus for generating a schema for data asset information, the apparatus comprising: a processor; and a memory, the memory storing program code executable by the processor to perform operations comprising: accessing complex type information corresponding to a logical relational data model that defines an organization of the data asset information, the logical relational data model including a parent entity and child entities corresponding to the parent entity; treating the complex type information to produce scrubbed complex type information, said treating of the complex type information including removing at least one foreign key from at least one of the child entities; translating the scrubbed complex type information to produce a hierarchical data model corresponding to the logical relational data model, the hierarchical data model including a plurality of containers respectively corresponding to the child entities of the logical relational data model, the treating and translating being carried out such that the at least one foreign key removed from the at least one child entity is omitted from a first level container in the hierarchical data model and is present in a second level container in the hierarchical data model, the first level container paralleling the child entity from which the foreign key is removed, the second level container being at a higher level in the hierarchical data model than that of the first level container; and generating a schema for the data asset information based upon the hierarchical data model.
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11. An apparatus for generating a schema for data asset information, the apparatus comprising: a processor; and a memory, the memory storing program code executable by the processor to perform operations comprising: accessing complex type information corresponding to a logical relational data model that defines an organization of the data asset information, the logical relational data model including a parent entity and child entities corresponding to the parent entity; treating the complex type information to produce scrubbed complex type information, said treating of the complex type information including removing at least one foreign key from at least one of the child entities; translating the scrubbed complex type information to produce a hierarchical data model corresponding to the logical relational data model, the hierarchical data model including a plurality of containers respectively corresponding to the child entities of the logical relational data model, the treating and translating being carried out such that the at least one foreign key removed from the at least one child entity is omitted from a first level container in the hierarchical data model and is present in a second level container in the hierarchical data model, the first level container paralleling the child entity from which the foreign key is removed, the second level container being at a higher level in the hierarchical data model than that of the first level container; and generating a schema for the data asset information based upon the hierarchical data model. 13. The apparatus of claim 11 , wherein said treating of the complex type information comprises merging one of the child entities into the parent entity.
| 0.514089 |
31. The method of claim 28 , wherein the providing additionally comprises uploading one or more user-selected content into a database of potential contents.
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31. The method of claim 28 , wherein the providing additionally comprises uploading one or more user-selected content into a database of potential contents. 32. The method of claim 31 , wherein the uploading comprises processing image-based contents to improve image quality.
| 0.938333 |
7. The method of claim 1 , further comprising ordering the set of predicted complete queries in accordance with ranking criteria.
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7. The method of claim 1 , further comprising ordering the set of predicted complete queries in accordance with ranking criteria. 8. The method of claim 7 , wherein, when the partial search query is a member of the set of predicted complete queries, the ordering includes placing the partial search query at a top position of the ordered set of predicted complete queries.
| 0.923053 |
59. The system of claim 33 including a notifier that notifies the first user of the content, prior to the content being stored in either the first or the second portion of the user profile of the first user.
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59. The system of claim 33 including a notifier that notifies the first user of the content, prior to the content being stored in either the first or the second portion of the user profile of the first user. 60. The system of claim 59 wherein the notifier provides the first user with an option of storing the content in either the first or the second portion of the user knowledge profile of the first user.
| 0.805795 |
1. A computer-implemented method comprising: classifying a text into first subject matter categories; identifying one or more second subject matter categories in a plurality of second subject matter categories, each of the second subject matter categories being a hierarchical classification of a plurality of confirmed valid search results for queries, and wherein at least one query for each identified second subject matter category comprises a term in the text; filtering the identified second subject matter categories by excluding identified second subject matter categories whose ancestors are not among the first subject matter categories; for each second subject matter category in the filtered second subject matter categories: extracting one or more constituent terms from the queries of whose confirmed valid search results the second subject matter category is the hierarchical classification, where the constituent terms appear in the text; calculating an initial weight of the second subject matter category, the calculating comprising determining a sum of term frequency-inverse document frequency (tf-idf) values of each extracted constituent term in relation to a corpus of documents; and selecting the second subject matter category based on the initial weight and based on a threshold where the threshold specifies a degree of relatedness between a selected subject matter category and the text; and where the selected second subject matter categories are a sufficient basis for recommending to a user content associated with one or more of the selected second subject matter categories.
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1. A computer-implemented method comprising: classifying a text into first subject matter categories; identifying one or more second subject matter categories in a plurality of second subject matter categories, each of the second subject matter categories being a hierarchical classification of a plurality of confirmed valid search results for queries, and wherein at least one query for each identified second subject matter category comprises a term in the text; filtering the identified second subject matter categories by excluding identified second subject matter categories whose ancestors are not among the first subject matter categories; for each second subject matter category in the filtered second subject matter categories: extracting one or more constituent terms from the queries of whose confirmed valid search results the second subject matter category is the hierarchical classification, where the constituent terms appear in the text; calculating an initial weight of the second subject matter category, the calculating comprising determining a sum of term frequency-inverse document frequency (tf-idf) values of each extracted constituent term in relation to a corpus of documents; and selecting the second subject matter category based on the initial weight and based on a threshold where the threshold specifies a degree of relatedness between a selected subject matter category and the text; and where the selected second subject matter categories are a sufficient basis for recommending to a user content associated with one or more of the selected second subject matter categories. 2. The method of claim 1 in which calculating a tf-idf value of each extracted constituent term further comprises: calculating an inverse document frequency (idf) value of the constituent term in relation to the corpus of documents; calculating a term frequency (tf) value of the constituent term; and determining the tf-idf value of the extracted constituent term based on the idf value of the constituent term and the tf value of the constituent term.
