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8,726,170 | 12 | 13 | 12. An article comprising a non-transitory machine-readable storage medium embodying instructions that when performed by one or more machines result in operations comprising: initiating a communication between a first user and a second user via a collaboration channel, the second user having an identification for the collaboration channel, the collaboration channel selected from a group consisting of: a telephone call, a Voice of Internet Protocol (VOIP) telephone call or an instant messaging session; automatically initiating a first service to associate the identification with a business entity and to obtain query parameters for the business entity in response to the initiation of the communication; automatically initiating a second service to retrieve contextual information associated with the business entity based on the obtained query parameters for the business entity; categorizing the retrieved contextual information into a plurality of categories; presenting the first user with a list of links for each of the categories. | 12. An article comprising a non-transitory machine-readable storage medium embodying instructions that when performed by one or more machines result in operations comprising: initiating a communication between a first user and a second user via a collaboration channel, the second user having an identification for the collaboration channel, the collaboration channel selected from a group consisting of: a telephone call, a Voice of Internet Protocol (VOIP) telephone call or an instant messaging session; automatically initiating a first service to associate the identification with a business entity and to obtain query parameters for the business entity in response to the initiation of the communication; automatically initiating a second service to retrieve contextual information associated with the business entity based on the obtained query parameters for the business entity; categorizing the retrieved contextual information into a plurality of categories; presenting the first user with a list of links for each of the categories. 13. An article as in claim 12 , wherein the non-transitory machine-readable storage medium further embodies instructions that when performed by one or more machines result in operations comprise: initiating a retrieval service in response to activation of one of the links to obtain documents or objects associated with the corresponding category for presentation to the first user. | 0.550588 |
8,009,334 | 1 | 3 | 1. A non-transitory computer readable medium having computer executable instructions for performing a method of processing expense information, the method comprising: receiving scanned information of a receipt from a scanner, the scanned information including information regarding various types of receipts having various formats and having different sizes, each of said receipts containing expense information printed thereon; processing said scanned information including numerical data in the receipt to obtain said expense information from said scanned information; categorizing said expense information for each receipt into one or more predetermined categories to obtain categorized information for each receipt, wherein said categorized information for each receipt is combined with categorized information for other said receipts to produce report information for one or more of said predetermined categories, wherein the various types of receipts include grocery receipts, purchase receipts, credit card receipts and bank statements. | 1. A non-transitory computer readable medium having computer executable instructions for performing a method of processing expense information, the method comprising: receiving scanned information of a receipt from a scanner, the scanned information including information regarding various types of receipts having various formats and having different sizes, each of said receipts containing expense information printed thereon; processing said scanned information including numerical data in the receipt to obtain said expense information from said scanned information; categorizing said expense information for each receipt into one or more predetermined categories to obtain categorized information for each receipt, wherein said categorized information for each receipt is combined with categorized information for other said receipts to produce report information for one or more of said predetermined categories, wherein the various types of receipts include grocery receipts, purchase receipts, credit card receipts and bank statements. 3. A non-transitory computer readable medium as claimed in claim 1 , wherein the scanned information from the scanned receipts is automatically received from the scanner, and the expense information for each receipt is captured from the scanned information for each receipt and categorized into one or more of said predetermined categories. | 0.5 |
8,818,998 | 1 | 15 | 1. An apparatus for analyzing non-deterministic results of a search query of data representing analogue information, such as audio data, comprising: a processor and a user interface, the processor being operably in communication with a plurality of audio data sources or databases representing the content thereof and adapted to communicate with the user interface which enables the user to query one or more audio data sources for the presence of search constituents within the audio data, wherein the processor is adapted to determine the non-deterministic likelihood of occurrence of the search constituent within at least part of each of the searched data sources for a user query and the user interface is adapted to present to the user the search results in a form including: a portlet presenting the overall search results (such as search strings) against part or all of the search query structure for a data source(s); a portlet presenting the data source (such as by source name) of one or more data source(s); a portlet presenting a data source filter tree for selecting currently active source(s); a portlet presenting the hit(s) of the search phrase(s) for a data source; and a portlet presenting the hit location(s) within a data source, and wherein at least one of the portlets presents the user with information related to the non-deterministic likelihood of occurrence of the search constituent as a probability of the relevance of a searched data source of the search query and parts of the search query, and the user interface further enabling the user to select and inspect at least part of the searched data source(s) for the presence of the search constituents; wherein each of the portlets is presented to the user with relevancy scores to the data as determined by the non-deterministic results and each portlet is updated and synchronized during a change-of-state cascade event whenever the state is changed within any one of the portlets; and wherein each of the portlets enable a user to edit the probable relevance of the data source, convert the non-deterministic results that are returned by the search of the audio data to deterministic results based on the relevancy score by the user interaction with the data