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9. A system, comprising: a processor; request processing logic in communication with the processor and configured to direct said processor to receive a translation request that has been originated using a communications device using a template in an application running on the communications device; broadcast logic in communication with the processor and configured to direct said processor to send, to a plurality of translators, a request broadcast based on the translation request; translation receiving logic in communication with the processor and configured to direct said processor to receive from the translators a plurality of respective translations responsive to the broadcast; translation parsing logic in communication with the processor and configured to direct said processor to parse the plurality of translations to detect any abusive or offensive language; translation transmission logic in communication with the processor and configured to direct said processor to forward the translations to an originator of the translation request; and selection receiving logic in communication with the processor and configured to direct said processor to receive an identity of a translation that has been chosen by the originator from among the plurality of translations. | 9. A system, comprising: a processor; request processing logic in communication with the processor and configured to direct said processor to receive a translation request that has been originated using a communications device using a template in an application running on the communications device; broadcast logic in communication with the processor and configured to direct said processor to send, to a plurality of translators, a request broadcast based on the translation request; translation receiving logic in communication with the processor and configured to direct said processor to receive from the translators a plurality of respective translations responsive to the broadcast; translation parsing logic in communication with the processor and configured to direct said processor to parse the plurality of translations to detect any abusive or offensive language; translation transmission logic in communication with the processor and configured to direct said processor to forward the translations to an originator of the translation request; and selection receiving logic in communication with the processor and configured to direct said processor to receive an identity of a translation that has been chosen by the originator from among the plurality of translations. 12. The system of claim 9 , wherein said request processing logic is further configured to direct said processor to: parse the translation request, prior to sending said request broadcast. | 0.591947 |
1. An image log management device comprising: a processor; a correspondence relationship information storing component, executed on the processor, that stores correspondence relationship information between an identifier of an input document subject to image forming processing, an identifier of one or more output documents resulting from the image forming processing of the input document, and an identifier of image log data of the one or more output documents; an image log data storage component that stores the image log data of the one or more output documents; an input component that inputs document disposal information including an identifier of a disposal document that has been disposed of; a document disposal information component that, based on the document disposal information, changes a status of a document corresponding to the disposal document to disposal-complete; and a deletion component that, based on the status of the document corresponding to the document disposal information and based on one or more identifiers of input or output documents stored in the correspondence relationship information, selects image log data requiring deletion and executes deletion processing thereon, wherein when all of the one or more output documents, processed by the image forming processing with the disposal document as the input document, have their respective status changed to disposal-complete, the deletion component deletes the image log data of the one or more output documents. | 1. An image log management device comprising: a processor; a correspondence relationship information storing component, executed on the processor, that stores correspondence relationship information between an identifier of an input document subject to image forming processing, an identifier of one or more output documents resulting from the image forming processing of the input document, and an identifier of image log data of the one or more output documents; an image log data storage component that stores the image log data of the one or more output documents; an input component that inputs document disposal information including an identifier of a disposal document that has been disposed of; a document disposal information component that, based on the document disposal information, changes a status of a document corresponding to the disposal document to disposal-complete; and a deletion component that, based on the status of the document corresponding to the document disposal information and based on one or more identifiers of input or output documents stored in the correspondence relationship information, selects image log data requiring deletion and executes deletion processing thereon, wherein when all of the one or more output documents, processed by the image forming processing with the disposal document as the input document, have their respective status changed to disposal-complete, the deletion component deletes the image log data of the one or more output documents. 3. The image log management device of claim 1 , wherein when the deletion component has deleted the image log data, deletion information representing that the image log data has been deleted is associated with the identifier of the deleted image log data and stored in the correspondence relationship information storing component. | 0.643844 |
15. A non-transitory computer readable medium embodying programmed instructions which, when executed by a processor, are operable for performing a method comprising: receiving print data defining a print job that includes documents that each comprise a mail piece having an intended recipient for delivery, wherein there are multiple recipients indicated by the documents of the print job; directing devices of a print shop to process the print job in accordance with a workflow that comprises an ordered set of activities to perform upon the documents, and the activities comprise operations performed by a printer or post-printing device for physically printed versions of the documents; acquiring preferences for the intended recipients; correlating documents in the print job with the acquired preferences; and altering processing of the correlated documents in the workflow by selectively skipping activities in the workflow for the correlated documents, based on the preferences of the intended recipients. | 15. A non-transitory computer readable medium embodying programmed instructions which, when executed by a processor, are operable for performing a method comprising: receiving print data defining a print job that includes documents that each comprise a mail piece having an intended recipient for delivery, wherein there are multiple recipients indicated by the documents of the print job; directing devices of a print shop to process the print job in accordance with a workflow that comprises an ordered set of activities to perform upon the documents, and the activities comprise operations performed by a printer or post-printing device for physically printed versions of the documents; acquiring preferences for the intended recipients; correlating documents in the print job with the acquired preferences; and altering processing of the correlated documents in the workflow by selectively skipping activities in the workflow for the correlated documents, based on the preferences of the intended recipients. 19. The medium of claim 15 wherein: alter processing of the correlated documents in the workflow after the controller initiates processing of the print job in accordance with the workflow. | 0.665657 |
1. A computer-implemented method for identifying content in a document, the method comprising: identifying within each document of a plurality of open documents, by a computing device, content corresponding to a particular subject matter category; in response to the computing device opening an additional document, identifying within the additional document, by the computer device, additional content corresponding to the particular subject matter category; providing, within the additional document, an indicator that visually distinguishes the identified additional content from the other content within the additional document; in response to the computing device opening a second additional document and content of the second additional document not corresponding to the particular subject matter category, determining a new subject matter category corresponding to the content of the second additional document; determining a majority of the plurality of open documents of the computing device relate to similar subject matter, wherein the particular subject matter category relates to the similar subject matter; in response to determining that the majority of the plurality of open documents of the computing device relate to the similar subject matter, assigning a weight to the similar subject matter and the new subject matter category, wherein the similar subject matter is more heavily weighted than the new subject matter category; and providing, based on the assigned weights, indicators that visually distinguish documents relating to the similar subject matter category from documents relating to the new subject matter category. | 1. A computer-implemented method for identifying content in a document, the method comprising: identifying within each document of a plurality of open documents, by a computing device, content corresponding to a particular subject matter category; in response to the computing device opening an additional document, identifying within the additional document, by the computer device, additional content corresponding to the particular subject matter category; providing, within the additional document, an indicator that visually distinguishes the identified additional content from the other content within the additional document; in response to the computing device opening a second additional document and content of the second additional document not corresponding to the particular subject matter category, determining a new subject matter category corresponding to the content of the second additional document; determining a majority of the plurality of open documents of the computing device relate to similar subject matter, wherein the particular subject matter category relates to the similar subject matter; in response to determining that the majority of the plurality of open documents of the computing device relate to the similar subject matter, assigning a weight to the similar subject matter and the new subject matter category, wherein the similar subject matter is more heavily weighted than the new subject matter category; and providing, based on the assigned weights, indicators that visually distinguish documents relating to the similar subject matter category from documents relating to the new subject matter category. 2. The method of claim 1 , wherein the plurality of open documents and the additional document comprise one or more of word processing documents, spreadsheets, text editing files, presentation documents or web browser pages. | 0.839972 |
7. The non-transitory computer program storage device of claim 6 , wherein: the unified index stores occurrence statistics for the keywords; the structured data repository comprises a structured database including table names and dimensional values of tables that include the first keyword; and the unstructured data repository comprises unstructured text that includes the second keyword. | 7. The non-transitory computer program storage device of claim 6 , wherein: the unified index stores occurrence statistics for the keywords; the structured data repository comprises a structured database including table names and dimensional values of tables that include the first keyword; and the unstructured data repository comprises unstructured text that includes the second keyword. 8. The non-transitory computer program storage device of claim 7 , wherein the method further comprises: performing a cost analysis, based on the occurrence statistics of the keyword, to determine an order for querying the unstructured data repository and, the structured data repository. | 0.849466 |
7. A computer system for optimizing a multi-language user interface layout via reverse pseudo-translation, the computer system comprising: one or more processors, one or more computer-readable memories, one or more computer-readable tangible storage medium, and program instructions stored on at least one of the one or more tangible storage medium for execution by at least one of the one or more processors via at least one of the one or more memories, wherein the computer system is capable of performing a method comprising: selecting at least one user interface page from a group of user interface pages in a first language; selecting at least one target language from a group of target languages to pseudo-translate the at least one user interface page; specifying at least one layout requirement for formatting the selected at least one user interface page; performing pseudo-translation of the at least one user interface page based on the selected at least one target language; modifying, by a merge algorithm, the at least one pseudo-translated user interface page according to the at least one specified layout requirement, wherein the merge algorithm comprises: applying the at least one specified layout requirement to the at least one pseudo-translated user interface page, wherein the at least one specified layout requirement is applied to the at least one pseudo-translated user interface page in a hierarchy from a most shared selected layout property among the at least one selected target language to a least shared selected layout property among the at least one selected target language; and implementing a reverse pseudo-translation of the at least one modified pseudo-translated user interface page. | 7. A computer system for optimizing a multi-language user interface layout via reverse pseudo-translation, the computer system comprising: one or more processors, one or more computer-readable memories, one or more computer-readable tangible storage medium, and program instructions stored on at least one of the one or more tangible storage medium for execution by at least one of the one or more processors via at least one of the one or more memories, wherein the computer system is capable of performing a method comprising: selecting at least one user interface page from a group of user interface pages in a first language; selecting at least one target language from a group of target languages to pseudo-translate the at least one user interface page; specifying at least one layout requirement for formatting the selected at least one user interface page; performing pseudo-translation of the at least one user interface page based on the selected at least one target language; modifying, by a merge algorithm, the at least one pseudo-translated user interface page according to the at least one specified layout requirement, wherein the merge algorithm comprises: applying the at least one specified layout requirement to the at least one pseudo-translated user interface page, wherein the at least one specified layout requirement is applied to the at least one pseudo-translated user interface page in a hierarchy from a most shared selected layout property among the at least one selected target language to a least shared selected layout property among the at least one selected target language; and implementing a reverse pseudo-translation of the at least one modified pseudo-translated user interface page. 8. The computer system of claim 7 further comprising: examining the at least one modified pseudo-translated user interface page for nonconformity with the at least one specified layout requirement; and editing, the at least one modified pseudo-translated user interface page. | 0.59316 |
1. A computer-implemented method executed by one or more processors of a social video search system, the method comprising: receiving a query from a user; obtaining, by the one or more processors, social context information about interactions of the user from a social graph; obtaining, by the one or more processors, a search result using the query; generating, by the one or more processors, an annotation for at least one search result where the annotation indicates the association with a particular source and the relation to the user; and providing, for display to the user, the modified search result and the annotation. | 1. A computer-implemented method executed by one or more processors of a social video search system, the method comprising: receiving a query from a user; obtaining, by the one or more processors, social context information about interactions of the user from a social graph; obtaining, by the one or more processors, a search result using the query; generating, by the one or more processors, an annotation for at least one search result where the annotation indicates the association with a particular source and the relation to the user; and providing, for display to the user, the modified search result and the annotation. 7. The method of claim 1 wherein the annotation is social information about the at least one search result. | 0.708 |
1. A non-transitory computer-readable storage medium storing program instructions executable by a computer to implement a compiler, wherein the compiler is configured to: receive source code for a computer program; determine that the source code includes an assignment context associating a variable with a value, the assignment context having: a declaration expression introducing the variable, wherein the declaration expression specifies a part of a type for the variable and does not specify another part of the type for the variable, wherein: the type for the variable is a parameterized type; the part of the type specified in the declaration expression comprises a ground type; and the part of the type not specified in the declaration expression comprises one or more type arguments for the parameterized type; and an initialization expression whose evaluation will result in the value, wherein a type of the initialization expression is a parameterized type corresponding to a given ground type and one or more type arguments; infer the type for the variable based at least on the specified part of the type in the parameterized type in the declaration expression and on the parameterized type of the initialization expression, wherein to infer the type for the variable, the compiler is configured to: identify a generic type corresponding to the parameterized type of the initialization expression; identify a generic supertype of the identified generic type, wherein the identified generic supertype has the same ground type as a ground type indicated by the part of the type for the variable introduced in the declaration expression; and infer a parameterized type for the variable by parameterizing the generic supertype with one or more type arguments of the parameterized type of the initialization expression; in response to said inferring, bind the variable to the inferred type; and compile the source code into an executable version of the computer program, wherein the compiling is dependent on the binding of the variable to the inferred type. | 1. A non-transitory computer-readable storage medium storing program instructions executable by a computer to implement a compiler, wherein the compiler is configured to: receive source code for a computer program; determine that the source code includes an assignment context associating a variable with a value, the assignment context having: a declaration expression introducing the variable, wherein the declaration expression specifies a part of a type for the variable and does not specify another part of the type for the variable, wherein: the type for the variable is a parameterized type; the part of the type specified in the declaration expression comprises a ground type; and the part of the type not specified in the declaration expression comprises one or more type arguments for the parameterized type; and an initialization expression whose evaluation will result in the value, wherein a type of the initialization expression is a parameterized type corresponding to a given ground type and one or more type arguments; infer the type for the variable based at least on the specified part of the type in the parameterized type in the declaration expression and on the parameterized type of the initialization expression, wherein to infer the type for the variable, the compiler is configured to: identify a generic type corresponding to the parameterized type of the initialization expression; identify a generic supertype of the identified generic type, wherein the identified generic supertype has the same ground type as a ground type indicated by the part of the type for the variable introduced in the declaration expression; and infer a parameterized type for the variable by parameterizing the generic supertype with one or more type arguments of the parameterized type of the initialization expression; in response to said inferring, bind the variable to the inferred type; and compile the source code into an executable version of the computer program, wherein the compiling is dependent on the binding of the variable to the inferred type. 4. The non-transitory computer-readable storage medium of claim 1 , wherein the assignment context is an assignment expression that includes the declaration expression and the initialization expression. | 0.727011 |
1. A lighting system comprising a luminaire that includes: a plurality of light channels that respectively produce different spectral distributions of light; a drive circuit coupled to the light channels and programmable to control relative intensities of light output from the light channels; and an interpreter module that processes a script to generate operating parameters for the drive circuit, wherein the script comprises a plurality of descriptors in a sequence that represents that a lighting scenario changes over time and wherein programming the drive circuit with the operating parameters results in the light channels producing the lighting scenario. | 1. A lighting system comprising a luminaire that includes: a plurality of light channels that respectively produce different spectral distributions of light; a drive circuit coupled to the light channels and programmable to control relative intensities of light output from the light channels; and an interpreter module that processes a script to generate operating parameters for the drive circuit, wherein the script comprises a plurality of descriptors in a sequence that represents that a lighting scenario changes over time and wherein programming the drive circuit with the operating parameters results in the light channels producing the lighting scenario. 9. The system of claim 1 , further comprising a computer in communication with the lighting system, wherein the computer is adapted to provide the script to the luminaire. | 0.610561 |
9. A computer program product comprising a non-transitory computer readable storage medium having a computer readable program stored therein, wherein the computer readable program, when executed on a computing device, causes the computing device to: parse a received input question having a set of question characteristics; compare the set of question characteristics found in the received input question to question characteristics associated with a set of previous questions; responsive to the set of question characteristics found in the received input question matching the question characteristics associated with one or more previous questions in the set of previous questions above a related-question predetermined threshold, identify whether answers to the one or more previous questions were obtained from static information sources or real-time information sources; and responsive to the answers to the one or more previous questions being obtained from the real-time information sources above the predetermined real-time threshold, initially utilize real-time information sources related to the characteristics of the input question to answer the input question. | 9. A computer program product comprising a non-transitory computer readable storage medium having a computer readable program stored therein, wherein the computer readable program, when executed on a computing device, causes the computing device to: parse a received input question having a set of question characteristics; compare the set of question characteristics found in the received input question to question characteristics associated with a set of previous questions; responsive to the set of question characteristics found in the received input question matching the question characteristics associated with one or more previous questions in the set of previous questions above a related-question predetermined threshold, identify whether answers to the one or more previous questions were obtained from static information sources or real-time information sources; and responsive to the answers to the one or more previous questions being obtained from the real-time information sources above the predetermined real-time threshold, initially utilize real-time information sources related to the characteristics of the input question to answer the input question. 10. The computer program product of claim 9 , wherein the computer readable program further causes the computing device to: responsive to the answers to the one or more previous questions failing to be obtained from the real-time information sources above the predetermined real-time threshold and responsive to the answers to the one or more previous questions being obtained from the static information sources above a predetermined static threshold, initially utilize static information sources related to the characteristics of the input question to answer the input question. | 0.54578 |
1. An improved method for optically reading printed characters using an optical character recognition (OCR) apparatus that represents a character as a black pixel shape within a pixel box circumscribing the black pixel shape and for inputting the OCR recognized characters to a text processing system that represents characters in outline font form in which each character is printed within a character box that is larger than a box circumscribing the character, comprising the steps of: optically scanning and recognizing a printed character with an optical character recognition (OCR) apparatus that represents the scanned character as a black pixel shape to be recognized within a pixel box that circumscribes the black pixel shape; the OCR apparatus generating a character code identifying the recognized black pixel shape and a size code identifying the size of the circumscribing pixel box; determining from an outline font table an enlargement ratio for the recognized character, the enlargement ratio being a characteristic outline font parameter for the recognized character which is the ratio between a box dimension of a box that circumscribes the recognized character when printed in outline font form and the same box dimension of the character box of the recognized character when printed in outline font form; and enlarging the size code of the recognized character by the determined enlargement ratio and inputting the resulting enlarged size code as a character box size code for the recognized character to the text processing system. | 1. An improved method for optically reading printed characters using an optical character recognition (OCR) apparatus that represents a character as a black pixel shape within a pixel box circumscribing the black pixel shape and for inputting the OCR recognized characters to a text processing system that represents characters in outline font form in which each character is printed within a character box that is larger than a box circumscribing the character, comprising the steps of: optically scanning and recognizing a printed character with an optical character recognition (OCR) apparatus that represents the scanned character as a black pixel shape to be recognized within a pixel box that circumscribes the black pixel shape; the OCR apparatus generating a character code identifying the recognized black pixel shape and a size code identifying the size of the circumscribing pixel box; determining from an outline font table an enlargement ratio for the recognized character, the enlargement ratio being a characteristic outline font parameter for the recognized character which is the ratio between a box dimension of a box that circumscribes the recognized character when printed in outline font form and the same box dimension of the character box of the recognized character when printed in outline font form; and enlarging the size code of the recognized character by the determined enlargement ratio and inputting the resulting enlarged size code as a character box size code for the recognized character to the text processing system. 2. An improved method as defined in claim 1 wherein an enlargement ratio is determined from the outline font table for each of two orthogonal box dimensions and the size code of the recognized character is enlarged by the determined enlargement ratios in both of said orthogonal dimensions. | 0.552867 |