| 0.545237 |
8. The method of claim 6 , wherein the folio comprises a spreadsheet.
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8. The method of claim 6 , wherein the folio comprises a spreadsheet. 10. The method of claim 8 , wherein the user-generated folio-level queries comprise refresh data requests initiated from within a toolbar or menu option of the spreadsheet.
| 0.959917 |
12. In an apparatus for entering data comprised of alpha characters and including a display panel and a keyboard comprised of a plurality of hard keys, and wherein (a) each hard key is identified by two alpha characters, and (b) one of the hard keys is actuated, the improvement wherein: the hard keys are arranged within an area; the apparatus includes a plurality of soft keys at locations away from the area, and wherein: each of the soft keys displays a different alpha character corresponding to one of the alpha characters of the actuated hard key.
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12. In an apparatus for entering data comprised of alpha characters and including a display panel and a keyboard comprised of a plurality of hard keys, and wherein (a) each hard key is identified by two alpha characters, and (b) one of the hard keys is actuated, the improvement wherein: the hard keys are arranged within an area; the apparatus includes a plurality of soft keys at locations away from the area, and wherein: each of the soft keys displays a different alpha character corresponding to one of the alpha characters of the actuated hard key. 14. The apparatus of claim 12 wherein each hard key is further identified with a numeric character and the apparatus includes: a first data field displayed at a first location; a new data field displayed at a second location; and wherein: the first and second data fields differ from one another; and the second data field includes the numeric character identifying the actuated hard key.
| 0.5 |
2. The method according to claim 1 , further comprising: moving the text box down from the original position as the pull-down gesture lengthens.
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2. The method according to claim 1 , further comprising: moving the text box down from the original position as the pull-down gesture lengthens. 3. The method according to claim 2 , wherein the user prompt icon further includes an arrow.
| 0.933971 |
19. The method of claim 10 wherein performing test generation comprises: 1) generating paths to cover the lower bound for the set of reachable observable states in the computer program; 2) symbolically executing the generated paths to generate test data to cover the paths; and 3) running the computer program against the generated test data.
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19. The method of claim 10 wherein performing test generation comprises: 1) generating paths to cover the lower bound for the set of reachable observable states in the computer program; 2) symbolically executing the generated paths to generate test data to cover the paths; and 3) running the computer program against the generated test data. 20. The method of claim 19 wherein the generating paths uses a depth first procedure to identify must transitions followed by zero or one may transition.
| 0.904061 |
1. A method comprising: obtaining, by a recommendation engine device, program historical data associated with users that each receive one or more programs via one or more channels of a program delivery network that provides a program service to which the users belong; obtaining, by the recommendation engine device, social network data associated with the users from social network sites to which the users belong, wherein the social network data includes a social graph, communication data pertaining to communications between the users via a communication network that provides a communication service to which the users belong, wherein the communication service includes a mobile phone service and a messaging service, and the communication data includes mobile phone calls, and user profile information pertaining to the users; calculating based on the social network data, the communication data, and the user profile information, by the recommendation engine device, a social similarity value that indicates a social similarity between one of the users and other users; calculating based on the program historical data, by the recommendation engine device, a channel-interest similarity value that indicates a common interest between the one of the users and the other users in relation to the one or more channels used by the users to receive the one or more programs; calculating based on the social similarity value and the channel-interest similarity value, by the recommendation engine device, a similarity index value that indicates a similarity between the one of the users and the other users; calculating based on the program historical data, by the recommendation engine device, a program regularity value, for each program, that indicates a regularity of consumption of each program over a time period; calculating based on the program regularity value, by the recommendation engine device, a program weight value, for each program, that indicates a priority value; calculating based on the program historical data, by the recommendation engine device, a stay-time, for each channel, that indicates a time period each of the users remained on each channel; calculating based on each program weight value, each stay-time, and each similarity index value, by the recommendation engine device, a channel weight for each channel; and selecting based on each channel weight, by the recommendation engine device, one or more channels to recommend to at least one of the users.