source, and altering the relevance of that data source computed for the overall query. | 1. An apparatus for analyzing non-deterministic results of a search query of data representing analogue information, such as audio data, comprising: a processor and a user interface, the processor being operably in communication with a plurality of audio data sources or databases representing the content thereof and adapted to communicate with the user interface which enables the user to query one or more audio data sources for the presence of search constituents within the audio data, wherein the processor is adapted to determine the non-deterministic likelihood of occurrence of the search constituent within at least part of each of the searched data sources for a user query and the user interface is adapted to present to the user the search results in a form including: a portlet presenting the overall search results (such as search strings) against part or all of the search query structure for a data source(s); a portlet presenting the data source (such as by source name) of one or more data source(s); a portlet presenting a data source filter tree for selecting currently active source(s); a portlet presenting the hit(s) of the search phrase(s) for a data source; and a portlet presenting the hit location(s) within a data source, and wherein at least one of the portlets presents the user with information related to the non-deterministic likelihood of occurrence of the search constituent as a probability of the relevance of a searched data source of the search query and parts of the search query, and the user interface further enabling the user to select and inspect at least part of the searched data source(s) for the presence of the search constituents; wherein each of the portlets is presented to the user with relevancy scores to the data as determined by the non-deterministic results and each portlet is updated and synchronized during a change-of-state cascade event whenever the state is changed within any one of the portlets; and wherein each of the portlets enable a user to edit the probable relevance of the data source, convert the non-deterministic results that are returned by the search of the audio data to deterministic results based on the relevancy score by the user interaction with the data source, and altering the relevance of that data source computed for the overall query. 15. The apparatus according to claim 1 , wherein selection from the results hit portlet effects one or more of the following: a change in the set of locations displayed in results hit location portlet; display of a menu allowing verification or invalidation of the hit(s) corresponding to the selection. | 0.636691 |
9,976,859 | 1 | 3 | 1. A method comprising: receiving, by use of a communication device, a request for navigation data from a mobile device, the request having a type; determining, by a processor coupled with the communication device, the type of the request, wherein the type of request is (a) a display request, (b) a route request, (c) a name request, or (d) some combination thereof; generating, by the processor responsive to receipt of the request, a query; generating, by the processor responsive to receipt of the query, a virtual table, the virtual table generated based on (a) one or more parameters determined based on the determined type of the request from the mobile device and (b) a navigation database, coupled with the processor, in which a set of navigation data is stored, the generated virtual table comprising at least a subset of the set of navigation data stored in the navigation database and independent from schema of the navigation database, the subset being determined by the determined type of the request, the generated virtual table and the navigation database being accessible via a same query format; querying, by the processor using the query format, the virtual table based on the request for navigation data to obtain the requested navigation data therefrom; constructing, by the processor, a map tile command based on the obtained navigation data; and sending, by the communication device, the map tile command to the mobile device. | 1. A method comprising: receiving, by use of a communication device, a request for navigation data from a mobile device, the request having a type; determining, by a processor coupled with the communication device, the type of the request, wherein the type of request is (a) a display request, (b) a route request, (c) a name request, or (d) some combination thereof; generating, by the processor responsive to receipt of the request, a query; generating, by the processor responsive to receipt of the query, a virtual table, the virtual table generated based on (a) one or more parameters determined based on the determined type of the request from the mobile device and (b) a navigation database, coupled with the processor, in which a set of navigation data is stored, the generated virtual table comprising at least a subset of the set of navigation data stored in the navigation database and independent from schema of the navigation database, the subset being determined by the determined type of the request, the generated virtual table and the navigation database being accessible via a same query format; querying, by the processor using the query format, the virtual table based on the request for navigation data to obtain the requested navigation data therefrom; constructing, by the processor, a map tile command based on the obtained navigation data; and sending, by the communication device, the map tile command to the mobile device. 3. The method of claim 1 , wherein the route request further includes a route computation request, a link attribute request, or some combination thereof. | 0.633971 |
8,078,960 | 16 | 17 | 16. The method as defined in claim 1 , wherein: the solution identifier includes a target that includes a character string that identifies an application used to create the electronic form associated with the document; and discovering the solution comprises discovering the solution using the character string. | 16. The method as defined in claim 1 , wherein: the solution identifier includes a target that includes a character string that identifies an application used to create the electronic form associated with the document; and discovering the solution comprises discovering the solution using the character string. 17. The method as defined in claim 16 , wherein: the discovering the solution comprises discovering the character string in a URL. | 0.5 |
7,660,791 | 3 | 4 | 3. The media of claim 2 , further comprising assigning link weights based on whether the origination domain is the same as the destination domain. | 3. The media of claim 2 , further comprising assigning link weights based on whether the origination domain is the same as the destination domain. 4. The media of claim 3 , further comprising assigning link weights for both incoming and outgoing links for each node. | 0.634969 |