19. A system comprising: a first processor for: receiving a specification of a statically typed first interface to a first function, the first interface including a specification of a data type of a parameter of the first function; analyzing the specification of the statically typed first interface to the first function to determine a function signature; determining, based on the analyzing, the function signature; identifying a second function using the function signature, the second function corresponding to the specification of the statically typed first interface to the first function, the second function written using a dynamically typed language; and generating an implementation of the first function that invokes the second function through a dynamically typed function specification associated with the second function. | 19. A system comprising: a first processor for: receiving a specification of a statically typed first interface to a first function, the first interface including a specification of a data type of a parameter of the first function; analyzing the specification of the statically typed first interface to the first function to determine a function signature; determining, based on the analyzing, the function signature; identifying a second function using the function signature, the second function corresponding to the specification of the statically typed first interface to the first function, the second function written using a dynamically typed language; and generating an implementation of the first function that invokes the second function through a dynamically typed function specification associated with the second function. 20. The system of claim 19 , further comprising: a second processor for executing the second function, the first processor executing the first function. | 0.607472 |
17. The non-transitory computer-readable medium of claim 14 , further comprising instructions for: generating a second set of bits based on the first set of bits wherein the second set of bits conveys at least the same information as the first set of bits; associating the second set of bits with a second version identifier that identifies a second format that is different than the first format; wherein the second set of bits comprises a second plurality of distinct subsets of bits: wherein each subset of bits of the second plurality of distinct subsets of bits corresponds to a different topic of a second set of topics; and wherein a topic represented in each of the second plurality of distinct subsets of bits, respectively, is determined according to the second format. | 17. The non-transitory computer-readable medium of claim 14 , further comprising instructions for: generating a second set of bits based on the first set of bits wherein the second set of bits conveys at least the same information as the first set of bits; associating the second set of bits with a second version identifier that identifies a second format that is different than the first format; wherein the second set of bits comprises a second plurality of distinct subsets of bits: wherein each subset of bits of the second plurality of distinct subsets of bits corresponds to a different topic of a second set of topics; and wherein a topic represented in each of the second plurality of distinct subsets of bits, respectively, is determined according to the second format. 23. The non-transitory computer-readable medium of claim 17 , further comprising instructions for: identifying a third subset of bits of the second set of bits that represents the first topic; and causing the third subset of bits to represent a value that is proportional to a value represented by the first subset of bits. | 0.844835 |
18. The method of claim 1 further comprising generating a visual display of the query expressed in the data schema query language. | 18. The method of claim 1 further comprising generating a visual display of the query expressed in the data schema query language. 19. The method of claim 18 wherein the visual display includes a display of the query expressed in the ontology query language. | 0.929391 |
1. A method comprising: parsing a context-enriched message with a server; acquiring a context data from the context-enriched message; generating a context-data tag that corresponds to the context data, wherein the context-data tag comprises a term that describes a context data attribute and wherein a display of the context data is weighted by the value of the context-data as a function of time; and rendering a context-data tag cloud comprising at least one context-data tag into a format accessible by a web browser. | 1. A method comprising: parsing a context-enriched message with a server; acquiring a context data from the context-enriched message; generating a context-data tag that corresponds to the context data, wherein the context-data tag comprises a term that describes a context data attribute and wherein a display of the context data is weighted by the value of the context-data as a function of time; and rendering a context-data tag cloud comprising at least one context-data tag into a format accessible by a web browser. 10. The method of claim 1 , wherein the depiction of the context-data tag comprises a virtual control that triggers the generation of an instruction to the mobile device to provide an updated context data. | 0.586476 |
26. An article of manufacture comprising at least one of a hardware device implementing logic and a computer storage media having computer executable code to cause operations to be performed, the operations comprising: accessing document identifiers for documents including at least one value that is a member of a set of values; generating a number of posting lists associated with a first level, wherein each posting list is associated with a range of consecutive values within the set of values and includes document identifiers for documents including at least one value within the range of consecutive values associated with the posting list, and wherein each document identifier is associated with one value in the set of values included in the document identified by the document identifier; performing at least one iteration of generating posting lists for an additional level, wherein each posting list generated for the additional level is formed by merging at least two posting lists associated with a previous level, wherein each generated posting list at one additional level is associated with consecutive values in the set of values, wherein each document in the generated posting list at the additional level includes one value in the consecutive values associated with the posting list at the additional level, and wherein a new additional level and posting lists associated therewith are generated with each iteration; receiving a query on a query range of values within the set of values; determining a minimum number of posting lists associated with one or more levels having consecutive values that include the query range of values; merging the determined posting lists to form a merged posting list including document identifiers of documents including values within the query range; and returning the document identifiers in the merged posting list. | 26. An article of manufacture comprising at least one of a hardware device implementing logic and a computer storage media having computer executable code to cause operations to be performed, the operations comprising: accessing document identifiers for documents including at least one value that is a member of a set of values; generating a number of posting lists associated with a first level, wherein each posting list is associated with a range of consecutive values within the set of values and includes document identifiers for documents including at least one value within the range of consecutive values associated with the posting list, and wherein each document identifier is associated with one value in the set of values included in the document identified by the document identifier; performing at least one iteration of generating posting lists for an additional level, wherein each posting list generated for the additional level is formed by merging at least two posting lists associated with a previous level, wherein each generated posting list at one additional level is associated with consecutive values in the set of values, wherein each document in the generated posting list at the additional level includes one value in the consecutive values associated with the posting list at the additional level, and wherein a new additional level and posting lists associated therewith are generated with each iteration; receiving a query on a query range of values within the set of values; determining a minimum number of posting lists associated with one or more levels having consecutive values that include the query range of values; merging the determined posting lists to form a merged posting list including document identifiers of documents including values within the query range; and returning the document identifiers in the merged posting list. 33. The article of manufacture of claim 26 , wherein the operations further comprise: filtering at least one of the determined posting lists at the first level in response to the determined posting lists at the first level including values outside of the query range of values to remove values not within the query range of values to form at least one filtered posting list only including values within the query range of values, wherein merging the determined posting lists comprising merging the at least one filtered posting list and determined posting lists that are not subject to filtering. | 0.5 |
14. The method of claim 11 , wherein said medical image is a mammogram, and wherein said predetermined computed image feature set includes one or more of size, spiculatedness, margin sharpness, eccentricity, sphericity, average grey level, contrast, cluster characteristics, and breast density characteristics. | 14. The method of claim 11 , wherein said medical image is a mammogram, and wherein said predetermined computed image feature set includes one or more of size, spiculatedness, margin sharpness, eccentricity, sphericity, average grey level, contrast, cluster characteristics, and breast density characteristics. 15. The method of claim 14 , wherein said predetermined computed image feature set may be augmented, reduced, and/or edited by the user. | 0.965677 |
5. The non-transitory computer-readable recording medium according to claim 1 , further comprising evaluating a data candidate when the determined set includes a plurality of the data items, a first heading identifying the data items and a plurality of second headings equivalent in number to the data items and identified by the data items, wherein the evaluating includes selecting a proper determined set from among combinations of the data items and the second headings by comparing the combinations based on relative positions of the data items and the second headings in the combinations, and the outputting includes outputting the proper determined set. | 5. The non-transitory computer-readable recording medium according to claim 1 , further comprising evaluating a data candidate when the determined set includes a plurality of the data items, a first heading identifying the data items and a plurality of second headings equivalent in number to the data items and identified by the data items, wherein the evaluating includes selecting a proper determined set from among combinations of the data items and the second headings by comparing the combinations based on relative positions of the data items and the second headings in the combinations, and the outputting includes outputting the proper determined set. 7. The non-transitory computer-readable recording medium according to claim 5 , wherein the evaluating includes selecting the proper determined set by comparing areas of regions that encompass the data items and the second headings in the combinations, respectively. | 0.868852 |
1. A method implemented by data processing apparatus, the method comprising: serving, to a user device, a document that includes value text that specifies a first value that is not tagged as a time sensitive attribute value; automatically identifying an entity based on entity text included in document text of the document after serving the document to the user device; automatically, after the document is served to the user device, identifying a time-sensitive attribute for the entity specified by attribute text included in the document text, wherein attributes identified by the document text have not previously been identified for the document as time-sensitive before serving the document; identifying the first value for the time-sensitive attribute based on the value text included in the document text; providing data to the user device that causes a prompt to be displayed in the document, the prompt identifying the first value for the time-sensitive attribute and including a user-selectable interface element that, upon selection, will cause the first value for the time-sensitive attribute to be tagged as a time-sensitive attribute value; and generating, responsive to a selection of the user-selectable interface element, a tag for the first value for the time-sensitive attribute in response to receiving user input indicating a selection of the user-selectable interface element, the tag indicating that the first value is time-sensitive, and wherein the tag causes the value text specifying the first value to be updated in response to one or more predetermined actions; responsive to generating the tag, generating a query specifying the entity; providing the query to a search system that provides a result value for the time-sensitive attribute of the entity included in the query; providing, to a user device that is currently accessing the document, result data that causes presentation of the result value as a replacement for the first value as indicated by the tag. | 1. A method implemented by data processing apparatus, the method comprising: serving, to a user device, a document that includes value text that specifies a first value that is not tagged as a time sensitive attribute value; automatically identifying an entity based on entity text included in document text of the document after serving the document to the user device; automatically, after the document is served to the user device, identifying a time-sensitive attribute for the entity specified by attribute text included in the document text, wherein attributes identified by the document text have not previously been identified for the document as time-sensitive before serving the document; identifying the first value for the time-sensitive attribute based on the value text included in the document text; providing data to the user device that causes a prompt to be displayed in the document, the prompt identifying the first value for the time-sensitive attribute and including a user-selectable interface element that, upon selection, will cause the first value for the time-sensitive attribute to be tagged as a time-sensitive attribute value; and generating, responsive to a selection of the user-selectable interface element, a tag for the first value for the time-sensitive attribute in response to receiving user input indicating a selection of the user-selectable interface element, the tag indicating that the first value is time-sensitive, and wherein the tag causes the value text specifying the first value to be updated in response to one or more predetermined actions; responsive to generating the tag, generating a query specifying the entity; providing the query to a search system that provides a result value for the time-sensitive attribute of the entity included in the query; providing, to a user device that is currently accessing the document, result data that causes presentation of the result value as a replacement for the first value as indicated by the tag. 6. The method of claim 1 , wherein the query specifies the time-sensitive attribute for the entity. | 0.619935 |
9. A system for converting speech to text, the system comprising: a digital audio signal comprising an encoding of a recorded spoken input; means for obtaining at least one measurement of said digital audio signal, the measurement comprising a first measured portion of said recorded spoken input and a second measured portion of said recorded spoken input; means for comparing said first measured portion of said recorded spoken input to a first database of digital audio signal characteristics; means for identifying at least one characteristic of said digital audio signal based on said comparison; means for transcribing said first measured portion of said spoken input using said at least one characteristic of the digital audio signal to create an initial transcription; means for backfilling said first database of digital audio signal characteristics with at least one characteristic from a second database of digital audio signal characteristics; means for identifying a second characteristic of said digital signal by comparing said second measured portion of said digital signal to said backfilled first database of digital audio signal characteristics; and means for transcribing said second measured portion of said recorded spoken input using said second characteristic of said digital signal. | 9. A system for converting speech to text, the system comprising: a digital audio signal comprising an encoding of a recorded spoken input; means for obtaining at least one measurement of said digital audio signal, the measurement comprising a first measured portion of said recorded spoken input and a second measured portion of said recorded spoken input; means for comparing said first measured portion of said recorded spoken input to a first database of digital audio signal characteristics; means for identifying at least one characteristic of said digital audio signal based on said comparison; means for transcribing said first measured portion of said spoken input using said at least one characteristic of the digital audio signal to create an initial transcription; means for backfilling said first database of digital audio signal characteristics with at least one characteristic from a second database of digital audio signal characteristics; means for identifying a second characteristic of said digital signal by comparing said second measured portion of said digital signal to said backfilled first database of digital audio signal characteristics; and means for transcribing said second measured portion of said recorded spoken input using said second characteristic of said digital signal. 14. A system according to claim 9 , further comprising means identifying at least one characteristic of said digital audio signal. | 0.513778 |
16. A system for multimedia information retrieval comprising: a search engine for querying an associated multimedia collection with a first component of a multimedia query and for querying at least a part of the queried multimedia collection with a second component of the multimedia query; a first comparison component for generating a first comparison measure between the first query component and a respective object in the collection for a first media type; a second comparison component for generating a second comparison measure between the second query component and a respective object in the collection for the second media type; a multimedia scoring component for generating aggregated scores for each of a set of objects in the collection based on the first comparison measure and the second comparison measure for the respective object, the multimedia scoring component applying an aggregating function which aggregates the first and second comparison measure in which a first confidence weighting is applied to the first comparison measure which is independent of the second comparison measure and a second confidence weighting is applied to the second comparison measure which is dependent on the first comparison measure, wherein the aqqreqatinq function is of general format:
s cw ( q,o )=α a N ( s a ( q,o )ƒ( s a ( q,o ), r a ( o,q ),θ a ))+α b N ( s b ( q,o ) g ( s a ( q,o ), r a ( o,q ), s b ( q,o ), r b ( o,q ),θ b ) wherein 0<α a <1, 0<α b <1 and α b =1−α a ; S cw is the aggregated score; a represents the first media type; b represents the second media type; s a (q,o) and s b (q,o) are similarity scores between the query q and the object o for the first and second media types respectively; r a (o,q) and r b (o,q) are rankinqs of the object o given by the respective similarity scores s a (q,o) and s b (q,o), with respect to other objects in the collection; ƒ is a function of at least one of r a (o,q) and s a (q,o) and optionally also of θ a ; g is a function of at least one of r a (o,q) and s a (q,o) and at least one of s b (q,o) and r b (o,q) and optionally also of θ b ; θ a is a set of one or more parameters; θ b is a set of one or more parameters; and N represents an optional normalizing operator; and a processor for implementing the search engine, first and second comparison components, and multimedia scoring component. | 16. A system for multimedia information retrieval comprising: a search engine for querying an associated multimedia collection with a first component of a multimedia query and for querying at least a part of the queried multimedia collection with a second component of the multimedia query; a first comparison component for generating a first comparison measure between the first query component and a respective object in the collection for a first media type; a second comparison component for generating a second comparison measure between the second query component and a respective object in the collection for the second media type; a multimedia scoring component for generating aggregated scores for each of a set of objects in the collection based on the first comparison measure and the second comparison measure for the respective object, the multimedia scoring component applying an aggregating function which aggregates the first and second comparison measure in which a first confidence weighting is applied to the first comparison measure which is independent of the second comparison measure and a second confidence weighting is applied to the second comparison measure which is dependent on the first comparison measure, wherein the aqqreqatinq function is of general format:
s cw ( q,o )=α a N ( s a ( q,o )ƒ( s a ( q,o ), r a ( o,q ),θ a ))+α b N ( s b ( q,o ) g ( s a ( q,o ), r a ( o,q ), s b ( q,o ), r b ( o,q ),θ b ) wherein 0<α a <1, 0<α b <1 and α b =1−α a ; S cw is the aggregated score; a represents the first media type; b represents the second media type; s a (q,o) and s b (q,o) are similarity scores between the query q and the object o for the first and second media types respectively; r a (o,q) and r b (o,q) are rankinqs of the object o given by the respective similarity scores s a (q,o) and s b (q,o), with respect to other objects in the collection; ƒ is a function of at least one of r a (o,q) and s a (q,o) and optionally also of θ a ; g is a function of at least one of r a (o,q) and s a (q,o) and at least one of s b (q,o) and r b (o,q) and optionally also of θ b ; θ a is a set of one or more parameters; θ b is a set of one or more parameters; and N represents an optional normalizing operator; and a processor for implementing the search engine, first and second comparison components, and multimedia scoring component. 19. The system of claim 16 , wherein the system is configured for receiving a multimedia query from a user which includes a textual component and an image component. | 0.590612 |
1. A method for providing semi-automatic speech transcription, comprising: (a) receiving audio by an automatic speech detection component; (b) automatically detecting speech in the audio by the automatic speech detection component, wherein the automatically detecting comprises (b1) partitioning the audio into a plurality of frames; (b2) classifying each of the plurality of frames as speech or non-speech based on the feature vector corresponding to each frame; and (b3) grouping the plurality of frames into a plurality of speech segments based on the classifications, wherein the grouping comprises, for a sequence of classifications, switching or not switching the speech or non-speech classification in the sequence based on a comparison of a cost for switching the classification with a cost for not switching the classification; (c) providing by the automatic speech detection component the detected speech as the plurality of speech segments to a transcription tool; (d) providing by the transcription tool each of the plurality of speech segments to a user via a transcription interface; and (e) receiving by the transcription tool via the transcription interface an indication for each of the plurality of speech segments from the user, wherein the indication comprises a transcription of the speech segment or an indication of non-speech for the speech segments. | 1. A method for providing semi-automatic speech transcription, comprising: (a) receiving audio by an automatic speech detection component; (b) automatically detecting speech in the audio by the automatic speech detection component, wherein the automatically detecting comprises (b1) partitioning the audio into a plurality of frames; (b2) classifying each of the plurality of frames as speech or non-speech based on the feature vector corresponding to each frame; and (b3) grouping the plurality of frames into a plurality of speech segments based on the classifications, wherein the grouping comprises, for a sequence of classifications, switching or not switching the speech or non-speech classification in the sequence based on a comparison of a cost for switching the classification with a cost for not switching the classification; (c) providing by the automatic speech detection component the detected speech as the plurality of speech segments to a transcription tool; (d) providing by the transcription tool each of the plurality of speech segments to a user via a transcription interface; and (e) receiving by the transcription tool via the transcription interface an indication for each of the plurality of speech segments from the user, wherein the indication comprises a transcription of the speech segment or an indication of non-speech for the speech segments. 9. The method of claim 1 , wherein the indication further comprises an indication that the speech segment cannot be transcribed. | 0.63028 |
1. A computer readable storage medium containing instructions of a program which, when executed by a processor, performs operations for retrieving data comprising: in response to receiving a query, determining whether the query satisfies at least one of a plurality of conditional constraints, wherein each constraint is programmatically generated by execution of a constraint generation routine that performs a constraint generation operation, comprising: issuing a first query to retrieve distinct values for a first field; receiving results for the first query, the results comprising the distinct values for the first field; for each of the distinct values for the first field, issuing a second query configured to retrieve, for a second field, a result set that satisfies a first condition comprising the first field related to the distinct value by an operator; determining whether a correlation exists between (i) the respective distinct value of the first field and (ii) a respective result set value of the second field; and if a correlation exists, defining a conditional constraint for the first field based on the correlation, wherein the conditional constraint specifies the addition of a second condition to a query if the query includes the respective first condition, wherein the second condition includes the second field related to the respective result set value by an operator; and for each conditional constraint satisfied, adding the respective second condition corresponding to the satisfied conditional constraint to the received query to produce a modified query. | 1. A computer readable storage medium containing instructions of a program which, when executed by a processor, performs operations for retrieving data comprising: in response to receiving a query, determining whether the query satisfies at least one of a plurality of conditional constraints, wherein each constraint is programmatically generated by execution of a constraint generation routine that performs a constraint generation operation, comprising: issuing a first query to retrieve distinct values for a first field; receiving results for the first query, the results comprising the distinct values for the first field; for each of the distinct values for the first field, issuing a second query configured to retrieve, for a second field, a result set that satisfies a first condition comprising the first field related to the distinct value by an operator; determining whether a correlation exists between (i) the respective distinct value of the first field and (ii) a respective result set value of the second field; and if a correlation exists, defining a conditional constraint for the first field based on the correlation, wherein the conditional constraint specifies the addition of a second condition to a query if the query includes the respective first condition, wherein the second condition includes the second field related to the respective result set value by an operator; and for each conditional constraint satisfied, adding the respective second condition corresponding to the satisfied conditional constraint to the received query to produce a modified query. 4. The computer readable medium of claim 1 , further comprising retrieving results corresponding to the modified query from a data repository and wherein the data repository is one of: a relational database, XML database, and object-relational database. | 0.555853 |