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1. A method comprising: obtaining, by a recommendation engine device, program historical data associated with users that each receive one or more programs via one or more channels of a program delivery network that provides a program service to which the users belong; obtaining, by the recommendation engine device, social network data associated with the users from social network sites to which the users belong, wherein the social network data includes a social graph, communication data pertaining to communications between the users via a communication network that provides a communication service to which the users belong, wherein the communication service includes a mobile phone service and a messaging service, and the communication data includes mobile phone calls, and user profile information pertaining to the users; calculating based on the social network data, the communication data, and the user profile information, by the recommendation engine device, a social similarity value that indicates a social similarity between one of the users and other users; calculating based on the program historical data, by the recommendation engine device, a channel-interest similarity value that indicates a common interest between the one of the users and the other users in relation to the one or more channels used by the users to receive the one or more programs; calculating based on the social similarity value and the channel-interest similarity value, by the recommendation engine device, a similarity index value that indicates a similarity between the one of the users and the other users; calculating based on the program historical data, by the recommendation engine device, a program regularity value, for each program, that indicates a regularity of consumption of each program over a time period; calculating based on the program regularity value, by the recommendation engine device, a program weight value, for each program, that indicates a priority value; calculating based on the program historical data, by the recommendation engine device, a stay-time, for each channel, that indicates a time period each of the users remained on each channel; calculating based on each program weight value, each stay-time, and each similarity index value, by the recommendation engine device, a channel weight for each channel; and selecting based on each channel weight, by the recommendation engine device, one or more channels to recommend to at least one of the users. 8. The method of claim 1 , wherein the one or more programs include a television program.
| 0.839203 |
10. The computer readable storage medium of claim 9 , further comprising: receiving an automated test script template; and generating the automated test script by writing the automated testing script command to the automated test script template.
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10. The computer readable storage medium of claim 9 , further comprising: receiving an automated test script template; and generating the automated test script by writing the automated testing script command to the automated test script template. 11. The computer readable storage medium of claim 10 , wherein the test script template is an empty script program and further comprising: populating the empty script program with the automated testing script command to create an automated testing program executable by the testing framework system.
| 0.911968 |
12. The apparatus of claim 11 further comprising means for storing in association with each dwell time position accumulated score, a word duration count corresponding to the time position length of the keyword associated with the accumulated score at the dwell time position.
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12. The apparatus of claim 11 further comprising means for storing in association with each dwell time position accumulated score, a word duration count corresponding to the time position length of the keyword associated with the accumulated score at the dwell time position. 13. The apparatus of claim 12 further comprising second means for storing, in association with each dwell time position accumulated score, a target pattern duration count corresponding to the time of the dwell time position in the target pattern.
| 0.92212 |
14. A computer system for responding to an audio query, the system comprising a processor and a memory, wherein the processor executes instructions stored in the memory as part of or in conjunction with additional components to respond to receiving an audio query from a computer user, and causes the processor to: enable a network communication component to communicate between the computer system and other computing devices over a network; translate an audio query to a textual representation of the audio query; identify vocalization nuances of the audio query; generate a confidence value for each vocalization nuance of the identified vocalization nuances, wherein each confidence value is a part of a confidence/risk values pair corresponding to an vocalization nuance, and wherein each confidence value represents a confidence that a corresponding vocalization nuance correctly reflects a corresponding aspect of the computer user; and provide a risk value corresponding to each vocalization nuance of the identified vocalization nuances, wherein each risk value is a part of a confidence/risk values pair corresponding to a vocalization nuance irrespective of whether the vocalization nuance accurately reflects some attribute of the computer user, and wherein each risk value represents a risk that content selected based on a corresponding vocalization nuance may be offensive; identify a plurality of search results in response to receiving the audio query from a content data store and based, in part, on the vocalization nuances of the audio query and the corresponding confidence/risk value pairs of the vocalization nuances; and generate a search results presentation for the computer user based on the identified plurality of search results and in light of the vocalization nuances of the audio query.
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14. A computer system for responding to an audio query, the system comprising a processor and a memory, wherein the processor executes instructions stored in the memory as part of or in conjunction with additional components to respond to receiving an audio query from a computer user, and causes the processor to: enable a network communication component to communicate between the computer system and other computing devices over a network; translate an audio query to a textual representation of the audio query; identify vocalization nuances of the audio query; generate a confidence value for each vocalization nuance of the identified vocalization nuances, wherein each confidence value is a part of a confidence/risk values pair corresponding to an vocalization nuance, and wherein each confidence value represents a confidence that a corresponding vocalization nuance correctly reflects a corresponding aspect of the computer user; and provide a risk value corresponding to each vocalization nuance of the identified vocalization nuances, wherein each risk value is a part of a confidence/risk values pair corresponding to a vocalization nuance irrespective of whether the vocalization nuance accurately reflects some attribute of the computer user, and wherein each risk value represents a risk that content selected based on a corresponding vocalization nuance may be offensive; identify a plurality of search results in response to receiving the audio query from a content data store and based, in part, on the vocalization nuances of the audio query and the corresponding confidence/risk value pairs of the vocalization nuances; and generate a search results presentation for the computer user based on the identified plurality of search results and in light of the vocalization nuances of the audio query. 15. The computer system of claim 14 , wherein the executed instructions further cause the processor to identify a plurality of search results in response to receiving the audio query from a content data store and in light of the vocalization nuances of the audio query whose confidence/risk value pairs fall within predetermined thresholds.
| 0.501263 |
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