8,763,038 | 1 | 3 | 1. A method of detecting text in a television video display table, comprising: saving a frame of video representing an image to a memory device; determining that the frame of video contains a table having cells containing text; storing a working copy of the frame of video to a memory; isolating text in the table by: removing any table boundaries from the image; removing any cell boundaries from the image; determining if the image has three dimensional or shadow attributes in the table boundaries or cell boundaries and removing any three dimensional or shadow attributes identified, wherein determining if the image has three dimensional or shadow attributes in the table boundaries or cell boundaries is carried out by finding line patterns adjacent and outside table or cell boundaries that track a table or cell boundary; where determining if a cell has three dimensional or shadow attributes is carried out by subtracting a variable rectangular band of pixels from other cell values to see what size band maximizes cancellation in order to distinguish the cell from the text area; thereby producing text isolated against a contrasting color background; and processing the isolated text using an optical character recognition (OCR) engine to extract the text as data. | 1. A method of detecting text in a television video display table, comprising: saving a frame of video representing an image to a memory device; determining that the frame of video contains a table having cells containing text; storing a working copy of the frame of video to a memory; isolating text in the table by: removing any table boundaries from the image; removing any cell boundaries from the image; determining if the image has three dimensional or shadow attributes in the table boundaries or cell boundaries and removing any three dimensional or shadow attributes identified, wherein determining if the image has three dimensional or shadow attributes in the table boundaries or cell boundaries is carried out by finding line patterns adjacent and outside table or cell boundaries that track a table or cell boundary; where determining if a cell has three dimensional or shadow attributes is carried out by subtracting a variable rectangular band of pixels from other cell values to see what size band maximizes cancellation in order to distinguish the cell from the text area; thereby producing text isolated against a contrasting color background; and processing the isolated text using an optical character recognition (OCR) engine to extract the text as data. 3. The method according to claim 1 , wherein determining that the frame of video contains a table having cells containing text is carried out by tracking remote control commands transmitted from a remote control to identify commands that result in display of a table having cells containing text. | 0.5 |
9,286,291 | 1 | 7 | 1. A computer usable program product comprising a computer usable storage device including computer usable code for disambiguation of dependent referring expression in natural language processing, the computer usable code comprising: computer usable code for selecting a portion of a document in a set of documents, the portion including a set of dependent referring expression instances; computer usable code for filtering the portion to identify an instance from a set of dependent referring expression instances by using a linguistic characteristic of the instance, the instance of dependent referring expression referring to a full expression, the full expression occurring in another document in the set of documents; computer usable code for locating the full expression in one member document in the set of documents by locating where the dependent referring expression is defined to be a stand-in for the full expression; and computer usable code for resolving, using a processor and a memory, the instance using the full expression such that information about the full expression is available at a location of the instance, wherein the computer usable code for resolving comprises: computer usable code for modifying the instance by adding data at a location of the instance, such that the data makes the information about the full expression accessible from the location of the instance; computer usable code for modifying the document to produce a second document, wherein the second document includes a mapping between the instance and the full expression in a metadata section of the second document, the metadata section being distinct from a location of the instance; and computer usable code for linking the instance to the mapping using a link, wherein the link is usable to make the information about the full expression accessible from the location of the instance. | 1. A computer usable program product comprising a computer usable storage device including computer usable code for disambiguation of dependent referring expression in natural language processing, the computer usable code comprising: computer usable code for selecting a portion of a document in a set of documents, the portion including a set of dependent referring expression instances; computer usable code for filtering the portion to identify an instance from a set of dependent referring expression instances by using a linguistic characteristic of the instance, the instance of dependent referring expression referring to a full expression, the full expression occurring in another document in the set of documents; computer usable code for locating the full expression in one member document in the set of documents by locating where the dependent referring expression is defined to be a stand-in for the full expression; and computer usable code for resolving, using a processor and a memory, the instance using the full expression such that information about the full expression is available at a location of the instance, wherein the computer usable code for resolving comprises: computer usable code for modifying the instance by adding data at a location of the instance, such that the data makes the information about the full expression accessible from the location of the instance; computer usable code for modifying the document to produce a second document, wherein the second document includes a mapping between the instance and the full expression in a metadata section of the second document, the metadata section being distinct from a location of the instance; and computer usable code for linking the instance to the mapping using a link, wherein the link is usable to make the information about the full expression accessible from the location of the instance. 7. The computer usable program product of claim 1 , wherein the computer usable code is stored in a computer readable storage medium in a server data processing system, and wherein the computer usable code is downloaded over a network to a remote data processing system for use in a computer readable storage medium associated with the remote data processing system. | 0.5425 |