1. A computer-implemented method, comprising: selecting automatically a first affinity vector comprising a first plurality of topic affinity level values that are associated with a first user of a computer-implemented system, wherein a plurality of the first plurality of topic affinity level values are each based on an inference from a first one or more usage behaviors; selecting automatically a second affinity vector comprising a second plurality of topic affinity level values that are associated with a second user of the computer-implemented system, wherein a plurality of the second plurality of topic affinity level values are each based on an inference from a second one or more usage behaviors, and wherein the selection of the second affinity vector is in accordance with a determination of a relatively high level of similarity between a plurality of the first plurality of topic affinity level values and a corresponding plurality of the second plurality of topic affinity level values; identifying automatically one or more pairs of contrasting corresponding topic affinity level values by comparing topic affinity level values in the first affinity vector with topic affinity level values in the second affinity vector; and generating automatically a recommendation for delivery to the first user, wherein the recommendation is generated in accordance with the one or more pairs of contrasting corresponding topic affinity level values. | 1. A computer-implemented method, comprising: selecting automatically a first affinity vector comprising a first plurality of topic affinity level values that are associated with a first user of a computer-implemented system, wherein a plurality of the first plurality of topic affinity level values are each based on an inference from a first one or more usage behaviors; selecting automatically a second affinity vector comprising a second plurality of topic affinity level values that are associated with a second user of the computer-implemented system, wherein a plurality of the second plurality of topic affinity level values are each based on an inference from a second one or more usage behaviors, and wherein the selection of the second affinity vector is in accordance with a determination of a relatively high level of similarity between a plurality of the first plurality of topic affinity level values and a corresponding plurality of the second plurality of topic affinity level values; identifying automatically one or more pairs of contrasting corresponding topic affinity level values by comparing topic affinity level values in the first affinity vector with topic affinity level values in the second affinity vector; and generating automatically a recommendation for delivery to the first user, wherein the recommendation is generated in accordance with the one or more pairs of contrasting corresponding topic affinity level values. 3. The method of claim 1 , further comprising: determining the relatively high level of similarity between the plurality of the first plurality of topic affinity level values and the corresponding plurality of the second plurality of topic affinity level values, wherein the relatively high level of similarity is determined by performing a cosine similarity calculation. | 0.544549 |
1. A method of measuring quality of determinations of semantic similarity between documents in a corpus, the method comprising: obtaining a weighted semantic graph of semantic similarity between unstructured text in documents within an analyzed corpus, wherein weights of the semantic graph are inferred by unsupervised learning of the weights by one or more computers, and wherein the semantic graph comprises: more than 1000 nodes, each corresponding to at least one respective document within the analyzed corpus; and more than 2000 weighted edges, each weighted edge linking two of the nodes and having a score indicating an amount of semantic similarity between documents corresponding to the two linked nodes; obtaining access to an external corpus having at least some other documents with unstructured text about entities mentioned in the analyzed corpus, the other documents not being within the analyzed corpus; for each of at least 20 evaluation nodes among the nodes of the graph, by one or more processors, scoring semantic similarity between documents in the analyzed corpus and documents in the external corpus selected as being associated with adjacent nodes to the respective evaluation node, wherein scoring semantic similarity comprises: determining the adjacent node in the graph based on the adjacent node sharing an edge with the respective evaluation node; selecting one or more documents from the external corpus based on the selected documents being associated with the adjacent node; determining n-gram weights of a plurality of n-grams in text of the document corresponding to the adjacent node based on the weight of the edge linking the respective evaluation node to the adjacent node in the semantic graph; and determining one or more exogenous semantic similarity scores between the documents selected from the external corpus and the respective evaluation node, the exogenous semantic similarity scores being determined based on the determined n-gram weights and the presence of corresponding n-grams in the respective documents selected from the external corpus; and determining, by one or more processors, a measure of quality of at least some of the weighted edges of the semantic graph based on the exogenous semantic similarity scores. | 1. A method of measuring quality of determinations of semantic similarity between documents in a corpus, the method comprising: obtaining a weighted semantic graph of semantic similarity between unstructured text in documents within an analyzed corpus, wherein weights of the semantic graph are inferred by unsupervised learning of the weights by one or more computers, and wherein the semantic graph comprises: more than 1000 nodes, each corresponding to at least one respective document within the analyzed corpus; and more than 2000 weighted edges, each weighted edge linking two of the nodes and having a score indicating an amount of semantic similarity between documents corresponding to the two linked nodes; obtaining access to an external corpus having at least some other documents with unstructured text about entities mentioned in the analyzed corpus, the other documents not being within the analyzed corpus; for each of at least 20 evaluation nodes among the nodes of the graph, by one or more processors, scoring semantic similarity between documents in the analyzed corpus and documents in the external corpus selected as being associated with adjacent nodes to the respective evaluation node, wherein scoring semantic similarity comprises: determining the adjacent node in the graph based on the adjacent node sharing an edge with the respective evaluation node; selecting one or more documents from the external corpus based on the selected documents being associated with the adjacent node; determining n-gram weights of a plurality of n-grams in text of the document corresponding to the adjacent node based on the weight of the edge linking the respective evaluation node to the adjacent node in the semantic graph; and determining one or more exogenous semantic similarity scores between the documents selected from the external corpus and the respective evaluation node, the exogenous semantic similarity scores being determined based on the determined n-gram weights and the presence of corresponding n-grams in the respective documents selected from the external corpus; and determining, by one or more processors, a measure of quality of at least some of the weighted edges of the semantic graph based on the exogenous semantic similarity scores. 8. The method of claim 1 , wherein obtaining a weighted semantic graph comprises: obtaining the analyzed corpus, the analyzed corpus comprising more than 5000 documents; for each document in the analyzed corpus, with one or more processers: determining a respective n-gram vector, each n-gram vector comprising a plurality of values each indicating presence of a respective n-gram in text of the respective document, wherein the n-gram vectors indicate at least 500 values and correspond to at least some n-grams including three words; determining the scores indicating the amount of semantic similarity relative to the other documents in the analyzed corpus based on angles between the n-gram vector of the respective document and n-gram vectors of the other documents in the analyzed corpus. | 0.664811 |
21. A non-transitory computer readable medium encoded with a program for creating a glossary, the program comprising instructions for: identifying, in at least one information source, a plurality of glossary items, each of the glossary items identifying a part or a component; identifying a set of glossary items within the plurality of glossary items that comprises a common concept; selecting one of the glossary items in the set of glossary items as a canonical form for the set of glossary items, the canonical form being a most common way of representing the common concept; designating each of the other glossary items in the set of glossary items as a variant glossary item with a variant form that varies from the canonical form; defining, by using the canonical form, at least one syntactic structure for each of at least one semantic classes, the at least one syntactic structure including one of the plurality of glossary items; and searching, in at least one of the at least one information source and a second information source, for the at least one syntactic structure of the semantic class. | 21. A non-transitory computer readable medium encoded with a program for creating a glossary, the program comprising instructions for: identifying, in at least one information source, a plurality of glossary items, each of the glossary items identifying a part or a component; identifying a set of glossary items within the plurality of glossary items that comprises a common concept; selecting one of the glossary items in the set of glossary items as a canonical form for the set of glossary items, the canonical form being a most common way of representing the common concept; designating each of the other glossary items in the set of glossary items as a variant glossary item with a variant form that varies from the canonical form; defining, by using the canonical form, at least one syntactic structure for each of at least one semantic classes, the at least one syntactic structure including one of the plurality of glossary items; and searching, in at least one of the at least one information source and a second information source, for the at least one syntactic structure of the semantic class. 23. The non-transitory computer readable medium according to claim 21 , wherein the at least one semantic class is at least one of: a symptom; a cause; and an action. | 0.617546 |
22. The method of claim 21 including identifying a number of addressees of the electronic document. | 22. The method of claim 21 including identifying a number of addressees of the electronic document. 23. The method of claim 22 including generating a document weight term utilizing the number of addressees of the electronic document. | 0.913687 |
2. Electronic musical apparatus for enabling a performer to control the production of a musical accompaniment including notes having one or more of the musical parameters of rhythm pattern, chord pattern and melodic contour, said accompaniment being produced during a musical performance, said apparatus comprising in combination: harmony selection means for enabling the performer to select one harmony from a plurality of different harmonies and to change from the one harmony to a second harmony within the plurality of different harmonies during the performance, the plurality of different harmonies being defined by a plurality of different chord types having a plurality of different root notes and the one harmony having a defined chord type and a defined root note; processing means responsive to the selection of the one harmony for generating parameter signals defining a first segment of music including a plurality of pitched accompaniment notes arranged in the one harmony and having a first chord pattern, for modifying the parameter signals during the performance in response to a change in the defined root note while retaining the defined chord type in order to define a second segment of music having a second chord pattern different from the first chord pattern and for modifying the parameter signals during the performance in response to a change in the defined chord type in order to harmonically modulate the second segment compared to the first segment; and output means for converting the parameter signals to sound, whereby a performer of limited skill or musical knowledge can play a musically-variable accompaniment to a melody written in any one of a variety of musical keys. | 2. Electronic musical apparatus for enabling a performer to control the production of a musical accompaniment including notes having one or more of the musical parameters of rhythm pattern, chord pattern and melodic contour, said accompaniment being produced during a musical performance, said apparatus comprising in combination: harmony selection means for enabling the performer to select one harmony from a plurality of different harmonies and to change from the one harmony to a second harmony within the plurality of different harmonies during the performance, the plurality of different harmonies being defined by a plurality of different chord types having a plurality of different root notes and the one harmony having a defined chord type and a defined root note; processing means responsive to the selection of the one harmony for generating parameter signals defining a first segment of music including a plurality of pitched accompaniment notes arranged in the one harmony and having a first chord pattern, for modifying the parameter signals during the performance in response to a change in the defined root note while retaining the defined chord type in order to define a second segment of music having a second chord pattern different from the first chord pattern and for modifying the parameter signals during the performance in response to a change in the defined chord type in order to harmonically modulate the second segment compared to the first segment; and output means for converting the parameter signals to sound, whereby a performer of limited skill or musical knowledge can play a musically-variable accompaniment to a melody written in any one of a variety of musical keys. 16. Apparatus, as claimed in claim 2, wherein the processing means also performs the function of modifying the parameter signals to produce a progression of different chords, each chord in the progression having a harmony different from the one harmony. | 0.703445 |
2. A computer system as in claim 1 , wherein the data stream of the first participant comprises a first video of extended facial expressions. | 2. A computer system as in claim 1 , wherein the data stream of the first participant comprises a first video of extended facial expressions. 6. A computer system as in claim 2 , wherein the computer system is configured to receive at least one data stream of the data stream of the first participant and the data stream of the second participant from a wearable device. | 0.955911 |
1. A method, comprising: scanning, by a computer, a set of messages of a user sent to or received from a plurality of persons, the messages comprising a first message from a first person; generating a plurality of profiles for the persons, each profile comprising a name of a respective person from one of the messages, and at least one of a social network profile name or a link to a social network profile for the respective person, the plurality of profiles including a first profile for the first person; extracting information from the messages to form search queries, the extracted information comprising a domain obtained from an address of the first message, the domain corresponding to a first website, the search queries including a first query, and the first query comprising search criteria including the domain; communicating, over a network, with a plurality of servers in an automated way to extract data from the servers, the extracting data comprising querying the servers using the search queries, the extracted data comprising first data obtained from the first website; storing a respective portion of the data extracted from the servers in each profile of the plurality of profiles, the storing comprising storing the first data in the first profile; in response to an incomplete input in an input field for a new address of a new message being composed by the user, identifying a set of persons in the plurality of profiles that match the incomplete input, the set of persons including the first person; determining, using the plurality of profiles, a relevancy score for each person of the set of persons based on a type of communication of the new message, wherein an address for a same type of communication as the new address is given more weight than an address for another type of communication, and the relevancy score further based on types of fields in which addresses of senders and recipients of the messages appear, wherein a weight given for an address in a From field is greater than a weight given for an address in a CC or BCC field; and presenting to the user a plurality of suggestions to complete the incomplete input based on the set of persons, wherein the suggestions are presented in an order based on the respective relevancy score for each person of the set of persons. | 1. A method, comprising: scanning, by a computer, a set of messages of a user sent to or received from a plurality of persons, the messages comprising a first message from a first person; generating a plurality of profiles for the persons, each profile comprising a name of a respective person from one of the messages, and at least one of a social network profile name or a link to a social network profile for the respective person, the plurality of profiles including a first profile for the first person; extracting information from the messages to form search queries, the extracted information comprising a domain obtained from an address of the first message, the domain corresponding to a first website, the search queries including a first query, and the first query comprising search criteria including the domain; communicating, over a network, with a plurality of servers in an automated way to extract data from the servers, the extracting data comprising querying the servers using the search queries, the extracted data comprising first data obtained from the first website; storing a respective portion of the data extracted from the servers in each profile of the plurality of profiles, the storing comprising storing the first data in the first profile; in response to an incomplete input in an input field for a new address of a new message being composed by the user, identifying a set of persons in the plurality of profiles that match the incomplete input, the set of persons including the first person; determining, using the plurality of profiles, a relevancy score for each person of the set of persons based on a type of communication of the new message, wherein an address for a same type of communication as the new address is given more weight than an address for another type of communication, and the relevancy score further based on types of fields in which addresses of senders and recipients of the messages appear, wherein a weight given for an address in a From field is greater than a weight given for an address in a CC or BCC field; and presenting to the user a plurality of suggestions to complete the incomplete input based on the set of persons, wherein the suggestions are presented in an order based on the respective relevancy score for each person of the set of persons. 13. The method of claim 1 , wherein the identifying the set of persons comprises matching the incomplete input to at least one field of the plurality of profiles other than a name field. | 0.535036 |
12. The system of claim 11 , the operations further comprising: searching for a POI listing in a POI data repository containing information that matches the portion of the textual data; generating a new POI listing that includes the at least some of the textual data extracted from the image if no POI listing is found; and storing the new POI listing in the POI data repository. | 12. The system of claim 11 , the operations further comprising: searching for a POI listing in a POI data repository containing information that matches the portion of the textual data; generating a new POI listing that includes the at least some of the textual data extracted from the image if no POI listing is found; and storing the new POI listing in the POI data repository. 13. The system of claim 12 , wherein if an existing POI listing containing information that matches the portion of the textual data is found, the operations further comprise updating the existing POI listing to include the at least some of the textual data extracted from the image. | 0.824254 |
1. A method of presenting images selected from an image store in response to a query, the method comprising: selecting images from the image store relating to the query; for respective selected images: computing a query relevance score relating to the query, and generating a first image instance scaled at a first zoom level that is proportional to the query relevance score; preparing an image instance set of scaled first image instances; presenting the image instance set; and upon detecting a user selection of a selected image instance in the image instance set: selecting a second zoom level for the selected image instance that is greater than the first image instance; requesting a differential image data set supplementing the first image instance at the first zoom level to a second image instance at the second zoom level; and upon receiving the differential image data set, presenting the second image instance comprising the first image instance supplemented with the differential image data set. | 1. A method of presenting images selected from an image store in response to a query, the method comprising: selecting images from the image store relating to the query; for respective selected images: computing a query relevance score relating to the query, and generating a first image instance scaled at a first zoom level that is proportional to the query relevance score; preparing an image instance set of scaled first image instances; presenting the image instance set; and upon detecting a user selection of a selected image instance in the image instance set: selecting a second zoom level for the selected image instance that is greater than the first image instance; requesting a differential image data set supplementing the first image instance at the first zoom level to a second image instance at the second zoom level; and upon receiving the differential image data set, presenting the second image instance comprising the first image instance supplemented with the differential image data set. 8. The method of claim 1 : the presenting restricted to a presentation space, and the generating comprising: generating an image instance scaled proportionally to the query relevance score of the image and relative to at least one dimension of the presentation space. | 0.715148 |
4. A method performed by a data processing apparatus, comprising: receiving query suggestion requests from a client device, each query suggestion request having been generated in response to a partial query input in a query input field of a search resource presented at the client device; in response to each query suggestion request: providing, to the client device, query suggestions responsive to the request; determining, for each query suggestion provided to the client device, a quality measure for the query suggestion; determining if a prediction criterion is met, the prediction criterion being independent of a user selection of a query suggestion provided to the client device in response to one or more query suggestion requests and independent of receiving a completed query from the client device; in response to determining that the prediction criterion is met, providing search results to the client device, each of the search results identifying a particular resource that satisfies a query, and includes a resource locator for the resource, comprising: determining whether a query suggestion has a quality measure that meets a threshold, the determining comprising: determining, for the query suggestion, a value indicating quality of resources referenced by search results responsive to the query suggestion, wherein a quality of each resource is determined by a valid prediction rate that is based on a ratio of number of times a search result referencing the resource was selected when provided in response to the query suggestion to a number of times a search result referencing the resource was provided in response to the query suggestion; in response to determining that a query suggestion provided to the client device has a quality measure that meets the threshold, providing, to the client device, search results for the query suggestion having the quality measure that meets the threshold, the search results being responsive to the query suggestion; and in response to determining that none of the query suggestions provided to the client device have a quality measure that meets the threshold, providing, to the client device, search results for only the partial query, the search results being responsive to the partial query; in response to determining that the prediction criterion is not met, not providing the search results to the client device. | 4. A method performed by a data processing apparatus, comprising: receiving query suggestion requests from a client device, each query suggestion request having been generated in response to a partial query input in a query input field of a search resource presented at the client device; in response to each query suggestion request: providing, to the client device, query suggestions responsive to the request; determining, for each query suggestion provided to the client device, a quality measure for the query suggestion; determining if a prediction criterion is met, the prediction criterion being independent of a user selection of a query suggestion provided to the client device in response to one or more query suggestion requests and independent of receiving a completed query from the client device; in response to determining that the prediction criterion is met, providing search results to the client device, each of the search results identifying a particular resource that satisfies a query, and includes a resource locator for the resource, comprising: determining whether a query suggestion has a quality measure that meets a threshold, the determining comprising: determining, for the query suggestion, a value indicating quality of resources referenced by search results responsive to the query suggestion, wherein a quality of each resource is determined by a valid prediction rate that is based on a ratio of number of times a search result referencing the resource was selected when provided in response to the query suggestion to a number of times a search result referencing the resource was provided in response to the query suggestion; in response to determining that a query suggestion provided to the client device has a quality measure that meets the threshold, providing, to the client device, search results for the query suggestion having the quality measure that meets the threshold, the search results being responsive to the query suggestion; and in response to determining that none of the query suggestions provided to the client device have a quality measure that meets the threshold, providing, to the client device, search results for only the partial query, the search results being responsive to the partial query; in response to determining that the prediction criterion is not met, not providing the search results to the client device. 6. The method of claim 4 , wherein a quality of each resource is further by a search score that measures a responsiveness of the resource to the query suggestion. | 0.809133 |
2. The method of claim 1 , wherein said determining one or more updates relative to the section of content comprises: locating the copy of the web-based source downloaded by the web crawler; and determining whether the located copy of the web-based source is more up-to-date relative to the section of content contained in the electronic document. | 2. The method of claim 1 , wherein said determining one or more updates relative to the section of content comprises: locating the copy of the web-based source downloaded by the web crawler; and determining whether the located copy of the web-based source is more up-to-date relative to the section of content contained in the electronic document. 3. The method of claim 2 , wherein said determining whether the located copy of the web-based source is more up-to-date relative to the section of content in the electronic document comprises: determining a time at which the copy of the web-based source was downloaded by the web crawler; and determining that the copy of the web-based source is more up-to-date relative to the section of content in the electronic document if the determined time is more recent than a time that the electronic document was last edited. | 0.713826 |
2. The media of claim 1 , wherein the method further includes receiving a selection of a search mode, wherein the search mode is set to match related concepts, and wherein the one or more components of the electronic medical record matches the search query when a clinical concept conveyed by the search query matches at least one related clinical concept recited in the one or more components of the electronic medical record, and wherein the clinical concept describes any aspect of a person's health condition. | 2. The media of claim 1 , wherein the method further includes receiving a selection of a search mode, wherein the search mode is set to match related concepts, and wherein the one or more components of the electronic medical record matches the search query when a clinical concept conveyed by the search query matches at least one related clinical concept recited in the one or more components of the electronic medical record, and wherein the clinical concept describes any aspect of a person's health condition. 3. The media of claim 2 , wherein determining the query-responsiveness score of each of the one or more components further includes: wherein the query-responsiveness score for the individual component is increased when the patient-subject status for one or more of the clinical concepts identified within the component is affirmative, when the truth status for one or more of the clinical concepts identified within the component equals positive, when the clinical-usage context for one or more of the clinical concepts identified within the component directly relates to the patient, when the document-importance factor is high, and when the specificity factor for one or more of the clinical concepts identified within the component is high. | 0.882241 |
15. A computer-implemented machine translation decoding method comprising: receiving as input a text segment in a source language to be translated into a target language; generating an initial translation by the computer as an initial current target language translation; estimating a probability of correctness of the initial translation by the computer, the probability based on alignment links between words and phrases in the source language and words and phrases in the target language; applying one or more modification operators by the computer to the initial current target language translation to generate one or more modified target language translations; estimating a probability of correctness of the one or more modified target language translations, the probability based on alignment links between words and phrases in the source language and words and phrases in the target language; determining by the computer whether one or more of the modified target language translations represents an improved translation in comparison with the initial current target language translation by comparing the estimated probability of correctness of the initial translation with the estimated probability of correctness of the one or more modified target language translations; iteratively modifying the current target language translation of the source language text segment based on the determination; and repeating said applying, said determining and said iteratively modifying until occurrence of a termination condition. | 15. A computer-implemented machine translation decoding method comprising: receiving as input a text segment in a source language to be translated into a target language; generating an initial translation by the computer as an initial current target language translation; estimating a probability of correctness of the initial translation by the computer, the probability based on alignment links between words and phrases in the source language and words and phrases in the target language; applying one or more modification operators by the computer to the initial current target language translation to generate one or more modified target language translations; estimating a probability of correctness of the one or more modified target language translations, the probability based on alignment links between words and phrases in the source language and words and phrases in the target language; determining by the computer whether one or more of the modified target language translations represents an improved translation in comparison with the initial current target language translation by comparing the estimated probability of correctness of the initial translation with the estimated probability of correctness of the one or more modified target language translations; iteratively modifying the current target language translation of the source language text segment based on the determination; and repeating said applying, said determining and said iteratively modifying until occurrence of a termination condition. 25. The method of claim 15 wherein iteratively modifying the translation comprises performing at each iteration one or more modification operations on the translation. | 0.737525 |
4. The apparatus of claim 1 , wherein the start-shot determination unit comprises: a pre-processing unit to determine frames belonging to a respective scene by detecting scene change among frames included in the video sequences and to obtain the total number of main characters appearing in the video sequences; a face detection unit to detect faces from the determined frames belonging to the respective scene to determine face detection frames; and a key-frame determination unit to cluster the determined face detection frames according to the main characters corresponding to the total number of main characters to determine the key-frames. | 4. The apparatus of claim 1 , wherein the start-shot determination unit comprises: a pre-processing unit to determine frames belonging to a respective scene by detecting scene change among frames included in the video sequences and to obtain the total number of main characters appearing in the video sequences; a face detection unit to detect faces from the determined frames belonging to the respective scene to determine face detection frames; and a key-frame determination unit to cluster the determined face detection frames according to the main characters corresponding to the total number of main characters to determine the key-frames. 10. The apparatus of claim 4 , wherein the key-frame determination unit comprises: a clothing information extraction unit to extract clothing information from a face detection frame; a character clustering unit to perform a character clustering method based on the extracted clothing information; and a main character determination unit to select a cluster corresponding to the main character from a plurality of clusters, clustered in the character clustering unit, corresponding to the total number of main characters and to provide frames included in the selected cluster as key-frames of each topic. | 0.56137 |
1. A method performed by a computer server for inferring location context categories for a set of mobile users having at least two members, comprising: for each mobile user in the set, obtaining at least one location context category; and applying multi-user collaborative machine learning with an objective function to the at least one location context category for each mobile user in the set to infer a single refined location context category for each mobile user in the set to form a matrix. | 1. A method performed by a computer server for inferring location context categories for a set of mobile users having at least two members, comprising: for each mobile user in the set, obtaining at least one location context category; and applying multi-user collaborative machine learning with an objective function to the at least one location context category for each mobile user in the set to infer a single refined location context category for each mobile user in the set to form a matrix. 3. The method of claim 1 , wherein said obtaining step comprises: for each mobile user in the set, sending a respective query to one or more mobile web applications that return the at least one location context category applicable to a submitting one of the mobile users responsive to the uncertain mobile device location data specified in the respective query; and for each mobile user in the set, receiving the at least one location context category from the mobile web application responsive to the respective query. | 0.5 |
14. The system of claim 11 , further comprising at least one configuration menu to adjust display characteristics of the translation viewer. | 14. The system of claim 11 , further comprising at least one configuration menu to adjust display characteristics of the translation viewer. 15. The system of claim 14 , further comprising a configuration switch to disable or enable the translation viewer. | 0.958061 |
1. A method executed by one or more processors of a computing system, the method for instantiating a database having a database schema corresponding to a unified modeling language (UML) specification, the method comprising: extracting at least one model element from a unified modeling language (UML) specification, the unified modeling language (UML) specification being defined by a first model comprising a plurality of model elements, the plurality of model elements of the first model being usable to describe software system structure, software system behavior, and software system architecture for one or more particular software systems; generating a first set of one or more declarative language coding patterns based on first predetermined criteria and the at least one model element, the declarative language coding patterns defining a database having a database schema representing the first model and the at least one model element; translating the generated first set of one or more declarative language coding patterns into a first set of one or more Structured Query Language (SQL) statements that when executed, can instantiate at least a portion of a database having the database schema representing the first model and the at least one model element; and instantiating within a data storage system, by executing the first set of one or more SQL statements, the at least a portion of the database having the database schema representing the first model and the at least one model element of the unified modeling language (UML) specification. | 1. A method executed by one or more processors of a computing system, the method for instantiating a database having a database schema corresponding to a unified modeling language (UML) specification, the method comprising: extracting at least one model element from a unified modeling language (UML) specification, the unified modeling language (UML) specification being defined by a first model comprising a plurality of model elements, the plurality of model elements of the first model being usable to describe software system structure, software system behavior, and software system architecture for one or more particular software systems; generating a first set of one or more declarative language coding patterns based on first predetermined criteria and the at least one model element, the declarative language coding patterns defining a database having a database schema representing the first model and the at least one model element; translating the generated first set of one or more declarative language coding patterns into a first set of one or more Structured Query Language (SQL) statements that when executed, can instantiate at least a portion of a database having the database schema representing the first model and the at least one model element; and instantiating within a data storage system, by executing the first set of one or more SQL statements, the at least a portion of the database having the database schema representing the first model and the at least one model element of the unified modeling language (UML) specification. 4. The method as in claim 1 , wherein the model includes a set of classes; wherein the first database includes a first set of tables and a first set of views of the first set of tables; and wherein the first set of views correspond to the set of classes in the model. | 0.560637 |
10. A speech recognition method for a speech recognition system comprising the steps of: (a) receiving input speech from a user; (b) processing said input speech to obtain at least one parameter value; (c) determining the user's level of experience with using automatic speech recognition, using the at least one parameter value; and (d) adjusting an amount of prompting provided to the user based upon said user's level of experience to assist the user in delivering speech commands to the system wherein the adjusting includes providing users having less experience with automatic speech recognition with prompting that is more detailed than that provided for users having more experience with automatic speech recognition. | 10. A speech recognition method for a speech recognition system comprising the steps of: (a) receiving input speech from a user; (b) processing said input speech to obtain at least one parameter value; (c) determining the user's level of experience with using automatic speech recognition, using the at least one parameter value; and (d) adjusting an amount of prompting provided to the user based upon said user's level of experience to assist the user in delivering speech commands to the system wherein the adjusting includes providing users having less experience with automatic speech recognition with prompting that is more detailed than that provided for users having more experience with automatic speech recognition. 15. The speech recognition method of claim 10 , wherein step (d) includes providing at least one of confirmation responses to user commands, instructions to guide the user to complete a request for an action, or playing an automated help message. | 0.578173 |
25. A distributed system operating in a landscape of computer systems providing message-based services defined in a service registry, the system comprising: a graphical user interface embodied by computer readable instructions executable by at least one processor, for querying supply part shortage information associated with a service part supply plan, using a request; a first memory storing a user interface controller for processing the request and involving a message including a message package derived from a common business object model, where the common business object model includes business objects having relationships that enable derivation of message-based service interfaces and message packages, the message package hierarchically organized as: a service part supply plan supply chain management shortage overview by elements query message entity; and at a first hierarchical level of the first message package, a selection package, where the selection package includes, at a second hierarchical level within the first message package, a service part supply plan supply chain management shortage overview by elements entity, and where the service part supply plan supply chain management shortage overview by elements entity includes, at a third hierarchical level within the first message package, a selection by actual result indicator and at least one of a selection by demand planner group code, a selection by service part planning product group code, a selection by ship from location internal identifier (ID), and a selection by ship to location internal ID; and a second memory, remote from the graphical user interface, storing a plurality of message-based service interfaces derived from the common business object model to provide consistent semantics with messages derived from the common business object model, where one of the message-based service interfaces is operable to process the message via the service interface, where processing the message includes unpacking the first message package based on the common business object model. | 25. A distributed system operating in a landscape of computer systems providing message-based services defined in a service registry, the system comprising: a graphical user interface embodied by computer readable instructions executable by at least one processor, for querying supply part shortage information associated with a service part supply plan, using a request; a first memory storing a user interface controller for processing the request and involving a message including a message package derived from a common business object model, where the common business object model includes business objects having relationships that enable derivation of message-based service interfaces and message packages, the message package hierarchically organized as: a service part supply plan supply chain management shortage overview by elements query message entity; and at a first hierarchical level of the first message package, a selection package, where the selection package includes, at a second hierarchical level within the first message package, a service part supply plan supply chain management shortage overview by elements entity, and where the service part supply plan supply chain management shortage overview by elements entity includes, at a third hierarchical level within the first message package, a selection by actual result indicator and at least one of a selection by demand planner group code, a selection by service part planning product group code, a selection by ship from location internal identifier (ID), and a selection by ship to location internal ID; and a second memory, remote from the graphical user interface, storing a plurality of message-based service interfaces derived from the common business object model to provide consistent semantics with messages derived from the common business object model, where one of the message-based service interfaces is operable to process the message via the service interface, where processing the message includes unpacking the first message package based on the common business object model. 27. The distributed system of claim 25 , wherein the first memory is remote from the second memory. | 0.94186 |
4. The method of claim 1 further comprising: transmitting information corresponding to the first number of the first group of the plurality of users making the first selection and the second number of the second group of the plurality of users making the second selection to a broadcast media studio corresponding to the media content. | 4. The method of claim 1 further comprising: transmitting information corresponding to the first number of the first group of the plurality of users making the first selection and the second number of the second group of the plurality of users making the second selection to a broadcast media studio corresponding to the media content. 5. The method of claim 4 further comprising: adjusting a feature of the media content based on the first number of the first group of the plurality of users making the first selection and the second number of the second group of the plurality of users making the second selection. | 0.933882 |
1. A system that generates recommendations of post-capture users to edit digital media content, the system comprising: one or more physical computer processors configured by computer readable instructions to: obtain contextual parameters of digital media content, the digital media content being associated with a content capture user and/or an end user, the contextual parameters defining one or more temporal attributes and/or spatial attributes associated with capture of the digital media content; receive editing parameters selected by the content capture user and/or the end user, the editing parameters defining one or more editing attributes of an edited version of the digital media content to be created, the one or more editing attributes including one or more selected moments of interest within the digital media content to include within the edited version of the digital media content, wherein receiving the one or more selected moments of interest includes: obtaining, via a first client computing platform, a portion of the digital media content, the portion of the digital media content having a time duration; effectuate transmission of the portion of the digital media content to the first client computing platform for presentation; and receiving, from the first client computing platform, a selection of one or more moments of interest within the portion of the digital media content, individual moments of interest corresponding to individual points in time within the time duration of the portion of the digital media content; obtain post-capture user profiles, individual post-capture user profiles including expertise attributes associated with individual post-capture users, the expertise attributes including stated information and feedback information, the stated information being provided by the post-capture users themselves and the feedback information including information provided by one or more of content capture users and/or end users for whom the individual post-capture users have created edited versions of other digital media content; identify a set of post-capture users as potential matches for creating the edited version of the digital media content based upon the contextual parameters, the editing parameters, and the one or more expertise attributes of the post-capture user profiles; and effectuate presentation of the set of post-capture users to the content capture user and/or the end user for selection by the content capture user and/or the end user of one of the post-capture users from the set of post-capture users to create the edited version of the digital media content. | 1. A system that generates recommendations of post-capture users to edit digital media content, the system comprising: one or more physical computer processors configured by computer readable instructions to: obtain contextual parameters of digital media content, the digital media content being associated with a content capture user and/or an end user, the contextual parameters defining one or more temporal attributes and/or spatial attributes associated with capture of the digital media content; receive editing parameters selected by the content capture user and/or the end user, the editing parameters defining one or more editing attributes of an edited version of the digital media content to be created, the one or more editing attributes including one or more selected moments of interest within the digital media content to include within the edited version of the digital media content, wherein receiving the one or more selected moments of interest includes: obtaining, via a first client computing platform, a portion of the digital media content, the portion of the digital media content having a time duration; effectuate transmission of the portion of the digital media content to the first client computing platform for presentation; and receiving, from the first client computing platform, a selection of one or more moments of interest within the portion of the digital media content, individual moments of interest corresponding to individual points in time within the time duration of the portion of the digital media content; obtain post-capture user profiles, individual post-capture user profiles including expertise attributes associated with individual post-capture users, the expertise attributes including stated information and feedback information, the stated information being provided by the post-capture users themselves and the feedback information including information provided by one or more of content capture users and/or end users for whom the individual post-capture users have created edited versions of other digital media content; identify a set of post-capture users as potential matches for creating the edited version of the digital media content based upon the contextual parameters, the editing parameters, and the one or more expertise attributes of the post-capture user profiles; and effectuate presentation of the set of post-capture users to the content capture user and/or the end user for selection by the content capture user and/or the end user of one of the post-capture users from the set of post-capture users to create the edited version of the digital media content. 7. The system of claim 1 , wherein the one or more processors are further configured to: receive a selection of one of the post-capture users from the set of post-capture users to create the edited version of the digital media content. | 0.530488 |
1. A method of training an expert-assisted phoneme recognition neural network system, the method comprising: at an expert-assisted phoneme recognition neural network system configured to generate one or more phoneme candidates as recognized within audible signal data, the expert-assisted phoneme recognition neural network system including an ensemble phoneme recognition neural network and a phoneme-specific experts system: selecting a target problematic phoneme; synthesizing a targeted training data set including an overemphasis of examples of the target problematic phoneme; synthesizing respective problematic phoneme-specific weight values for a problematic phoneme-specific expert neural network (PPENN) included in the phoneme-specific experts system, in accordance with a determination that the respective problematic phoneme-specific weight values satisfy an error convergence threshold, by: providing the synthesized target training data set to the PPENN; determining an output of the PPENN in response to providing the synthesized target training data set to the PPENN; updating the respective problematic phoneme-specific weight values for the PPENN based on a function of the output of the PPENN; and iteratively providing the synthesized target training data to the PPENN and updating the respective problematic phoneme-specific weight values until a set of updated weights satisfies the error convergence threshold. | 1. A method of training an expert-assisted phoneme recognition neural network system, the method comprising: at an expert-assisted phoneme recognition neural network system configured to generate one or more phoneme candidates as recognized within audible signal data, the expert-assisted phoneme recognition neural network system including an ensemble phoneme recognition neural network and a phoneme-specific experts system: selecting a target problematic phoneme; synthesizing a targeted training data set including an overemphasis of examples of the target problematic phoneme; synthesizing respective problematic phoneme-specific weight values for a problematic phoneme-specific expert neural network (PPENN) included in the phoneme-specific experts system, in accordance with a determination that the respective problematic phoneme-specific weight values satisfy an error convergence threshold, by: providing the synthesized target training data set to the PPENN; determining an output of the PPENN in response to providing the synthesized target training data set to the PPENN; updating the respective problematic phoneme-specific weight values for the PPENN based on a function of the output of the PPENN; and iteratively providing the synthesized target training data to the PPENN and updating the respective problematic phoneme-specific weight values until a set of updated weights satisfies the error convergence threshold. 4. The method of claim 1 , wherein the function of the output of the PPENN includes a partial derivative function of the output of the PPENN. | 0.565558 |
16. Apparatus as defined by claim 15 further comprising means responsive to said correlation figure and supplementary correlation figure for generating an occurrence indication indicative of the training word which corresponds most closely to the command word. | 16. Apparatus as defined by claim 15 further comprising means responsive to said correlation figure and supplementary correlation figure for generating an occurrence indication indicative of the training word which corresponds most closely to the command word. 17. Apparatus as defined by claim 16 wherein said means for generating an occurrence indication includes means for determining whether said combined correlation figure and supplementary correlation figure exceeds a predetermined threshold. | 0.883252 |
27. The system of claim 26 , wherein the term of experience is rounded down to a unit of time. | 27. The system of claim 26 , wherein the term of experience is rounded down to a unit of time. 29. The system of claim 27 , wherein the unit of time is an integer. | 0.983929 |
9. A non-transitory computer readable medium encoded with a computer program comprising instructions that when executed operate to cause a computer to perform operations comprising: segmenting textual content for each of a plurality of distinct book content items into word strings, each word string including a predefined number of contiguous words in the textual content of a distinct book content item; classifying word strings having a frequency of occurrence that is less than a threshold frequency of occurrence as uncommon word strings, the frequency of occurrence for each word string being a number of occurrences of the word string relative to the total number of word strings in the plurality of distinct book content items; representing a weighted graph in computer memory, the weighted graph including a plurality of distinct nodes, each distinct node representing a corresponding distinct book content item in the plurality of distinct book content items, and where an edge exists in the weighted graph between each pair of distinct nodes that represent distinct book content items that both include a matching uncommon word string, each edge in the weighted graph being weighted based on a relative importance of the distinct node from which the edge originates; for each distinct node: identifying matching word strings in the textual content of other distinct book content items that are represented by other distinct nodes, each matching word string being a word string that matches an uncommon word string in the textual content of the distinct book content item corresponding to the distinct node; and generating edges in the weighted graph connecting the distinct node to other distinct nodes corresponding to the other distinct book content items, each edge representing one or more matches of an uncommon word string to one or more matching word strings in the textual content of another distinct book content item; and determining a rank score for each distinct book content item based on the edges between the distinct nodes representing the distinct book content items, the rank score being a score indicative of the importance of each distinct book content item relative to other distinct book content items. | 9. A non-transitory computer readable medium encoded with a computer program comprising instructions that when executed operate to cause a computer to perform operations comprising: segmenting textual content for each of a plurality of distinct book content items into word strings, each word string including a predefined number of contiguous words in the textual content of a distinct book content item; classifying word strings having a frequency of occurrence that is less than a threshold frequency of occurrence as uncommon word strings, the frequency of occurrence for each word string being a number of occurrences of the word string relative to the total number of word strings in the plurality of distinct book content items; representing a weighted graph in computer memory, the weighted graph including a plurality of distinct nodes, each distinct node representing a corresponding distinct book content item in the plurality of distinct book content items, and where an edge exists in the weighted graph between each pair of distinct nodes that represent distinct book content items that both include a matching uncommon word string, each edge in the weighted graph being weighted based on a relative importance of the distinct node from which the edge originates; for each distinct node: identifying matching word strings in the textual content of other distinct book content items that are represented by other distinct nodes, each matching word string being a word string that matches an uncommon word string in the textual content of the distinct book content item corresponding to the distinct node; and generating edges in the weighted graph connecting the distinct node to other distinct nodes corresponding to the other distinct book content items, each edge representing one or more matches of an uncommon word string to one or more matching word strings in the textual content of another distinct book content item; and determining a rank score for each distinct book content item based on the edges between the distinct nodes representing the distinct book content items, the rank score being a score indicative of the importance of each distinct book content item relative to other distinct book content items. 10. The computer readable media of claim 9 , further causing a computer to perform operations comprising: receiving a relevance score for each of the plurality of distinct book content items, the relevance score being a measure of relevance of the distinct book content item to a search query; ranking the plurality of distinct book content items based on the rank score and the relevance score; and ordering search results for the book content items, the search results being ordered according to the ranking of the plurality of distinct book content items. | 0.525046 |