7,583,647 | 17 | 20 | 17. A method, comprising: determining, by a processor, that a server request is of a type that requires at least one token to be removed from a token pool for said server request to be handled; determining, by the processor, that said server request requires a predetermined number of tokens to be removed from said token pool for said server request to be handled; and handling, by the processor, said server request when said token pool contains said predetermined number of tokens for said request by removing said predetermined number of tokens when said server request is handled by a server with which said token pool is associated, wherein the processor is configured to receive tokens and to provide the tokens to, and remove tokens from a plurality of different pools that include said token pool, wherein each of the plurality of different pools is associated with a different server, and wherein the token pool is configured to hold M tokens. | 17. A method, comprising: determining, by a processor, that a server request is of a type that requires at least one token to be removed from a token pool for said server request to be handled; determining, by the processor, that said server request requires a predetermined number of tokens to be removed from said token pool for said server request to be handled; and handling, by the processor, said server request when said token pool contains said predetermined number of tokens for said request by removing said predetermined number of tokens when said server request is handled by a server with which said token pool is associated, wherein the processor is configured to receive tokens and to provide the tokens to, and remove tokens from a plurality of different pools that include said token pool, wherein each of the plurality of different pools is associated with a different server, and wherein the token pool is configured to hold M tokens. 20. A method as claimed in claim 17 , comprising receiving, by the processor, a predetermined number of tokens and providing, by the processor, the predetermined number of tokens during a first period to said token pool. | 0.5 |
9,047,536 | 1 | 10 | 1. A method for providing target point candidates that form a candidate set for selecting a target point from the candidate set by means of a geodetic measuring device, wherein the measuring device has a targeting unit defining a targeting direction and a camera aligned substantially in the targeting direction, the method comprising: coarsely aligning the measuring device with the target point; detecting an image in the targeting direction; searching for specific target object candidates in the detected image is effected by means of image processing, wherein: the search process is effected on the basis of predefined models; the target object candidates are respectively assigned at least one point representing the respective target object candidate as target point candidate; the target point candidates are assigned to the candidate set; a respective weight value is derived and assigned to the target point candidates; and the target point candidates of the candidate set are respectively provided together with an information item representing the weight value assigned to the respective target point candidate. | 1. A method for providing target point candidates that form a candidate set for selecting a target point from the candidate set by means of a geodetic measuring device, wherein the measuring device has a targeting unit defining a targeting direction and a camera aligned substantially in the targeting direction, the method comprising: coarsely aligning the measuring device with the target point; detecting an image in the targeting direction; searching for specific target object candidates in the detected image is effected by means of image processing, wherein: the search process is effected on the basis of predefined models; the target object candidates are respectively assigned at least one point representing the respective target object candidate as target point candidate; the target point candidates are assigned to the candidate set; a respective weight value is derived and assigned to the target point candidates; and the target point candidates of the candidate set are respectively provided together with an information item representing the weight value assigned to the respective target point candidate. 10. The method as claimed in claim 1 , wherein the target point is determined from the provided target point candidates and the information item representing the weight value assigned to the respective target point candidate by a user or automatically. | 0.556338 |
9,165,032 | 10 | 17 | 10. A highly parallel computing apparatus for an enterprise data warehouse system or a business intelligence system, the apparatus comprising: a plurality of processors in the highly parallel computing apparatus; memory resources in the highly parallel computing apparatus; a database server configured to receive a database query from an application at a database client system; a query compiler configured to prepare an execution plan for the query and compute a number of executive server processes (ESPs) in each ESP layer of the query; a workload management system configured to generate an affinity value based on the current run-time state of the apparatus, wherein the affinity value specifies a subset of processors for a range of processor subset sizes; and a query executor configured to execute the query wherein placement of ESP layers of the query onto processors of the computing system is determined using the affinity value. | 10. A highly parallel computing apparatus for an enterprise data warehouse system or a business intelligence system, the apparatus comprising: a plurality of processors in the highly parallel computing apparatus; memory resources in the highly parallel computing apparatus; a database server configured to receive a database query from an application at a database client system; a query compiler configured to prepare an execution plan for the query and compute a number of executive server processes (ESPs) in each ESP layer of the query; a workload management system configured to generate an affinity value based on the current run-time state of the apparatus, wherein the affinity value specifies a subset of processors for a range of processor subset sizes; and a query executor configured to execute the query wherein placement of ESP layers of the query onto processors of the computing system is determined using the affinity value. 17. The apparatus of claim 10 , wherein the affinity value is randomly generated. | 0.851648 |
7,761,590 | 9 | 12 | 9. An apparatus comprising a processor and a memory including computer program code, the memory and the computer program code configured to, with the processor, cause the apparatus at least to: receive data in a first form markup language comprising full XForms standard, the received data being intended for a client device; adapt portions of the received data which are incompatible with the client device into a second form markup language comprising XForms Basic that is compatible with the client device; capture reply data from the client device in order to validate the reply data; and send an error message to the client device in response to the reply data failing to validate and communicate the reply data to a server providing the data in the first markup language in response to the reply data validating, wherein the memory and computer program code are further configured to, with the processor, replace validation elements in the first form markup language with corresponding constraints in the second form markup language based on a mapping for conversion between Schema data types in full Xforms standard to corresponding constraints, and wherein the mapping includes providing a bind element to designate an XForms Basic data type and additional constraint corresponding to each Schema data type. | 9. An apparatus comprising a processor and a memory including computer program code, the memory and the computer program code configured to, with the processor, cause the apparatus at least to: receive data in a first form markup language comprising full XForms standard, the received data being intended for a client device; adapt portions of the received data which are incompatible with the client device into a second form markup language comprising XForms Basic that is compatible with the client device; capture reply data from the client device in order to validate the reply data; and send an error message to the client device in response to the reply data failing to validate and communicate the reply data to a server providing the data in the first markup language in response to the reply data validating, wherein the memory and computer program code are further configured to, with the processor, replace validation elements in the first form markup language with corresponding constraints in the second form markup language based on a mapping for conversion between Schema data types in full Xforms standard to corresponding constraints, and wherein the mapping includes providing a bind element to designate an XForms Basic data type and additional constraint corresponding to each Schema data type. 12. An apparatus according to claim 9 , wherein the memory and computer program code are further configured to, with the processor, cause the apparatus to determine whether the received data includes data types that are not supported in XForms Basic prior to adapting portions of the received data. | 0.5 |
8,112,412 | 11 | 13 | 11. At least one non-transitory computer readable medium containing a computer program product for suggesting reputable executable files for users to download, the computer program product comprising: program code for maintaining, by a computer, a database of categorization data concerning each of a plurality of executable files, wherein the executable files are available to users for download; wherein categorization data concerning each specific executable file comprises at least a plurality terms extracted from websites accessed by users prior to attempting to download the specific executable file, such that the terms indicate a category of executable file type for the specific executable file; program code for receiving, by a computer, notifications from a plurality of users, each of the notifications comprising at least an identifier of an executable file, downloading of which has been initiated by a user, and indications of terms describing the executable file extracted from at least one web page accessed by that user prior to initiating the downloading; program code for gleaning, by a computer, reputational scores of executable files identified in the received notifications; program code for adding, by a computer, categorization data concerning the identified executable files to the database; program code for, responsive to determining that a reputational score for a specific, identified executable file of a specific category is not acceptable, identifying, by a computer, at least one executable file of the same specific category with an acceptable reputational score; and program code for recommending, by a computer, the at least one executable file of the same specific category to a corresponding user. | 11. At least one non-transitory computer readable medium containing a computer program product for suggesting reputable executable files for users to download, the computer program product comprising: program code for maintaining, by a computer, a database of categorization data concerning each of a plurality of executable files, wherein the executable files are available to users for download; wherein categorization data concerning each specific executable file comprises at least a plurality terms extracted from websites accessed by users prior to attempting to download the specific executable file, such that the terms indicate a category of executable file type for the specific executable file; program code for receiving, by a computer, notifications from a plurality of users, each of the notifications comprising at least an identifier of an executable file, downloading of which has been initiated by a user, and indications of terms describing the executable file extracted from at least one web page accessed by that user prior to initiating the downloading; program code for gleaning, by a computer, reputational scores of executable files identified in the received notifications; program code for adding, by a computer, categorization data concerning the identified executable files to the database; program code for, responsive to determining that a reputational score for a specific, identified executable file of a specific category is not acceptable, identifying, by a computer, at least one executable file of the same specific category with an acceptable reputational score; and program code for recommending, by a computer, the at least one executable file of the same specific category to a corresponding user. 13. The at least one non-transitory computer readable medium of claim 11 wherein the program code for gleaning reputational scores of identified executable files further comprises: program code for receiving reputational scores from a reputation service. | 0.798732 |
8,392,188 | 1 | 16 | 1. A method for building a phonotactic model for domain independent speech recognition, comprising: recognizing phones from a user's input communication using a current phonotactic model stored in a database; detecting morphemes from the recognized phones; creating, via a new processor, a new phonotactic model using the detected morphemes, the creating the new phonotactic model comprising transforming a prior probability distribution associated with a first domain to a prior probability distribution associated with a second domain; replacing the current phonotactic model with the new phonotactic model in the database; and outputting the detected morphemes for processing. | 1. A method for building a phonotactic model for domain independent speech recognition, comprising: recognizing phones from a user's input communication using a current phonotactic model stored in a database; detecting morphemes from the recognized phones; creating, via a new processor, a new phonotactic model using the detected morphemes, the creating the new phonotactic model comprising transforming a prior probability distribution associated with a first domain to a prior probability distribution associated with a second domain; replacing the current phonotactic model with the new phonotactic model in the database; and outputting the detected morphemes for processing. 16. The method of claim 1 , wherein the morphemes are at least one of acoustic morphemes or non-acoustic morphemes. | 0.812704 |