26. A system comprising a computing device connected to a printer, wherein: the computing device is configured to: receive a representation of the document, the representation of the document specifying a first visual content item and a second visual content item of the document; select a portion of the representation of the document, the selected portion specifying, at least in part, the first visual content item; determine a characteristic of the selected portion; assign a score to the selected portion, based on the determined characteristic; receive user input selecting one of a plurality of ranges of score values, each of the plurality of ranges of score values corresponding to an amount of content from the document to be printed; determine whether the score is within the selected range of score values; when the score is within the selected range, generate a filtered version of the document that includes the first visual content item, the filtered version of the document omitting the second visual content item; and send the filtered version of the document to a printer for printing; and the printer is configured to: receive the filtered version of the document, and print a hard copy of the filtered version of the document. | 26. A system comprising a computing device connected to a printer, wherein: the computing device is configured to: receive a representation of the document, the representation of the document specifying a first visual content item and a second visual content item of the document; select a portion of the representation of the document, the selected portion specifying, at least in part, the first visual content item; determine a characteristic of the selected portion; assign a score to the selected portion, based on the determined characteristic; receive user input selecting one of a plurality of ranges of score values, each of the plurality of ranges of score values corresponding to an amount of content from the document to be printed; determine whether the score is within the selected range of score values; when the score is within the selected range, generate a filtered version of the document that includes the first visual content item, the filtered version of the document omitting the second visual content item; and send the filtered version of the document to a printer for printing; and the printer is configured to: receive the filtered version of the document, and print a hard copy of the filtered version of the document. 28. The system of claim 26 , wherein the score is assigned based on a value of a markup language attribute that is part of the selected portion of the representation of the document. | 0.89242 |
1. A computer-implemented method for encoding an alphanumeric message, comprising: determining whether a phrase comprising a string of one or more symbols in the alphanumeric message is a numeric phrase, and if so, encoding the phrase in a compressed form according to a predefined numeric model; when the phrase is not a numeric phrase, matching strings of at least one symbol in the alphanumeric message with corresponding entries in a predetermined phrase dictionary, wherein each dictionary entry corresponds to a phrase code and replacing matched strings in the alphanumeric message with corresponding phrase codes to result in creating a compressed encoding of the phrase; and encoding at least a portion of the remaining symbols in the alphanumeric message using a statistical model based coding method. | 1. A computer-implemented method for encoding an alphanumeric message, comprising: determining whether a phrase comprising a string of one or more symbols in the alphanumeric message is a numeric phrase, and if so, encoding the phrase in a compressed form according to a predefined numeric model; when the phrase is not a numeric phrase, matching strings of at least one symbol in the alphanumeric message with corresponding entries in a predetermined phrase dictionary, wherein each dictionary entry corresponds to a phrase code and replacing matched strings in the alphanumeric message with corresponding phrase codes to result in creating a compressed encoding of the phrase; and encoding at least a portion of the remaining symbols in the alphanumeric message using a statistical model based coding method. 7. The method of claim 1, wherein the encoding is used in a paging system and the dictionary entries are specifically adapted to include phrase codes for alphanumeric phrases particularly useful to users of the paging system. | 0.722724 |
1. A computer-implemented method for creating a prototype that includes motion control, machine vision, and Data Acquisition (DAQ) functionality, the method comprising: displaying a graphical user interface (GUI) that provides GUI access to a set of operations, wherein the set of operations includes one or more motion control operations, one or more machine vision operations, and one or more DAQ operations; creating a sequence of operations, wherein creating the sequence comprises including a plurality of operations in the sequence in response to user input selecting each operation in the plurality of operations from the GUI, wherein including the plurality of operations in the sequence in response to the user input selecting each operation in the plurality of operations from the GUI comprises including the plurality of operations in the sequence without receiving user input specifying program code for performing the plurality of operations; wherein the plurality of operations included in the sequence includes at least one motion control operation, at least one machine vision operation, and at least one DAQ operation, wherein at least one of the DAQ operations included in the sequence is operable to control a DAQ measurement device to acquire measurement data of a device under test; wherein the method further comprises storing information representing the sequence of operations in a data structure, wherein the sequence of operations comprises the prototype. | 1. A computer-implemented method for creating a prototype that includes motion control, machine vision, and Data Acquisition (DAQ) functionality, the method comprising: displaying a graphical user interface (GUI) that provides GUI access to a set of operations, wherein the set of operations includes one or more motion control operations, one or more machine vision operations, and one or more DAQ operations; creating a sequence of operations, wherein creating the sequence comprises including a plurality of operations in the sequence in response to user input selecting each operation in the plurality of operations from the GUI, wherein including the plurality of operations in the sequence in response to the user input selecting each operation in the plurality of operations from the GUI comprises including the plurality of operations in the sequence without receiving user input specifying program code for performing the plurality of operations; wherein the plurality of operations included in the sequence includes at least one motion control operation, at least one machine vision operation, and at least one DAQ operation, wherein at least one of the DAQ operations included in the sequence is operable to control a DAQ measurement device to acquire measurement data of a device under test; wherein the method further comprises storing information representing the sequence of operations in a data structure, wherein the sequence of operations comprises the prototype. 28. The method of claim 1 , further comprising: displaying a visual indication of results of performing the sequence while the sequence is being created, wherein the visual indication enables a user to evaluate the results of performing the sequence, wherein interactively displaying the visual indication comprises: for each operation in at least a subset of the operations included in the sequence, updating the displayed visual indication in response to including the operation in the sequence in order to visually indicate a change in the results of performing the sequence, wherein the change is caused by including the operation in the sequence, wherein updating the displayed visual indication provides interactive visual feedback to the user indicating the change caused by including the operation in the sequence. | 0.586659 |
1. A method for improving a speech recognition system comprising the steps of: initiating a speech dialog with at least one point in the dialog where there is a grammar of possible responses and a set of at least one expected response; wherein the set is a subset of the grammar and the set includes the most likely response or responses expected to be uttered by a user at the at least one point in the speech dialog, the set of at least one expected response for the at least one point being known in the speech recognition system before receiving input speech from the user; progressing through the speech dialog until arriving at the at least one point; receiving input speech from the user; generating acoustic features of the input speech using an apparatus with at least one hardware-implemented processor; comparing the generated input speech acoustic features to acoustic models associated with words in the grammar to generate a hypothesis; comparing the hypothesis with at least one expected response in the set to determine if the hypothesis matches the at least one expected response in the set; if the hypothesis matches the at least one expected response in the set, adapting at least one acoustic model corresponding to the matched expected response using the acoustic features of the input speech to use the at least one adapted model with future input speech in the speech recognition system, otherwise, not adapting the at least one acoustic model corresponding to the expected response. | 1. A method for improving a speech recognition system comprising the steps of: initiating a speech dialog with at least one point in the dialog where there is a grammar of possible responses and a set of at least one expected response; wherein the set is a subset of the grammar and the set includes the most likely response or responses expected to be uttered by a user at the at least one point in the speech dialog, the set of at least one expected response for the at least one point being known in the speech recognition system before receiving input speech from the user; progressing through the speech dialog until arriving at the at least one point; receiving input speech from the user; generating acoustic features of the input speech using an apparatus with at least one hardware-implemented processor; comparing the generated input speech acoustic features to acoustic models associated with words in the grammar to generate a hypothesis; comparing the hypothesis with at least one expected response in the set to determine if the hypothesis matches the at least one expected response in the set; if the hypothesis matches the at least one expected response in the set, adapting at least one acoustic model corresponding to the matched expected response using the acoustic features of the input speech to use the at least one adapted model with future input speech in the speech recognition system, otherwise, not adapting the at least one acoustic model corresponding to the expected response. 9. The method of claim 1 wherein the at least one acoustic model is adapted by creating a new acoustic model based upon the input speech. | 0.605704 |
1. A system providing access to a directory services repository which is stored on a computer system, the claimed system comprising: a directory services application programming interface, also known as the API, which includes at least one callable element that is capable of accessing a component of the directory services repository in response to being called; and a driver which is capable of translating a relational database language statement into an executable API sequence that includes a call to the callable element and produces an API result, the driver also being capable of translating the API result into a relational database result, wherein the directory services repository component includes an effective class and an object having a context the relational database language statement identifies a table and a subset restriction, and the driver and the API together map the effective class to the table and also map the context to the subset restriction. | 1. A system providing access to a directory services repository which is stored on a computer system, the claimed system comprising: a directory services application programming interface, also known as the API, which includes at least one callable element that is capable of accessing a component of the directory services repository in response to being called; and a driver which is capable of translating a relational database language statement into an executable API sequence that includes a call to the callable element and produces an API result, the driver also being capable of translating the API result into a relational database result, wherein the directory services repository component includes an effective class and an object having a context the relational database language statement identifies a table and a subset restriction, and the driver and the API together map the effective class to the table and also map the context to the subset restriction. 7. The system of claim 1, wherein the relational database language statement is written in a Structured Query Language format. | 0.646382 |
1. A method, in a data processing system, for evaluating the performance of a text analysis engine, the method comprising: receiving a plurality of pre-annotated reference documents; receiving a set of annotation types associated with the pre-annotated reference documents; analyzing annotation contexts of reference annotations in the plurality of pre-annotated reference documents using the set of annotation types; identifying similar annotation contexts between the reference annotations and the set of annotation types; responsive to identifying the similar annotation contexts, clustering the similar annotation contexts thereby forming a plurality of reference annotation clusters; computing a set of reference content heterogeneity scores based on the number of reference annotation clusters for each annotation type in the set of annotation types; computing an integral reference content rate for the set of annotation types; and outputting the integral reference content rate to a user. | 1. A method, in a data processing system, for evaluating the performance of a text analysis engine, the method comprising: receiving a plurality of pre-annotated reference documents; receiving a set of annotation types associated with the pre-annotated reference documents; analyzing annotation contexts of reference annotations in the plurality of pre-annotated reference documents using the set of annotation types; identifying similar annotation contexts between the reference annotations and the set of annotation types; responsive to identifying the similar annotation contexts, clustering the similar annotation contexts thereby forming a plurality of reference annotation clusters; computing a set of reference content heterogeneity scores based on the number of reference annotation clusters for each annotation type in the set of annotation types; computing an integral reference content rate for the set of annotation types; and outputting the integral reference content rate to a user. 6. The method of claim 1 , further comprising: computing performance rates for each annotation type in the set of annotation types. | 0.691331 |
6. The method of claim 1 , further comprising determining which nodes and contexts of the input document are streamable. | 6. The method of claim 1 , further comprising determining which nodes and contexts of the input document are streamable. 9. The method of claim 6 , wherein determining which nodes and contexts of an input document are streamable comprises: initializing all data references as being streamable; matching a root path of the input document into a template; setting the root path of the input document as a current context; determining if the current context is streamable based on a data reference defining the current context; streaming the current context during parsing of the input document in response to the current context being streamable; and buffering the current context in response to the current context being non-streamable. | 0.795817 |
3. The method of claim 1 , further comprising the steps of: creating a subset H of the highest confidence scores; adding the translations in the subset H directly to the parallel corpus; creating a subset L of lowest confidence scores; presenting the subset L to human translators for correction; and adding human corrections to the parallel corpus. | 3. The method of claim 1 , further comprising the steps of: creating a subset H of the highest confidence scores; adding the translations in the subset H directly to the parallel corpus; creating a subset L of lowest confidence scores; presenting the subset L to human translators for correction; and adding human corrections to the parallel corpus. 4. The method of claim 3 , wherein the step of presenting the subset L to human translators for correction comprises presenting a Graphical User Interface (GUI) to the translator, the GUI providing at least the items in subset L, a window to make translation corrections and an update button. | 0.883333 |
1. A method comprising: detecting a publication that was shared by a member of an on-line social networking system; determining that the publication includes a name entity, the name entity comprising a string of characters; selecting one or more candidate profiles from a plurality of member profiles in the on-line social networking system, based on the name entity; selecting a matching profile from the candidate profiles, utilizing one or more disambiguation techniques, the one or more disambiguation techniques comprising utilizing data from a candidate profile from the one or more candidate profiles and data from one or more profiles of connections of the candidate profile; and identifying, using at least one processor, the matching profile from the candidate profiles as a member profile in the on-line social networking system that represents a member referenced by the name entity. | 1. A method comprising: detecting a publication that was shared by a member of an on-line social networking system; determining that the publication includes a name entity, the name entity comprising a string of characters; selecting one or more candidate profiles from a plurality of member profiles in the on-line social networking system, based on the name entity; selecting a matching profile from the candidate profiles, utilizing one or more disambiguation techniques, the one or more disambiguation techniques comprising utilizing data from a candidate profile from the one or more candidate profiles and data from one or more profiles of connections of the candidate profile; and identifying, using at least one processor, the matching profile from the candidate profiles as a member profile in the on-line social networking system that represents a member referenced by the name entity. 5. The method of claim 1 , comprising determining a rank of the name entity corresponding to the member of the on-line social networking system with respect to other name entities corresponding to other members of the on-line social networking system referenced in the publication, the rank being with respect to the publication. | 0.629518 |
16. The method of claim 14 , further comprising: detecting and tracking continuously a subsequent motion or position of the hand responsive to the computer application being launched via a hand gesture, wherein the detected hand motion or position is converted to corresponding motion or position of an on-screen object. | 16. The method of claim 14 , further comprising: detecting and tracking continuously a subsequent motion or position of the hand responsive to the computer application being launched via a hand gesture, wherein the detected hand motion or position is converted to corresponding motion or position of an on-screen object. 17. The method of claim 16 , wherein holding the hand still within a specified tolerance for a specified number of frames is used to indicate readiness to make a selection. | 0.940926 |
3. A computer implemented method of creating a visually enhanced text comprising: (a) providing a standard computer comprising one or more processor, memory, and display; (b) obtaining a first text and at least one additional text, texts comprising corresponding and noncorresponding passages; (c) loading the first text and the at least one additional text into computer memory; (d) displaying the first text in a horizontal line; (e) displaying the at least one additional text interlinearly with the first text; (f) for corresponding passages in the at least one additional text and the first text, vertically aligning the corresponding passages in their respective lines; (g) allowing gaps to form in the first text when noncorresponding text of the at least one additional text is longer than the noncorresponding text in the first text; (h) replacing identical symbols in the corresponding passages with a single copy of the identical symbols in a different font. | 3. A computer implemented method of creating a visually enhanced text comprising: (a) providing a standard computer comprising one or more processor, memory, and display; (b) obtaining a first text and at least one additional text, texts comprising corresponding and noncorresponding passages; (c) loading the first text and the at least one additional text into computer memory; (d) displaying the first text in a horizontal line; (e) displaying the at least one additional text interlinearly with the first text; (f) for corresponding passages in the at least one additional text and the first text, vertically aligning the corresponding passages in their respective lines; (g) allowing gaps to form in the first text when noncorresponding text of the at least one additional text is longer than the noncorresponding text in the first text; (h) replacing identical symbols in the corresponding passages with a single copy of the identical symbols in a different font. 4. The method of claim 3 wherein the first text and the at least one additional text are displayed using different colors. | 0.56383 |
1. One or more computer-readable storage media comprising instructions stored thereon that, responsive to execution by a computing device, cause the computing device to perform operations comprising: collecting posts to a social network and responses to each post by a social network community; binning the posts into different post classes based on a number of the responses to each post, the binning comprising normalizing the number of the responses to each post by dividing the number of the responses to each post by a total number of subscribers of the social network community, generating a histogram of the normalized number of responses to each post, applying Otsu thresholding techniques to the histogram to identify one or more boundaries between distributions of the posts in the histogram, and binning at least one of the distributions of the posts into a popular post class that includes posts that received a high number of responses; extracting features from the posts, the extracted features including at least one non-textual feature; and forming a prediction model by applying a learning model to the different classes of posts and the extracted features. | 1. One or more computer-readable storage media comprising instructions stored thereon that, responsive to execution by a computing device, cause the computing device to perform operations comprising: collecting posts to a social network and responses to each post by a social network community; binning the posts into different post classes based on a number of the responses to each post, the binning comprising normalizing the number of the responses to each post by dividing the number of the responses to each post by a total number of subscribers of the social network community, generating a histogram of the normalized number of responses to each post, applying Otsu thresholding techniques to the histogram to identify one or more boundaries between distributions of the posts in the histogram, and binning at least one of the distributions of the posts into a popular post class that includes posts that received a high number of responses; extracting features from the posts, the extracted features including at least one non-textual feature; and forming a prediction model by applying a learning model to the different classes of posts and the extracted features. 6. The one or more computer-readable storage media of claim 1 , wherein the extracted features further include a differential term frequency-inverse document frequency (Diff-TFIDF) of each post. | 0.621419 |
10. A tangible computer-readable storage medium storing a plurality of instructions which, when executed by a processor, cause the processor to perform operations for using user provided tags for searching, the operations comprising: collecting, by an application server, a plurality of user provided tags associated with a plurality of entities, wherein the plurality of user provided tags comprises semantic descriptions entered by a plurality of different users; creating, by the application server, a tag topological network layer that is managed by a service provider, wherein the tag topological network layer predefines a next entity for each one of the plurality of entities based upon the plurality of user provided tags; receiving, by the application server, a user query that contains a search term; and generating, by the application server, a search result containing an entity of the plurality of entities in the tag topological network layer, wherein the entity is found based on a distance measure of a tag vector, t p , for the entity, p, wherein a function t p (i) represents a measure of a weight of a tag, i, that is used to tag the entity, p, based on a normalized count of times tag, i, is used to tag the entity, p, wherein the entity contains a link to another entity in accordance with the tag topological network layer, wherein the link is created in accordance with the tag vector of the entity. | 10. A tangible computer-readable storage medium storing a plurality of instructions which, when executed by a processor, cause the processor to perform operations for using user provided tags for searching, the operations comprising: collecting, by an application server, a plurality of user provided tags associated with a plurality of entities, wherein the plurality of user provided tags comprises semantic descriptions entered by a plurality of different users; creating, by the application server, a tag topological network layer that is managed by a service provider, wherein the tag topological network layer predefines a next entity for each one of the plurality of entities based upon the plurality of user provided tags; receiving, by the application server, a user query that contains a search term; and generating, by the application server, a search result containing an entity of the plurality of entities in the tag topological network layer, wherein the entity is found based on a distance measure of a tag vector, t p , for the entity, p, wherein a function t p (i) represents a measure of a weight of a tag, i, that is used to tag the entity, p, based on a normalized count of times tag, i, is used to tag the entity, p, wherein the entity contains a link to another entity in accordance with the tag topological network layer, wherein the link is created in accordance with the tag vector of the entity. 14. The tangible computer-readable storage medium of claim 10 , wherein the entity comprises a web page. | 0.556762 |
2. The system of claim 1 , wherein the distributed computing cluster is configured to store unstructured data. | 2. The system of claim 1 , wherein the distributed computing cluster is configured to store unstructured data. 3. The system of claim 2 , wherein a query coordinator and a query planner of one of the plurality of data nodes are selected as an initiating query coordinator and an initiating query planner, respectively, for a query from a client. | 0.936349 |