9,280,749 | 17 | 18 | 17. A non-transitory, computer readable storage medium storing instructions that, when executed by a processor, cause the processor to: identify a first set of model data including a first set of device data from a plurality of model computing devices, the first set of device data including location data and access data for the plurality of model computing devices; identify a plurality of categories for an attribute of a population segment, each category defining a segment of the attribute, the population segment including the candidate computing device; train a classification model using the first set of model data and the plurality of categories; receive a device identifier from the candidate computing device, the device identifier including device data of the candidate computing device; apply the device data to the classification model to generate an offline prediction of a category of the plurality of categories for the candidate computing device; determine a location of the candidate computing device using an IP address of the candidate computing device; generate an online prediction of a category of the plurality of categories for the candidate computing device using the location; and generate a composite prediction of a category of the plurality of categories for the candidate computing device by combining the offline prediction and the online prediction. | 17. A non-transitory, computer readable storage medium storing instructions that, when executed by a processor, cause the processor to: identify a first set of model data including a first set of device data from a plurality of model computing devices, the first set of device data including location data and access data for the plurality of model computing devices; identify a plurality of categories for an attribute of a population segment, each category defining a segment of the attribute, the population segment including the candidate computing device; train a classification model using the first set of model data and the plurality of categories; receive a device identifier from the candidate computing device, the device identifier including device data of the candidate computing device; apply the device data to the classification model to generate an offline prediction of a category of the plurality of categories for the candidate computing device; determine a location of the candidate computing device using an IP address of the candidate computing device; generate an online prediction of a category of the plurality of categories for the candidate computing device using the location; and generate a composite prediction of a category of the plurality of categories for the candidate computing device by combining the offline prediction and the online prediction. 18. The non-transitory, computer readable medium of claim 17 , further comprising instructions that cause the processor to: receive, from the candidate computing device, a request for content; select a content item based on the composite prediction; and serve the content item to the candidate computing device in response to the request. | 0.627753 |
8,930,353 | 1 | 3 | 1. A computer system for searching within previous search results for new search results, the computer system comprising: one or more processors, one or more computer-readable memories, one or more computer-readable tangible storage devices and program instructions which are stored on at least one of the one or more computer-readable tangible storage devices for execution by at least one of the one or more processors via at least one of the one or more memories, the program instructions comprising: program instructions to receive first search contexts from a second computer, the first search contexts including parameters including characteristics of elements of second search contexts and parameters including weight of importance to the user that elements of the second search contexts includes the characteristics of the first search contexts, wherein the weight of importance represents the characteristics of elements of the second search contexts that are visited by the user during a previous web page search, and wherein the characteristics of elements of second search contexts include an aggregate of search terms, search results, and data of the first search contexts and of the second search contexts, wherein the data includes user activities during a web page search of the first search contexts and the second search contexts including an accumulation of whether the user bookmarked the second search contexts and the first search contexts, how much time the user spent on the second search contexts and the first search contexts or how often the user visited the second search contexts or the first search contexts; program instructions to search for the characteristics of the elements of the second search contexts in a repository; program instructions to determine a match between the characteristics of the elements of the second search contexts and the characteristics elements of the first search contexts, wherein determination of the characteristics of elements is based on a match of uniform resource locators of the characteristics of the elements of the second search contexts and the characteristics elements of the first search contexts, wherein the program instructions to determine a match between the characteristics of the elements of the second search contexts and the characteristics elements of the first search contexts, further comprises: program instructions to determine if an accumulation of numerical values of both of the characteristics of the elements of the second search contexts and the characteristics elements of the first search contexts exceeds a defined threshold value, and wherein the second search contexts and the first search contexts are considered a match if the accumulated numerical values exceeds the defined threshold value; program instructions to determine if the second search contexts are related to the first search contexts based on the match, and to rank a first list of search results based on the determination, wherein the numerical values are assigned to the characteristics of the elements of the second search contexts and the characteristics elements of the first search contexts; program instructions to merge the second search contexts and first search contexts if the second search contexts are related to the first search contexts; program instructions to create a second list of search results based on the merge; and program instructions to transmit the second list to the second computer. | 1. A computer system for searching within previous search results for new search results, the computer system comprising: one or more processors, one or more computer-readable memories, one or more computer-readable tangible storage devices and program instructions which are stored on at least one of the one or more computer-readable tangible storage devices for execution by at least one of the one or more processors via at least one of the one or more memories, the program instructions comprising: program instructions to receive first search contexts from a second computer, the first search contexts including parameters including characteristics of elements of second search contexts and parameters