21. The method of claim 15 in which said step for applying a recursive reduction procedure includes the step of applying a repeating reduction procedure wherein: a determination is made as to whether the rule, R, of said acquired grammar element is an AND rule or an OR rule; when said rule, R, of said acquired grammar element is an AND rule, then subrule B, the first subrule of R, is acquired with its repetition symbol, and the subrule NS, the next rule of rule R is acquired with its repetition symbol; a determination is made whether the subrule B is the same as the subrule NS excepting the said repetition symbol of each; in the event subrule B is the same as subrule NS, said repetition symbol of subrule B is combined as a peer with said repetition symbol of NS to derive a resulting repetition symbol which is made the repetition symbol of subrule B; and subrule NS is removed. | 21. The method of claim 15 in which said step for applying a recursive reduction procedure includes the step of applying a repeating reduction procedure wherein: a determination is made as to whether the rule, R, of said acquired grammar element is an AND rule or an OR rule; when said rule, R, of said acquired grammar element is an AND rule, then subrule B, the first subrule of R, is acquired with its repetition symbol, and the subrule NS, the next rule of rule R is acquired with its repetition symbol; a determination is made whether the subrule B is the same as the subrule NS excepting the said repetition symbol of each; in the event subrule B is the same as subrule NS, said repetition symbol of subrule B is combined as a peer with said repetition symbol of NS to derive a resulting repetition symbol which is made the repetition symbol of subrule B; and subrule NS is removed. 23. The method of claim 21 in which: said rule, R of said acquired grammar element is determined to be an OR rule; then subrule C, a subrule of rule R is acquired with its repetition symbol, and another subrule of rule R, subrule N is acquired with its repetition symbol; a determination is made whether the subrule C is the same as the subrule N excepting the said repetition symbol of each; in the event subrule C is the same as subrule N, said repetition symbol of subrule C and said repetition symbol of subrule N are combined as subordinates to derive a resulting repetition symbol which is made the repetition symbol of subrule C; and subrule N is removed. | 0.793606 |
21. The computer-readable medium of claim 20 , further comprising: causing the system to execute the first recording of the base script to generate the first response file; causing the system to execute the second recording of the base script again to generate the second response file; and storing a dynamic value list that comprises the first dynamic value data and the second dynamic value data. | 21. The computer-readable medium of claim 20 , further comprising: causing the system to execute the first recording of the base script to generate the first response file; causing the system to execute the second recording of the base script again to generate the second response file; and storing a dynamic value list that comprises the first dynamic value data and the second dynamic value data. 22. The computer-readable medium of claim 21 , wherein the first recording is identical to the second recording and the first recording and the second recording are for testing a business flow on the system. | 0.838339 |
1. A method comprising: developing, via a processor, a statistical model for a natural language understanding application using human knowledge exclusive of any annotated data; during a first execution of the natural language understanding application via the processor: receiving a sequence of words to yield a received sequence of words; assigning a sequence of tags to the received sequence of words by using the statistical model, to yield annotated words; and when sufficient annotated words become available, updating the statistical model; developing a replacement statistical model for the natural language understanding application using the annotated words by: developing a first part of the replacement model without the human knowledge; developing a second part of the replacement model from both the human knowledge and the annotated words; and when the received and subsequently annotated data is sufficient, weighting a contribution from the first part more to the assigning than when the received and subsequently annotated data is insufficient; and during a second execution of the natural language understanding application, receiving a second sequence of words and assigning a second sequence of tags to the second sequence of words by using the replacement statistical model. | 1. A method comprising: developing, via a processor, a statistical model for a natural language understanding application using human knowledge exclusive of any annotated data; during a first execution of the natural language understanding application via the processor: receiving a sequence of words to yield a received sequence of words; assigning a sequence of tags to the received sequence of words by using the statistical model, to yield annotated words; and when sufficient annotated words become available, updating the statistical model; developing a replacement statistical model for the natural language understanding application using the annotated words by: developing a first part of the replacement model without the human knowledge; developing a second part of the replacement model from both the human knowledge and the annotated words; and when the received and subsequently annotated data is sufficient, weighting a contribution from the first part more to the assigning than when the received and subsequently annotated data is insufficient; and during a second execution of the natural language understanding application, receiving a second sequence of words and assigning a second sequence of tags to the second sequence of words by using the replacement statistical model. 2. The method of claim 1 , wherein developing the replacement statistical model comprises: developing the replacement model using both the human knowledge and the annotated words. | 0.750693 |
1. A data-processing system comprising: one or more processors; one or more computer-readable media; and a routine stored in the computer-readable media that when executed on the one or more processors, receives a set of feedback statistics, each feedback statistic represents a level of user satisfaction with a belief; calculates weighted statistics of the feedback statistics; calculates an entropy of the weighted statistics based on frequencies of occurrence of the weighted statistics; calculates a confidence value for the belief based on the entropy of the weighted statistics associated with the belief; calculates a rank value for the belief based on the confidence value and an average of the weighted statistics; and stores the rank value in the one or more computer-readable media. | 1. A data-processing system comprising: one or more processors; one or more computer-readable media; and a routine stored in the computer-readable media that when executed on the one or more processors, receives a set of feedback statistics, each feedback statistic represents a level of user satisfaction with a belief; calculates weighted statistics of the feedback statistics; calculates an entropy of the weighted statistics based on frequencies of occurrence of the weighted statistics; calculates a confidence value for the belief based on the entropy of the weighted statistics associated with the belief; calculates a rank value for the belief based on the confidence value and an average of the weighted statistics; and stores the rank value in the one or more computer-readable media. 6. The system of claim 1 , wherein determines the confidence value based on the entropy further comprises: calculates the confidence value as a function of the entropy when the entropy is less than an uncertainty threshold; and sets the confidence value to zero when the entropy is greater than the uncertainty threshold. | 0.506179 |
11. The system of claim 9 , wherein modifying the scoring data to favor newer documents comprises discounting an anchor score in the scoring data. | 11. The system of claim 9 , wherein modifying the scoring data to favor newer documents comprises discounting an anchor score in the scoring data. 12. The system of claim 11 , where the anchor score is for the first document and measures anchors to the first document. | 0.963609 |
29. The computer-readable memory device of claim 28 , where the coherence of the terms in the sequence is calculated relative to a collection of documents. | 29. The computer-readable memory device of claim 28 , where the coherence of the terms in the sequence is calculated relative to a collection of documents. 31. The computer-readable memory device of claim 29 , where the coherence of the terms in the sequence is calculated as: LR ( A , B ) = L ( f ( B ) , N ) L ( f ( AB ) , f ( A ) ) · L ( f ( ∼ AB ) , f ( ∼ A ) ) , where f(A) defines a number of occurrences of term A in the collection of documents, f(˜A) defines a number of occurrences of a term other than term A in the collection of documents, f(B) defines a number of occurrences of term B in the collection of documents, N defines a total number of events in the collection of documents, f(AB) defines a number of times term A is followed by term B in the collection of documents, and f(˜AB) is a number of times a term other than A is followed by term B in the collection of documents, where L ( k , n ) = ( k n ) k · ( 1 - k n ) ( n - k ) , where n and k are integers. | 0.697232 |
96. The system of claim 67 , wherein the voice input is processed by a plurality of language models comprising at least the first and second language models; wherein the at least one hardware processor generates the second of the at least two text search queries at least by using a recognition result produced by a selected language model of the plurality of language models; and wherein the selected language model is selected based, at least in part, on a score and/or confidence value associated with processing the voice input using the selected language model. | 96. The system of claim 67 , wherein the voice input is processed by a plurality of language models comprising at least the first and second language models; wherein the at least one hardware processor generates the second of the at least two text search queries at least by using a recognition result produced by a selected language model of the plurality of language models; and wherein the selected language model is selected based, at least in part, on a score and/or confidence value associated with processing the voice input using the selected language model. 99. The system of claim 96 , wherein the second language model is a general language model. | 0.883761 |
1. A method comprising: receiving, by a device that includes one or more processors, an input indicative of acoustic feature parameters associated with speech; identifying, using the input, a speech frame having an acoustic feature representation of the speech at a given time within a duration of the speech, wherein identifying the speech frame includes determining the acoustic feature parameters based on samples of the acoustic feature representation at harmonic frequencies associated with the speech frame; based on the speech frame being a voiced speech frame, modifying aperiodicity parameters of the speech frame to correspond to: a first value for first harmonic frequencies greater than a first threshold, a second value for second harmonic frequencies less than a second threshold, and one or more values between the first value and the second value for given harmonic frequencies less than the first threshold and greater than the second threshold; based on the modified aperiodicity parameters, determining a dispersion factor for phase parameters of the speech frame, wherein determining the dispersion factor includes modifying the phase parameters of the speech frame based on the determined dispersion factor; determining, for a harmonic frequency of the speech, based on the acoustic feature parameters, the modified phase parameters and the modified aperiodicity parameters, a modulated noise representation for modulating noise pertaining to one or more of an aspirate or a fricative in the speech, wherein the aspirate is associated with a characteristic of an exhalation of at least a threshold amount of breath, and wherein the fricative is associated with a characteristic of airflow between two or more vocal tract articulators; and providing, by the device, an audio signal indicative of a synthetic audio pronunciation of the speech based on the modulated noise representation. | 1. A method comprising: receiving, by a device that includes one or more processors, an input indicative of acoustic feature parameters associated with speech; identifying, using the input, a speech frame having an acoustic feature representation of the speech at a given time within a duration of the speech, wherein identifying the speech frame includes determining the acoustic feature parameters based on samples of the acoustic feature representation at harmonic frequencies associated with the speech frame; based on the speech frame being a voiced speech frame, modifying aperiodicity parameters of the speech frame to correspond to: a first value for first harmonic frequencies greater than a first threshold, a second value for second harmonic frequencies less than a second threshold, and one or more values between the first value and the second value for given harmonic frequencies less than the first threshold and greater than the second threshold; based on the modified aperiodicity parameters, determining a dispersion factor for phase parameters of the speech frame, wherein determining the dispersion factor includes modifying the phase parameters of the speech frame based on the determined dispersion factor; determining, for a harmonic frequency of the speech, based on the acoustic feature parameters, the modified phase parameters and the modified aperiodicity parameters, a modulated noise representation for modulating noise pertaining to one or more of an aspirate or a fricative in the speech, wherein the aspirate is associated with a characteristic of an exhalation of at least a threshold amount of breath, and wherein the fricative is associated with a characteristic of airflow between two or more vocal tract articulators; and providing, by the device, an audio signal indicative of a synthetic audio pronunciation of the speech based on the modulated noise representation. 6. The method of claim 1 , wherein the given time corresponds to one or more of a time-instant associated with a characteristic of a glottal cycle of the speech or a given time-instant associated with an unvoiced portion of the speech. | 0.662358 |
1. A server comprising: a processor to execute instructions; a set of registers for the storage of information to be communicated; an interface, the interface to connect with a component of each of a plurality of clients, wherein the plurality of clients includes a first client having a first computer platform and a second client having a second computer platform that is different than the first computer platform; a start up and control service, the start up and control service to be started by the interface, wherein the start up and control service is a single service for the management of applications for the plurality of clients, and wherein the startup and control service is to respond to requests from the plurality of clients for information regarding applications; and an application for execution to be managed by a connected component of one of the plurality of clients via the start up and control service through the interface, the start up and control service to initialize and monitor the application; wherein the set of registers is utilized to provide a registry, the registry including an entry for the application to be managed, the entry including one or more attributes for the management of the application, wherein the start up and control service is to obtain information from the set of registers to respond to requests for information from the plurality of clients. | 1. A server comprising: a processor to execute instructions; a set of registers for the storage of information to be communicated; an interface, the interface to connect with a component of each of a plurality of clients, wherein the plurality of clients includes a first client having a first computer platform and a second client having a second computer platform that is different than the first computer platform; a start up and control service, the start up and control service to be started by the interface, wherein the start up and control service is a single service for the management of applications for the plurality of clients, and wherein the startup and control service is to respond to requests from the plurality of clients for information regarding applications; and an application for execution to be managed by a connected component of one of the plurality of clients via the start up and control service through the interface, the start up and control service to initialize and monitor the application; wherein the set of registers is utilized to provide a registry, the registry including an entry for the application to be managed, the entry including one or more attributes for the management of the application, wherein the start up and control service is to obtain information from the set of registers to respond to requests for information from the plurality of clients. 18. The server of claim 1 , wherein the one or more attributes include attributes selected from the group consisting of: an attribute to identify a host that is running the application; an attribute to describe the current version of the application; and an attribute describing how a client may connect to the application. | 0.566216 |
23. A system for extracting structured knowledge from unstructured text for use in a knowledge representation system, the knowledge representation system comprising a knowledge base that represents knowledge using a structured, machine-readable format, the structured, machine-readable format comprising fact triples, the system comprising one or more computing devices configured to: identify sentences in the unstructured text; convert each of a subset of the sentences to one or more simplified assertion statements of the form: subject noun phrase, verb phrase, object noun phrase; convert each of a subset of the simplified assertion statements to a corresponding fact triple, each fact triple being constructed from three knowledge base objects, the three knowledge base objects comprising two entity objects and a relationship object expressing a relationship between the two entity objects; and group the fact triples into a plurality of quarantine groups such that each of the fact triples is included in more than one of the quarantine groups, each quarantine group being defined by a corresponding one of a plurality of fact characteristics, a first one of the fact characteristics being that all of the fact triples in the corresponding quarantine group include a same one of the entity objects, a second one of the fact characteristics being that all of the fact triples in the corresponding quarantine group include a same one of the relationship objects; determine a reliability for each quarantine group with reference to the knowledge base; determine that more than one of the quarantine groups in which a first fact triple is included has at least a specified reliability; and classify the first fact triple as a reliable fact triple in response to determining that more than one of the quarantine groups in which the first fact triple is included has at least the specified reliability. | 23. A system for extracting structured knowledge from unstructured text for use in a knowledge representation system, the knowledge representation system comprising a knowledge base that represents knowledge using a structured, machine-readable format, the structured, machine-readable format comprising fact triples, the system comprising one or more computing devices configured to: identify sentences in the unstructured text; convert each of a subset of the sentences to one or more simplified assertion statements of the form: subject noun phrase, verb phrase, object noun phrase; convert each of a subset of the simplified assertion statements to a corresponding fact triple, each fact triple being constructed from three knowledge base objects, the three knowledge base objects comprising two entity objects and a relationship object expressing a relationship between the two entity objects; and group the fact triples into a plurality of quarantine groups such that each of the fact triples is included in more than one of the quarantine groups, each quarantine group being defined by a corresponding one of a plurality of fact characteristics, a first one of the fact characteristics being that all of the fact triples in the corresponding quarantine group include a same one of the entity objects, a second one of the fact characteristics being that all of the fact triples in the corresponding quarantine group include a same one of the relationship objects; determine a reliability for each quarantine group with reference to the knowledge base; determine that more than one of the quarantine groups in which a first fact triple is included has at least a specified reliability; and classify the first fact triple as a reliable fact triple in response to determining that more than one of the quarantine groups in which the first fact triple is included has at least the specified reliability. 35. The system of claim 23 wherein the one or more computing devices are configured to: group the fact triples into a plurality of quarantine groups by adding each fact triple to a plurality of fact buckets, each fact bucket being defined by the corresponding fact characteristic shared by all fact triples added to that fact bucket; and wherein the one or more computing devices are configured to determine the reliability for each quarantine group by: for each fact bucket, determining whether each of a first number of fact triples added to that fact bucket is true or false with respect to the knowledge base; and designating each fact bucket as one of reliable, unreliable, or unknown based on one or both of a first number of fact triples added to that fact bucket determined to be false, or a second number of fact triples added to that fact bucket determined to be true. | 0.528029 |
8. The system of claim 7 , wherein the information includes one or more code snippets of a plurality of code snippets that are contextually related to the designated code; wherein the plurality of code snippets is associated with the plurality of users in the social networking environment in the social data graph; and wherein the recommendation logic is configured to recommend at least one code snippet of the one or more code snippets for inclusion in the designated code based on the at least one code snippet of the one or more code snippets being associated with the at least one user of the plurality of users who is included in the social network of the developer in the social data graph. | 8. The system of claim 7 , wherein the information includes one or more code snippets of a plurality of code snippets that are contextually related to the designated code; wherein the plurality of code snippets is associated with the plurality of users in the social networking environment in the social data graph; and wherein the recommendation logic is configured to recommend at least one code snippet of the one or more code snippets for inclusion in the designated code based on the at least one code snippet of the one or more code snippets being associated with the at least one user of the plurality of users who is included in the social network of the developer in the social data graph. 14. The system of claim 8 , wherein the recommendation logic is configured to recommend a specified code snippet for inclusion in the designated code further based on user feedback from one or more users who retrieve the specified code snippet. | 0.834732 |
17. A method performed at least in part on at least one processor, said method comprising: receiving an image to classify, said image being located within a first web page; identifying a plurality of web pages comprising said image by transmitting a search request to a search system and returning said plurality of web pages, the first web page included in said plurality of web pages; identifying a training set of examples from at least one of said web pages, said training set of examples comprising a subset of said text within said plurality of web pages, the subset of said text being classified based on a determination of a measure of proximity to said image, said training set comprising at least one positive example and at least one negative example; training a classifier using said training set, said classifier being a binary classifier; and classifying said text within said plurality of web pages using said classifier to identify a group of text associated with said image. | 17. A method performed at least in part on at least one processor, said method comprising: receiving an image to classify, said image being located within a first web page; identifying a plurality of web pages comprising said image by transmitting a search request to a search system and returning said plurality of web pages, the first web page included in said plurality of web pages; identifying a training set of examples from at least one of said web pages, said training set of examples comprising a subset of said text within said plurality of web pages, the subset of said text being classified based on a determination of a measure of proximity to said image, said training set comprising at least one positive example and at least one negative example; training a classifier using said training set, said classifier being a binary classifier; and classifying said text within said plurality of web pages using said classifier to identify a group of text associated with said image. 20. The method of claim 17 , wherein said web page comprises a HyperText Markup Language (HTML) document. | 0.641085 |
8. A non-transitory computer readable media encoded with a computer program comprising instructions that when executed operate to cause a computer to perform operations comprising: identifying a plurality of content items that are each available to navigated to as a result selecting search results that correspond to respective ones of the textual content items; segmenting textual content from in each of a plurality of distinct ones of the content items into word strings, each word string including contiguous words in the textual content of a corresponding content item; classifying word strings having a frequency of occurrence that is less than a threshold frequency of occurrence as uncommon word strings, the frequency of occurrence for each word string being a number of occurrences of the word string in the plurality of distinct content items relative to a total number of word strings in the plurality of distinct content items; identifying matching uncommon word strings segmented from between pairs of distinct content items that each can be navigated to via search results returned to users in response to submission of search queries; and determining a rank score for each distinct content item based on the matching uncommon word strings, the rank score being a score indicative of the importance of each distinct content item relative to other distinct content items. | 8. A non-transitory computer readable media encoded with a computer program comprising instructions that when executed operate to cause a computer to perform operations comprising: identifying a plurality of content items that are each available to navigated to as a result selecting search results that correspond to respective ones of the textual content items; segmenting textual content from in each of a plurality of distinct ones of the content items into word strings, each word string including contiguous words in the textual content of a corresponding content item; classifying word strings having a frequency of occurrence that is less than a threshold frequency of occurrence as uncommon word strings, the frequency of occurrence for each word string being a number of occurrences of the word string in the plurality of distinct content items relative to a total number of word strings in the plurality of distinct content items; identifying matching uncommon word strings segmented from between pairs of distinct content items that each can be navigated to via search results returned to users in response to submission of search queries; and determining a rank score for each distinct content item based on the matching uncommon word strings, the rank score being a score indicative of the importance of each distinct content item relative to other distinct content items. 12. The computer readable media of claim 8 , wherein the operations further comprise determining pair weight for each pair of distinct content items that include matching uncommon word strings based on a relative importance of the distinct content items that comprise the pair. | 0.591899 |
9. The method of claim 8 , further comprising associating the first data object with the first alias. | 9. The method of claim 8 , further comprising associating the first data object with the first alias. 10. The method of claim 9 , wherein the associating comprises registration of the first alias with a name management server. | 0.946718 |
4. A method as claimed in claim 3 wherein the candidate indexes are ordered by request value of importance, the method further comprising, with identification of each candidate index, searching previously identified indexes for an existing index that is similar to the candidate index and choosing from the steps of a) creating a new index, b) reusing the best existing index and c) modifying the best existing index to satisfy the context of the candidate index, choice of steps a, b and c being dependent on match between the candidate index and the best existing index and the value of importance of the requests for which the indexes are identified. | 4. A method as claimed in claim 3 wherein the candidate indexes are ordered by request value of importance, the method further comprising, with identification of each candidate index, searching previously identified indexes for an existing index that is similar to the candidate index and choosing from the steps of a) creating a new index, b) reusing the best existing index and c) modifying the best existing index to satisfy the context of the candidate index, choice of steps a, b and c being dependent on match between the candidate index and the best existing index and the value of importance of the requests for which the indexes are identified. 12. A method as claimed in claim 4 wherein the best existing index is reused if the best index is sorted, the candidate index is either hash or sorted, the best existing index has more columns and the columns of the candidate index match, in identity and in position, leading segments of the best existing index. | 0.924449 |