including weight of importance to the user that elements of the second search contexts includes the characteristics of the first search contexts, wherein the weight of importance represents the characteristics of elements of the second search contexts that are visited by the user during a previous web page search, and wherein the characteristics of elements of second search contexts include an aggregate of search terms, search results, and data of the first search contexts and of the second search contexts, wherein the data includes user activities during a web page search of the first search contexts and the second search contexts including an accumulation of whether the user bookmarked the second search contexts and the first search contexts, how much time the user spent on the second search contexts and the first search contexts or how often the user visited the second search contexts or the first search contexts; program instructions to search for the characteristics of the elements of the second search contexts in a repository; program instructions to determine a match between the characteristics of the elements of the second search contexts and the characteristics elements of the first search contexts, wherein determination of the characteristics of elements is based on a match of uniform resource locators of the characteristics of the elements of the second search contexts and the characteristics elements of the first search contexts, wherein the program instructions to determine a match between the characteristics of the elements of the second search contexts and the characteristics elements of the first search contexts, further comprises: program instructions to determine if an accumulation of numerical values of both of the characteristics of the elements of the second search contexts and the characteristics elements of the first search contexts exceeds a defined threshold value, and wherein the second search contexts and the first search contexts are considered a match if the accumulated numerical values exceeds the defined threshold value; program instructions to determine if the second search contexts are related to the first search contexts based on the match, and to rank a first list of search results based on the determination, wherein the numerical values are assigned to the characteristics of the elements of the second search contexts and the characteristics elements of the first search contexts; program instructions to merge the second search contexts and first search contexts if the second search contexts are related to the first search contexts; program instructions to create a second list of search results based on the merge; and program instructions to transmit the second list to the second computer. 3. The computer system of claim 1 , wherein program instructions to merge the second search contexts and first search contexts if the second search contexts are related to the first search contexts, further comprises: program instructions to merge the second search contexts and first search contexts with the first list of search results. | 0.5 |
8,380,725 | 6 | 7 | 6. The messaging system of claim 1 , wherein the database comprises a plurality of replacement text elements, each of said plurality of replacement text elements corresponding to one of the plurality of permitted words. | 6. The messaging system of claim 1 , wherein the database comprises a plurality of replacement text elements, each of said plurality of replacement text elements corresponding to one of the plurality of permitted words. 7. The messaging system of claim 6 , wherein at least one of the plurality of permitted words does not have any corresponding replacement text element. | 0.5 |
8,214,746 | 2 | 3 | 2. The method of claim 1 , wherein the information regarding the users further comprises indicia of identity of at least one of the users. | 2. The method of claim 1 , wherein the information regarding the users further comprises indicia of identity of at least one of the users. 3. The method of claim 2 , wherein the indicia of identity of the users comprises visual depictions of at least one of the users. | 0.5 |
7,711,573 | 161 | 162 | 161. The computer program product of claim 160 , the computer readable medium further storing: program code for setting the term of experience to zero when the experience range is zero; program code for determining a start time for the experience range when the experience range is greater than zero; program code for determining an end time for the experience range when the experience range is greater than zero; program code for computing a time difference between the start time and the end time when the experience range is greater than zero; and program code for setting the term of experience to the time difference when the experience range is greater than zero, wherein the term of experience is rounded down to a unit of time. | 161. The computer program product of claim 160 , the computer readable medium further storing: program code for setting the term of experience to zero when the experience range is zero; program code for determining a start time for the experience range when the experience range is greater than zero; program code for determining an end time for the experience range when the experience range is greater than zero; program code for computing a time difference between the start time and the end time when the experience range is greater than zero; and program code for setting the term of experience to the time difference when the experience range is greater than zero, wherein the term of experience is rounded down to a unit of time. 162. The computer program product of claim 161 , wherein the program code for setting the term of experience to the time difference further comprises: program code for computing a repeated entry time difference for each said at least one skill or experience-related phrase that is a repeated entry and is associated with an other experience range; and program code for adding to the time difference each repeated entry time difference, wherein the other experience range includes an other start time and an other end time, and wherein the other start time and the start time are different, or the other end time and the end time are different. | 0.5 |