11. A computer software product, including a non-transitory computer-readable storage medium in which computer program instructions are stored, which instructions, when executed by a computer, cause the computer to perform the steps of: receiving a data stream of a multi-participant meeting having participants, wherein multiple topics are discussed; analyzing the data stream to detect one of the topics and to define topical sub-activities of the one topic, the topical sub-activities being performed by at least a portion of the participants; identifying respective contributions to the one topic by two of the participants in the topical sub-activities associated therewith; making an evaluation of the respective contributions to selected ones of the topical sub-activities; calculating a connection weight between the two participants based on the evaluation of the respective contributions; and reporting the connection weight, wherein said receiving, analyzing, identifying, making and calculating steps are implemented in either computer hardware, or computer software embodied in a non-transitory, tangible, computer-readable storage medium. | 11. A computer software product, including a non-transitory computer-readable storage medium in which computer program instructions are stored, which instructions, when executed by a computer, cause the computer to perform the steps of: receiving a data stream of a multi-participant meeting having participants, wherein multiple topics are discussed; analyzing the data stream to detect one of the topics and to define topical sub-activities of the one topic, the topical sub-activities being performed by at least a portion of the participants; identifying respective contributions to the one topic by two of the participants in the topical sub-activities associated therewith; making an evaluation of the respective contributions to selected ones of the topical sub-activities; calculating a connection weight between the two participants based on the evaluation of the respective contributions; and reporting the connection weight, wherein said receiving, analyzing, identifying, making and calculating steps are implemented in either computer hardware, or computer software embodied in a non-transitory, tangible, computer-readable storage medium. 15. The computer software product according to claim 11 , wherein analyzing the data stream to define topical sub-activities comprises: identifying the participants; demarcating the topical sub-activities by speech pauses of the identified participants; and subdividing the demarcated topical sub-activities by detecting replacement times wherein one speaker is replaced by another speaker among the identified participants. | 0.534535 |
6. A method of operating a network-based agent to seek out users of a network with common interests, where said users are connected via user terminals and data communication connections to a server system which provides access to an electronic data transmission media, comprising the steps of: dynamically creating bulletin boards for said users, comprising: scanning bulletin board postings to existing bulletin boards, identifying a group of users who have common interests, matching users with other like inclined users in said identified group to create a proposed new bulletin board. | 6. A method of operating a network-based agent to seek out users of a network with common interests, where said users are connected via user terminals and data communication connections to a server system which provides access to an electronic data transmission media, comprising the steps of: dynamically creating bulletin boards for said users, comprising: scanning bulletin board postings to existing bulletin boards, identifying a group of users who have common interests, matching users with other like inclined users in said identified group to create a proposed new bulletin board. 12. The method of operating a network-based agent of claim 6, wherein said step of automatically creating further comprises: continuing to enroll additional users in said proposed new bulletin board. | 0.807119 |
5. The method of claim 1 wherein: the web page conforms to a mark-up language; and the predetermined syntax requires an opening tag at a beginning of, and a closing tag at an end of, the source-descriptive version. | 5. The method of claim 1 wherein: the web page conforms to a mark-up language; and the predetermined syntax requires an opening tag at a beginning of, and a closing tag at an end of, the source-descriptive version. 6. The method of claim 5 wherein: the opening tag is <SPAN>; and the closing tag is </SPAN>. | 0.939064 |
5. A question and answer data editing device for editing the content of a dialogue to generate question and answer data, comprising: a processor configured to function as at least one of a matching unit, an expression pattern extraction unit, a question and answer variation extraction unit and an associated question and answer variation extraction unit, wherein the matching unit detects a first question part or a first answer part from a history data of said dialogue content that is similar to a first question and answer data included in existing question and answer data; the expression pattern extraction unit extracts an expression pattern including a context or a condition in which said dialogue was made from the proximity of said first question part or said first answer part, and registers said extracted expression pattern as index information of said first question and answer data; the question and answer variation extraction unit extracts a second question part or a second answer part not similar to said first question and answer data from the proximity of said first question part or said first answer part and registers said extracted second question part or second answer part as a variation of said first question and answer data; the associated question and answer extraction unit extracts: a) a third answer part from the history data, when a third question part in the history data is similar to the first question and answer data and the third answer part in the history data is not similar to the first question and answer data, and; b) the third question part from the history data, when the third answer part in the history data is similar to the first question and answer data and the third question part is not similar to the first question and answer data, and; registers said third question part or said third answer part as associated question and answer data of said first question and answer data. | 5. A question and answer data editing device for editing the content of a dialogue to generate question and answer data, comprising: a processor configured to function as at least one of a matching unit, an expression pattern extraction unit, a question and answer variation extraction unit and an associated question and answer variation extraction unit, wherein the matching unit detects a first question part or a first answer part from a history data of said dialogue content that is similar to a first question and answer data included in existing question and answer data; the expression pattern extraction unit extracts an expression pattern including a context or a condition in which said dialogue was made from the proximity of said first question part or said first answer part, and registers said extracted expression pattern as index information of said first question and answer data; the question and answer variation extraction unit extracts a second question part or a second answer part not similar to said first question and answer data from the proximity of said first question part or said first answer part and registers said extracted second question part or second answer part as a variation of said first question and answer data; the associated question and answer extraction unit extracts: a) a third answer part from the history data, when a third question part in the history data is similar to the first question and answer data and the third answer part in the history data is not similar to the first question and answer data, and; b) the third question part from the history data, when the third answer part in the history data is similar to the first question and answer data and the third question part is not similar to the first question and answer data, and; registers said third question part or said third answer part as associated question and answer data of said first question and answer data. 18. A dialogue supporting device for supporting a dialogue by using said generated question and answer data or said index information which is generated by the question and answer data editing device according to claim 5 . | 0.5 |
1. A system for executing a database query comprising: a processor configured to: generate one or more predicates based on implicit filtering present within the database query, wherein each of the one or more predicates specifies a condition with respect to a respective predicate value; select an access path for the database query based on the one or more predicates and integrating the one or more predicates within the selected access plan; execute a set number of operations of the database query in accordance with the selected access plan; repeatedly update the respective predicate value of at least one predicate of the one or more predicates based on data accessed, wherein the updating increases filtering of data, and execute the database query until the filtering of the data cannot be improved by an updated predicate value. | 1. A system for executing a database query comprising: a processor configured to: generate one or more predicates based on implicit filtering present within the database query, wherein each of the one or more predicates specifies a condition with respect to a respective predicate value; select an access path for the database query based on the one or more predicates and integrating the one or more predicates within the selected access plan; execute a set number of operations of the database query in accordance with the selected access plan; repeatedly update the respective predicate value of at least one predicate of the one or more predicates based on data accessed, wherein the updating increases filtering of data, and execute the database query until the filtering of the data cannot be improved by an updated predicate value. 2. The system of claim 1 , wherein the filtering present within the database query includes one or more from the group consisting of FETCH FIRST or FETCH FIRST with one or more of ORDER BY, MIN, MAX, DISTINCT, GROUP BY, and UNION. | 0.593696 |
29. A computer program storage product storing instructions that when executed on a computer system, perform a method for performing synthesis of relationships between a plurality of concept definitions using a domain of information, wherein the domain of information comprises a plurality of facets each having a plurality of facet attributes, wherein each of the plurality of concept definitions comprises at least one the plurality of facet attributes, and wherein the method comprises: determining whether any explicit relationships exist between the plurality of concept definitions, wherein an explicit relationship is determined to exist between any two of the plurality concept definitions if the two of the plurality of concept definitions share at least one common facet attribute or each has a facet attribute of the same lineage in at least one facet attribute hierarchy of the plurality of facet attributes; determining whether any implicit relationships exist between the plurality of concept definitions, wherein an implicit relationship between two of the plurality of concept definitions is determined to exist based on a statistical identification of a relationship between a facet attribute in a first of the two of the plurality of concept definitions and a facet attribute in a second of the two of the plurality of concept definitions; and when it is determined that at least one explicit relationship and/or at least one implicit relationship exists between two of the plurality of concept definitions, synthesizing a relationship between the two of the plurality of concept definitions. | 29. A computer program storage product storing instructions that when executed on a computer system, perform a method for performing synthesis of relationships between a plurality of concept definitions using a domain of information, wherein the domain of information comprises a plurality of facets each having a plurality of facet attributes, wherein each of the plurality of concept definitions comprises at least one the plurality of facet attributes, and wherein the method comprises: determining whether any explicit relationships exist between the plurality of concept definitions, wherein an explicit relationship is determined to exist between any two of the plurality concept definitions if the two of the plurality of concept definitions share at least one common facet attribute or each has a facet attribute of the same lineage in at least one facet attribute hierarchy of the plurality of facet attributes; determining whether any implicit relationships exist between the plurality of concept definitions, wherein an implicit relationship between two of the plurality of concept definitions is determined to exist based on a statistical identification of a relationship between a facet attribute in a first of the two of the plurality of concept definitions and a facet attribute in a second of the two of the plurality of concept definitions; and when it is determined that at least one explicit relationship and/or at least one implicit relationship exists between two of the plurality of concept definitions, synthesizing a relationship between the two of the plurality of concept definitions. 33. The computer program storage product of claim 29 , wherein an explicit or implicit relationship is determined to exist between any two of the plurality of concept definitions if: all of the facet attributes of a one of the two concept definitions are related to all or a subset of the facet attributes of the other of the two concept definitions. | 0.575786 |
4. The method according to claim 3 , wherein the first results output includes a column for each leaf in the first set and a separate row for each unique tuple of values for leaves associated with the first leaf type in the first set. | 4. The method according to claim 3 , wherein the first results output includes a column for each leaf in the first set and a separate row for each unique tuple of values for leaves associated with the first leaf type in the first set. 5. The method according to claim 4 , wherein, for each column in the first results output corresponding to a leaf associated with the second leaf type, an aggregate value is provided in each row of the column based on data in the row. | 0.919549 |
11. The method according to claim 10 , wherein the restoring includes: judging whether or not the focus position is within a displaying area defined by the scrolling position; and adjusting the focus position so that the focus position is within the displaying area if it is judged that the focus position is not within the displaying area. | 11. The method according to claim 10 , wherein the restoring includes: judging whether or not the focus position is within a displaying area defined by the scrolling position; and adjusting the focus position so that the focus position is within the displaying area if it is judged that the focus position is not within the displaying area. 12. The method according to claim 11 , wherein the adjusting the focus position is performed so that a scrolling amount from the top of a page is minimized and a focus target is displayed appropriately. | 0.86689 |
4. The invention according to claim 2 wherein: portions of said puzzle piece means are of a particular color to describe a particular color along a vertical direction so as to indicate proper assembly of said puzzle pieces in said vertical direction. | 4. The invention according to claim 2 wherein: portions of said puzzle piece means are of a particular color to describe a particular color along a vertical direction so as to indicate proper assembly of said puzzle pieces in said vertical direction. 5. The invention according to claim 4 wherein: said color means along said vertical direction is distributed along each vertical column containing initial information and said vertical column containing resultant information to describe a color in said resultant information column which corresponds to the color combination of the colors contained in said initial information columns. | 0.862015 |
1. A method of creating a secure intellectual property (IP) representation of a circuit design for use with a software-based simulator, said method comprising: translating a hardware description language representation of the circuit design into an intermediate form specified using a general-purpose, high level programming language; obfuscating the general-purpose, high level programming language of the intermediate form using at least one obfuscation technique operable on the general-purpose high level programming language; compiling the intermediate form of the circuit design to produce encrypted object code; and linking the encrypted object code with a simulation kernel library creating the secure IP representation of the circuit design comprising an encrypted simulation model of the circuit design and a simulation kernel configured to execute the encrypted simulation model in coordination with the simulator, wherein the simulator is a hardware description language simulator. | 1. A method of creating a secure intellectual property (IP) representation of a circuit design for use with a software-based simulator, said method comprising: translating a hardware description language representation of the circuit design into an intermediate form specified using a general-purpose, high level programming language; obfuscating the general-purpose, high level programming language of the intermediate form using at least one obfuscation technique operable on the general-purpose high level programming language; compiling the intermediate form of the circuit design to produce encrypted object code; and linking the encrypted object code with a simulation kernel library creating the secure IP representation of the circuit design comprising an encrypted simulation model of the circuit design and a simulation kernel configured to execute the encrypted simulation model in coordination with the simulator, wherein the simulator is a hardware description language simulator. 8. The method of claim 1 , wherein the intermediate form of the circuit design comprises an encrypted form of the general-purpose, high level programming language. | 0.61605 |
1. A method of ranking candidate answers to a natural language question, the method comprising the steps of: a computer receiving the natural language question from a mobile device via a computer network by which the computer and the mobile device are coupled, the natural language question being asked by a user of the mobile device; the computer generating candidate answers to the received natural language question; subsequent to the step of generating the candidate answers, the computer identifying first contextual information about the user, the first contextual information including (1) interests of the user based on historical usage of the mobile device by the user to run applications on the mobile device, visit websites via a browser provided by the mobile device, display textual articles on the mobile devices, enter terms into a search engine via the mobile device, and capture multimedia content, (2) a geographic location of the user provided by a global positioning system (GPS) coupled to the mobile device, (3) data specifying characteristics of an environment in proximity to the user, the characteristics of the environment being detected and measured by a first sensor coupled to the mobile device, and (4) data specifying a bodily function of the user, the bodily function being detected and measured by a second sensor coupled to the mobile device; based on the first contextual information, the computer determining a prioritization of definitions of terms; based on the first contextual information and the prioritization of the definitions of the terms, the computer generating a lexicon of the terms; using a forecaster that employs mobile-based time series manipulation and pattern recognition and based on (1) the historical usage of the mobile device by the user to run the applications on the mobile device, visit the websites via the browser provided by the mobile device, display the textual articles on the mobile devices, enter the terms into the search engine via the mobile device, and capture the multimedia content, (2) the geographic location of the user provided by the GPS coupled to the mobile device, (3) the data specifying characteristics of the environment in proximity to the user, and (4) the data specifying the bodily function of the user, the computer forecasting second contextual information that indicates future behavior of the user; based on the forecasted second contextual information, the computer performing word sense disambiguation of the terms in the lexicon and adjusting the prioritization of the definitions of the terms in the lexicon; based on the word sense disambiguation of the terms in the lexicon and the adjusted prioritization of the definitions of the terms, the computer modifying the candidate answers; and based in part on the adjusted prioritization of the definitions of the terms in the lexicon, the modified candidate answers, and the forecasted second contextual information, the computer ranking the modified candidate answers to the natural language question, the highest ranked modified candidate answer being more likely to be a correct answer to the natural language question than the other candidate answers. | 1. A method of ranking candidate answers to a natural language question, the method comprising the steps of: a computer receiving the natural language question from a mobile device via a computer network by which the computer and the mobile device are coupled, the natural language question being asked by a user of the mobile device; the computer generating candidate answers to the received natural language question; subsequent to the step of generating the candidate answers, the computer identifying first contextual information about the user, the first contextual information including (1) interests of the user based on historical usage of the mobile device by the user to run applications on the mobile device, visit websites via a browser provided by the mobile device, display textual articles on the mobile devices, enter terms into a search engine via the mobile device, and capture multimedia content, (2) a geographic location of the user provided by a global positioning system (GPS) coupled to the mobile device, (3) data specifying characteristics of an environment in proximity to the user, the characteristics of the environment being detected and measured by a first sensor coupled to the mobile device, and (4) data specifying a bodily function of the user, the bodily function being detected and measured by a second sensor coupled to the mobile device; based on the first contextual information, the computer determining a prioritization of definitions of terms; based on the first contextual information and the prioritization of the definitions of the terms, the computer generating a lexicon of the terms; using a forecaster that employs mobile-based time series manipulation and pattern recognition and based on (1) the historical usage of the mobile device by the user to run the applications on the mobile device, visit the websites via the browser provided by the mobile device, display the textual articles on the mobile devices, enter the terms into the search engine via the mobile device, and capture the multimedia content, (2) the geographic location of the user provided by the GPS coupled to the mobile device, (3) the data specifying characteristics of the environment in proximity to the user, and (4) the data specifying the bodily function of the user, the computer forecasting second contextual information that indicates future behavior of the user; based on the forecasted second contextual information, the computer performing word sense disambiguation of the terms in the lexicon and adjusting the prioritization of the definitions of the terms in the lexicon; based on the word sense disambiguation of the terms in the lexicon and the adjusted prioritization of the definitions of the terms, the computer modifying the candidate answers; and based in part on the adjusted prioritization of the definitions of the terms in the lexicon, the modified candidate answers, and the forecasted second contextual information, the computer ranking the modified candidate answers to the natural language question, the highest ranked modified candidate answer being more likely to be a correct answer to the natural language question than the other candidate answers. 2. The method of claim 1 , further comprising the computer adjusting weights of a machine learning ensemble based on the forecasted second contextual information, wherein the machine learning ensemble includes (1) logistic regression phases and routes, (2) dimensions of evidence refinement, (3) probability density functions, classification techniques, and regression techniques, (4) natural language processing systems, and (5) a data retrieval system. | 0.813218 |
4. A speech recognition system comprising: an input identification means for identifying each of a plurality of users of received signals of utterance; recognition result storage for storing top N recognition vocabularies having high recognition scores starting from the best solution as N best solutions, N being an integer equal to one or more, the recognition scores being calculated by comparing data corresponding to the utterance with a plurality of recognition vocabularies, a recognition word having the highest recognition score being the best solution; a recognition result extraction means for extracting N best solutions extracted as following N best solutions from the recognition result storage, the following N best solutions following chronologically the utterance corresponding to a preceding N best solutions, the following N best solutions having been made by one of the users different from the user of the utterance corresponding to the preceding N best solutions; a degree of association calculation means for calculating a degree of association representing a likelihood that the following N best solutions are N best solutions obtained by a response utterance in response to the utterance corresponding to the preceding N best solutions; a response utterance determination means for determining that the following N best solutions are N best solutions obtained by a response utterance in response to the utterance corresponding to the preceding N best solutions in the case of the degree of association being equal to or more than a threshold value; a repeat utterance determination means for determining whether the following N best solutions are N best solutions obtained by a repeat utterance in response to the utterance corresponding to the preceding N best solution, in the case that the following N best solutions are N best solutions obtained by a response utterance in response to the utterance corresponding to the preceding N best solutions; and an agreement determination means for: determining whether a preceding best solution and a following best solution agree with each other in the case of the following N best solutions being best solutions obtained by a repeat utterance in response to the utterance corresponding to the preceding N best solutions, the preceding best solution being a best solution of the preceding N best solutions, the following best solution being a best solution of the following N best solutions is the following best solution; and determining that some or all of the preceding N best solutions can be replaced with some or all of the following N best solutions in the case that the preceding best solution and the following best solution do not agree with each other. | 4. A speech recognition system comprising: an input identification means for identifying each of a plurality of users of received signals of utterance; recognition result storage for storing top N recognition vocabularies having high recognition scores starting from the best solution as N best solutions, N being an integer equal to one or more, the recognition scores being calculated by comparing data corresponding to the utterance with a plurality of recognition vocabularies, a recognition word having the highest recognition score being the best solution; a recognition result extraction means for extracting N best solutions extracted as following N best solutions from the recognition result storage, the following N best solutions following chronologically the utterance corresponding to a preceding N best solutions, the following N best solutions having been made by one of the users different from the user of the utterance corresponding to the preceding N best solutions; a degree of association calculation means for calculating a degree of association representing a likelihood that the following N best solutions are N best solutions obtained by a response utterance in response to the utterance corresponding to the preceding N best solutions; a response utterance determination means for determining that the following N best solutions are N best solutions obtained by a response utterance in response to the utterance corresponding to the preceding N best solutions in the case of the degree of association being equal to or more than a threshold value; a repeat utterance determination means for determining whether the following N best solutions are N best solutions obtained by a repeat utterance in response to the utterance corresponding to the preceding N best solution, in the case that the following N best solutions are N best solutions obtained by a response utterance in response to the utterance corresponding to the preceding N best solutions; and an agreement determination means for: determining whether a preceding best solution and a following best solution agree with each other in the case of the following N best solutions being best solutions obtained by a repeat utterance in response to the utterance corresponding to the preceding N best solutions, the preceding best solution being a best solution of the preceding N best solutions, the following best solution being a best solution of the following N best solutions is the following best solution; and determining that some or all of the preceding N best solutions can be replaced with some or all of the following N best solutions in the case that the preceding best solution and the following best solution do not agree with each other. 8. The speech recognition system according to claim 4 , the system further comprising: a co-occurrence information storage that stores co-occurrence information representing co-occurrence relationships between recognition vocabularies and/or a semantic attribute storage that stores semantic attributes representing the meanings of recognition vocabularies, and a comparison process changing means for changing a method for comparing an utterance with a plurality of recognition vocabularies on the basis of the co-occurrence information and/or the semantic attributes in the case of the preceding best solution and the following best solution being coincident with each other. | 0.6024 |
14. A computer system method by which a user inputs information into a record, the computer system including a processor, an input device, a memory, a database and a program including instructions, wherein the program resides in the memory and the processor configured to execute the program including the instructions comprising: receiving a first utterance from an input device, wherein the first utterance includes at least one word; retrieving a program code that includes one or more markers, each of which is associated with a certain utterance and correspond to a computer-implemented task; assessing the program code for the presence of one or more markers for which the certain utterance with which it is associated is the first utterance; if the first utterance is associated with one or more markers, perform computer-implemented task corresponding to the one or more markers associated with the first utterance; if the first utterance is not associated with one or more markers, show notification configured to convey same to user; receiving a second utterance by the input device; entering the second utterance into the memory; accessing by the processor a set of template hierarchies from the database, wherein the set of template hierarchies includes at least one template; performing a matching algorithm instructing the processor to compare words of the second utterance to terms of the template hierarchy to determine a match between the words and terms, the matching algorithm further instructing the processor to perform the steps of: calculating a score based on the match between the words of the second utterance to terms of the template hierarchy; populating the at least one template with data elements of the database that correspond to the terms of the template hierarchy to obtain a populated template; computing a total score based on the match between all words of the second utterance to the populated template; selecting the at least one template with a high total score; establishing there is no template with the high total score recording by the processor the second utterance as a sequence of words; and communicating the sequence of words to the user. | 14. A computer system method by which a user inputs information into a record, the computer system including a processor, an input device, a memory, a database and a program including instructions, wherein the program resides in the memory and the processor configured to execute the program including the instructions comprising: receiving a first utterance from an input device, wherein the first utterance includes at least one word; retrieving a program code that includes one or more markers, each of which is associated with a certain utterance and correspond to a computer-implemented task; assessing the program code for the presence of one or more markers for which the certain utterance with which it is associated is the first utterance; if the first utterance is associated with one or more markers, perform computer-implemented task corresponding to the one or more markers associated with the first utterance; if the first utterance is not associated with one or more markers, show notification configured to convey same to user; receiving a second utterance by the input device; entering the second utterance into the memory; accessing by the processor a set of template hierarchies from the database, wherein the set of template hierarchies includes at least one template; performing a matching algorithm instructing the processor to compare words of the second utterance to terms of the template hierarchy to determine a match between the words and terms, the matching algorithm further instructing the processor to perform the steps of: calculating a score based on the match between the words of the second utterance to terms of the template hierarchy; populating the at least one template with data elements of the database that correspond to the terms of the template hierarchy to obtain a populated template; computing a total score based on the match between all words of the second utterance to the populated template; selecting the at least one template with a high total score; establishing there is no template with the high total score recording by the processor the second utterance as a sequence of words; and communicating the sequence of words to the user. 18. The computer system method of claim 14 , wherein the database is located on a memory of a remote computer. | 0.714923 |
1. A method for containing analog verification intellectual property (IP) for circuit simulation, the method comprising: using a computer, reading an analog verification file containing at least one analog verification unit (vunit) that contains properties describing analog circuit design verification requirements including checks for specified simulation results, wherein the analog verification file is separate from input design IP; binding the vunit to one of a top-level circuit, a subcircuit master, and a subcircuit instance during circuit hierarchy expansion, to set vunit scope; performing a circuit simulation with a computer-operated circuit simulation tool combining the input design IP and the analog circuit design verification requirements; and tangibly outputting circuit simulation and verification results. | 1. A method for containing analog verification intellectual property (IP) for circuit simulation, the method comprising: using a computer, reading an analog verification file containing at least one analog verification unit (vunit) that contains properties describing analog circuit design verification requirements including checks for specified simulation results, wherein the analog verification file is separate from input design IP; binding the vunit to one of a top-level circuit, a subcircuit master, and a subcircuit instance during circuit hierarchy expansion, to set vunit scope; performing a circuit simulation with a computer-operated circuit simulation tool combining the input design IP and the analog circuit design verification requirements; and tangibly outputting circuit simulation and verification results. 7. The method of claim 1 wherein the circuit simulation tool, via the vunit, coordinates the input design IP and the analog circuit design verification requirements with no manual netlist editing. | 0.751263 |
1. A method of providing text responsive to detection of audio playing, including: receiving a signal corresponding to an audio track; determining from the signal an identity of the audio track and a current audio position within the audio track; displaying on a display a portion of a text that is linked and synchronized to the audio track identified and automatically scrolling the portion of the text displayed in pace with the audio track playing; and displaying a current text indicator that emphasizes current text, wherein the current text indicator is visually synchronized on the display to current audio playing from the audio track; wherein the display is a touchscreen, further including, responsive to user interaction with the display that scrolls the current text indicator beyond an area visible on the display, displaying an updated portion of the text as the audio track continues to play and at least temporarily suspending the automatic scrolling of the portion of text displayed until the audio track catches up with the portion of the text displayed. | 1. A method of providing text responsive to detection of audio playing, including: receiving a signal corresponding to an audio track; determining from the signal an identity of the audio track and a current audio position within the audio track; displaying on a display a portion of a text that is linked and synchronized to the audio track identified and automatically scrolling the portion of the text displayed in pace with the audio track playing; and displaying a current text indicator that emphasizes current text, wherein the current text indicator is visually synchronized on the display to current audio playing from the audio track; wherein the display is a touchscreen, further including, responsive to user interaction with the display that scrolls the current text indicator beyond an area visible on the display, displaying an updated portion of the text as the audio track continues to play and at least temporarily suspending the automatic scrolling of the portion of text displayed until the audio track catches up with the portion of the text displayed. 3. The method of claim 1 , wherein the text is lyrics and the audio track is musical. | 0.796485 |
8. The voice data analyzing device according to claim 1 , further comprising a speaker recognition unit which recognizes which speaker is the speaker of each utterance included in a specified piece of voice data by calculating consistency with the speaker models and consistency of the co-occurrence relationship in the whole voice data in regard to each utterance included in the specified voice data by use of the speaker models derived by the speaker model deriving unit and the speaker co-occurrence model derived by the speaker co-occurrence model deriving unit. | 8. The voice data analyzing device according to claim 1 , further comprising a speaker recognition unit which recognizes which speaker is the speaker of each utterance included in a specified piece of voice data by calculating consistency with the speaker models and consistency of the co-occurrence relationship in the whole voice data in regard to each utterance included in the specified voice data by use of the speaker models derived by the speaker model deriving unit and the speaker co-occurrence model derived by the speaker co-occurrence model deriving unit. 9. The voice data analyzing device according to claim 8 , wherein the speaker recognition unit calculates the probability that an utterance corresponds to each speaker and selects a speaker maximizing the probability as the result of the speaker recognition in regard to each utterance. | 0.697452 |
14. A non-transitory computer readable medium having stored thereon a sequence of instructions which, when executed by a processor causes the processor to execute a process comprising: identifying an internal social network for an enterprise; collecting a set of messages from the internal social network; performing semantic filtering to the set of messages, the semantic filtering reducing an excess noise associated with data that is not relevant to the enterprise; performing semantic analysis upon the set of messages collected from the internal social network to determine a contextual significance of one or more terms in the set of messages; identifying themes in the set of messages, the themes pertaining to at least one of customer preferences, demographic information, industry trends, customer view points: as a result of the performed semantic analysis, clustering together messages that are similar to each other; categorizing the set of messages into a plurality of categories based at least in part on the contextual significance of the one or more terms in the set of messages; associating each category of the plurality of categories with one or more tags; associating respective tags to the set of messages that are categorized as a result of the semantic analysis to generate a set of tagged messages; determining associations between the one or more tags and one or more enterprise applications corresponding to the enterprise such that the set of tagged messages are sent to respective ones of the one or more enterprise applications based at least in part on the determined associations: and storing the tagged messages in an actionable social message store, the actionable message store storing in a table format for each message, one or more of a source of the message, a topic parameter associated with the message, data associated with the message, and the one or more tags associated with the message, wherein the semantic analysis performed comprises latent semantic analysis (LSAT), the LSA referring to a form of statistical language modeling that distinguishes two identical words based on a semantic significance of the word. | 14. A non-transitory computer readable medium having stored thereon a sequence of instructions which, when executed by a processor causes the processor to execute a process comprising: identifying an internal social network for an enterprise; collecting a set of messages from the internal social network; performing semantic filtering to the set of messages, the semantic filtering reducing an excess noise associated with data that is not relevant to the enterprise; performing semantic analysis upon the set of messages collected from the internal social network to determine a contextual significance of one or more terms in the set of messages; identifying themes in the set of messages, the themes pertaining to at least one of customer preferences, demographic information, industry trends, customer view points: as a result of the performed semantic analysis, clustering together messages that are similar to each other; categorizing the set of messages into a plurality of categories based at least in part on the contextual significance of the one or more terms in the set of messages; associating each category of the plurality of categories with one or more tags; associating respective tags to the set of messages that are categorized as a result of the semantic analysis to generate a set of tagged messages; determining associations between the one or more tags and one or more enterprise applications corresponding to the enterprise such that the set of tagged messages are sent to respective ones of the one or more enterprise applications based at least in part on the determined associations: and storing the tagged messages in an actionable social message store, the actionable message store storing in a table format for each message, one or more of a source of the message, a topic parameter associated with the message, data associated with the message, and the one or more tags associated with the message, wherein the semantic analysis performed comprises latent semantic analysis (LSAT), the LSA referring to a form of statistical language modeling that distinguishes two identical words based on a semantic significance of the word. 15. The computer readable medium of claim 14 , wherein the sequence of instructions further comprises steps to display results of the semantic analysis on a dashboard tool. | 0.576602 |
16. A computer-implemented social media intelligence system comprising: a. a digital processor performing a computer program comprising executable instructions stored on a memory device; b. the computer program providing a social media intelligence application comprising: i. a first software module obtaining real-time social media conversations; ii. a second software module automatically classifying, categorizing and summarizing expressed emotions in said real-time social media conversations by using a knowledge base of emotional words, or phrases, or both words and phrases as an input, wherein the knowledge base comprises a list of emotional tokens and pairwise token distance using a degree of similarity, each pair associated with a valence and arousal score expressed on a scale; iii. a third software module defining a continuous distance metric between said real-time social media conversations comprising same or different number of the emotional tokens utilizing an expand-align-compare (EAC) algorithm; iv. a fourth software module conducting hierarchical clustering based on that distance metric; and vi. a fifth software module outputting a result associated with said hierarchical clustering. | 16. A computer-implemented social media intelligence system comprising: a. a digital processor performing a computer program comprising executable instructions stored on a memory device; b. the computer program providing a social media intelligence application comprising: i. a first software module obtaining real-time social media conversations; ii. a second software module automatically classifying, categorizing and summarizing expressed emotions in said real-time social media conversations by using a knowledge base of emotional words, or phrases, or both words and phrases as an input, wherein the knowledge base comprises a list of emotional tokens and pairwise token distance using a degree of similarity, each pair associated with a valence and arousal score expressed on a scale; iii. a third software module defining a continuous distance metric between said real-time social media conversations comprising same or different number of the emotional tokens utilizing an expand-align-compare (EAC) algorithm; iv. a fourth software module conducting hierarchical clustering based on that distance metric; and vi. a fifth software module outputting a result associated with said hierarchical clustering. 23. The system of claim 16 , wherein the valence and arousal scores are derived from a sample of social media communications containing at least one of the tokens. | 0.507192 |
1. A method for managing information in a computer system, the method comprising: (a) the computer receiving a request to store text in a table in a database, wherein the request contains the text; (b) the computer responsive to receiving the request containing the text, determining whether a first collection of textual information having a first concept that is related to a second concept for the text is present in the database, wherein the first concept is related to the second concept when the first concept is within a degree of relatedness to the second concept, wherein step (b) comprises: identifying, by the processing unit, a quantity of available resources for a data processing system in which the processor unit is located; and selecting, by the processing unit, the degree of relatedness based on the quantity of available resources such that the degree of relatedness increases as the quantity of available resources increases and decreases as the quantity of available resources decreases; (c) the computer, responsive to a determination that the first collection of textual information in the database having the first concept that is related to the second concept for the text is present in the database, associating the text with the first collection of textual information in the database; and (d) the computer, responsive to a determination that the first collection of textual information in the database having the first concept that is related to the second concept for the text is absent from the database, creating a second collection for the data with a third concept that is related to the second concept for the text within the degree of relatedness. | 1. A method for managing information in a computer system, the method comprising: (a) the computer receiving a request to store text in a table in a database, wherein the request contains the text; (b) the computer responsive to receiving the request containing the text, determining whether a first collection of textual information having a first concept that is related to a second concept for the text is present in the database, wherein the first concept is related to the second concept when the first concept is within a degree of relatedness to the second concept, wherein step (b) comprises: identifying, by the processing unit, a quantity of available resources for a data processing system in which the processor unit is located; and selecting, by the processing unit, the degree of relatedness based on the quantity of available resources such that the degree of relatedness increases as the quantity of available resources increases and decreases as the quantity of available resources decreases; (c) the computer, responsive to a determination that the first collection of textual information in the database having the first concept that is related to the second concept for the text is present in the database, associating the text with the first collection of textual information in the database; and (d) the computer, responsive to a determination that the first collection of textual information in the database having the first concept that is related to the second concept for the text is absent from the database, creating a second collection for the data with a third concept that is related to the second concept for the text within the degree of relatedness. 7. The method of claim 1 , wherein step (b), step (c), and step (d) are performed responsive to an expiration of a period of time. | 0.657247 |
13. The method of claim 1 , wherein the one or more candidate sibling queries are ranked for each child query. | 13. The method of claim 1 , wherein the one or more candidate sibling queries are ranked for each child query. 16. The method of claim 13 , wherein selecting one or more final sibling queries includes filtering the ranked queries. | 0.96962 |
1. A computer-implemented method for facilitating cross-language communication among users of respective wireless communication devices, the method comprising: receiving, at a first of the wireless communication devices, a first user input indicating willingness to participate in a cross-language communication session with a second of the wireless communication devices; receiving, at the second wireless communication device, a second user input indicating willingness to participate in the cross-language communication session with the first wireless communication device; receiving, at the first wireless communication device, a first signal from a first sensor of the first wireless communication device, other than an antenna; receiving, at the second wireless communication device, a second signal from a second sensor of the second wireless communication device, other than an antenna; automatically comparing the first signal to the second signal to determine whether the first signal and the second signal satisfy a similarity criterion; if the first and second signals satisfy the similarity criterion, automatically establishing the cross-language communication session; and if the cross-language communication session is established: after receiving the first and second user inputs, receiving a first user message entered on the first wireless communication device in a first natural language; automatically generating a translated first user message, including translating the first user message into a second natural language, different than the first natural language; and outputting the translated first user message on the second wireless communication device. | 1. A computer-implemented method for facilitating cross-language communication among users of respective wireless communication devices, the method comprising: receiving, at a first of the wireless communication devices, a first user input indicating willingness to participate in a cross-language communication session with a second of the wireless communication devices; receiving, at the second wireless communication device, a second user input indicating willingness to participate in the cross-language communication session with the first wireless communication device; receiving, at the first wireless communication device, a first signal from a first sensor of the first wireless communication device, other than an antenna; receiving, at the second wireless communication device, a second signal from a second sensor of the second wireless communication device, other than an antenna; automatically comparing the first signal to the second signal to determine whether the first signal and the second signal satisfy a similarity criterion; if the first and second signals satisfy the similarity criterion, automatically establishing the cross-language communication session; and if the cross-language communication session is established: after receiving the first and second user inputs, receiving a first user message entered on the first wireless communication device in a first natural language; automatically generating a translated first user message, including translating the first user message into a second natural language, different than the first natural language; and outputting the translated first user message on the second wireless communication device. 24. A method according to claim 1 , wherein: receiving the first signal comprises receiving an ambient sound signal from a microphone of the first wireless communication device; and receiving the second signal comprises receiving an ambient sound signal from a microphone of the second wireless communication device. | 0.63003 |
8. A printer according to claim 7, wherein each one of said bytes comprises also at least one bit, the sequence of a predetermined number of said bits in subsequent bytes controlling said spacing means for defining the letter spacing of the character being traced, and comprising a shift register for storing said predetermined number of bits during the tracing of a character, said shift register being cleared upon addressing the instructions of a subsequent character. | 8. A printer according to claim 7, wherein each one of said bytes comprises also at least one bit, the sequence of a predetermined number of said bits in subsequent bytes controlling said spacing means for defining the letter spacing of the character being traced, and comprising a shift register for storing said predetermined number of bits during the tracing of a character, said shift register being cleared upon addressing the instructions of a subsequent character. 9. A printer according to claim 8, wherein the last byte of each character includes an indication of the end of routine, recognition means being provided for causing said input means to address the next input signal to said memory in response to the indication of said end of routine. | 0.808589 |
4. A method for predicting the translation quality of a translated input document comprising: receiving an input document pair composed of a plurality of sentence pairs, each sentence pair including a source sentence in a source language and a machine translation of the source language sentence to a target language sentence; for each of the sentence pairs, generating a representation of the sentence pair based on a set of features extracted for the sentence pair; generating a fixed length representation of the input document pair, based on the sentence pair representations, using a Gaussian Mixture Model; predicting a translation quality of the translated input document based on the representation of the input document pair; and outputting a decision to an external device based on the predicted translation quality of the translated input document, wherein the generating of the representation of the sentence pair, the generating of the representation of the input document pair, and the predicting of the translation quality of the translated input document, and the outputting of the decision are performed with a processor. | 4. A method for predicting the translation quality of a translated input document comprising: receiving an input document pair composed of a plurality of sentence pairs, each sentence pair including a source sentence in a source language and a machine translation of the source language sentence to a target language sentence; for each of the sentence pairs, generating a representation of the sentence pair based on a set of features extracted for the sentence pair; generating a fixed length representation of the input document pair, based on the sentence pair representations, using a Gaussian Mixture Model; predicting a translation quality of the translated input document based on the representation of the input document pair; and outputting a decision to an external device based on the predicted translation quality of the translated input document, wherein the generating of the representation of the sentence pair, the generating of the representation of the input document pair, and the predicting of the translation quality of the translated input document, and the outputting of the decision are performed with a processor. 15. The method of claim 4 , wherein the generating of the fixed length representation of the input document pair comprises generating a Fisher vector, based on the gradient of the log-likelihood of the sentence pair representations with respect to the Gaussian Mixture Model. | 0.645498 |
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