9,218,408 | 10 | 16 | 10. A system for creating a data mart in a business intelligence server environment on a computer including one or more microprocessors, said system comprising: a plurality of data sources that maintain data within an enterprise, said plurality of data sources including at least a relational database and a multidimensional database; a business intelligence server, executing on the computer, that provides a virtual logical semantic model, wherein the virtual logical semantic model is a presentation layer to a user and is logically mapped to a plurality of physical models, each said physical model representing entities in a said data source, wherein the business intelligence server receives a specification of a list of levels and a selection of a said data source for storing aggregated data by using the virtual logical semantic model, wherein the plurality of levels spans data from at least two of said plurality of data sources, wherein said levels are identified by analyzing runtimes of queries on the two or more said data sources, wherein at least one said query reduces runtimes thereof by aggregating the data at said levels; an aggregate matrix, created with the virtual semantic model, which includes the plurality of levels, wherein the aggregate matrix is operable to perform data aggregation only at said levels by creating for each said level, in the said data source, a single aggregate table that contains columns at the each said level and at higher levels in a dimension hierarchy, and creating for each fact table a single aggregate table that includes columns corresponding to primary keys at the each said level; wherein the business intelligence server generates a multidimensional cube that contains the aggregated data in the aggregate tables, and stores the multidimensional cube in the said data source. | 10. A system for creating a data mart in a business intelligence server environment on a computer including one or more microprocessors, said system comprising: a plurality of data sources that maintain data within an enterprise, said plurality of data sources including at least a relational database and a multidimensional database; a business intelligence server, executing on the computer, that provides a virtual logical semantic model, wherein the virtual logical semantic model is a presentation layer to a user and is logically mapped to a plurality of physical models, each said physical model representing entities in a said data source, wherein the business intelligence server receives a specification of a list of levels and a selection of a said data source for storing aggregated data by using the virtual logical semantic model, wherein the plurality of levels spans data from at least two of said plurality of data sources, wherein said levels are identified by analyzing runtimes of queries on the two or more said data sources, wherein at least one said query reduces runtimes thereof by aggregating the data at said levels; an aggregate matrix, created with the virtual semantic model, which includes the plurality of levels, wherein the aggregate matrix is operable to perform data aggregation only at said levels by creating for each said level, in the said data source, a single aggregate table that contains columns at the each said level and at higher levels in a dimension hierarchy, and creating for each fact table a single aggregate table that includes columns corresponding to primary keys at the each said level; wherein the business intelligence server generates a multidimensional cube that contains the aggregated data in the aggregate tables, and stores the multidimensional cube in the said data source. 16. The system of claim 10 , wherein the business intelligence server invokes a Java application programming interface (API) of the said data source in order to configure metadata therein. | 0.536946 |
9,098,545 | 14 | 20 | 14. The method of claim 1 , further comprising the step of creating a group of users comprising at least the other user and the submitter user. | 14. The method of claim 1 , further comprising the step of creating a group of users comprising at least the other user and the submitter user. 20. The computer system of claim 14 , where: the processor further comprises software for allowing one of the interested users to communicate with another of the interested users regarding the news story. | 0.5 |
8,381,236 | 1 | 2 | 1. A method to execute one or more functions of a new service module on a document personalization production system, the method comprising: establishing communication between an application framework of the document personalization production system and the new service module, wherein the new service module is located on a server and the application framework is configured to integrate the new service module in the document personalization production system without reprogramming a production manager of the document personalization production system; registering a machine of the document personalization production system to the application framework by communicating a name, a capability, a control system, and a metadata to the application framework in order to execute the new service module and determining the machine's operating parameters to execute the one or more functions of the new service module; the application framework providing one or more interfaces to enable the production manager to issue instructions and data transmission for executing the one or more functions of the new service module without reprogramming the production manager; and the application framework providing one or more plugins to execute the one or more functions of the new service module in the document personalization production system. | 1. A method to execute one or more functions of a new service module on a document personalization production system, the method comprising: establishing communication between an application framework of the document personalization production system and the new service module, wherein the new service module is located on a server and the application framework is configured to integrate the new service module in the document personalization production system without reprogramming a production manager of the document personalization production system; registering a machine of the document personalization production system to the application framework by communicating a name, a capability, a control system, and a metadata to the application framework in order to execute the new service module and determining the machine's operating parameters to execute the one or more functions of the new service module; the application framework providing one or more interfaces to enable the production manager to issue instructions and data transmission for executing the one or more functions of the new service module without reprogramming the production manager; and the application framework providing one or more plugins to execute the one or more functions of the new service module in the document personalization production system. 2. The method to execute a new service module on a document personalization production system, as in claim 1 , further comprising the application framework communicating to the production manager that the new service module is initialized. | 0.643284 |
7,890,528 | 1 | 5 | 1. A computer-implemented method for refining search results using dynamically displayed categorization information, comprising: under control of one or more computer systems configured with executable instructions, in response to receiving a search query, providing for display a first ordered set of data entries and at least one of a first ordered set of preferred search categories, the first ordered set of data entries selected from a data store based at least in part on the received search query, the first ordered set of preferred search categories selected based at least in part upon search category preference information for at least a portion of the data entries in the first ordered set of data entries, the search category preference information for each data entry in the first ordered set of data entries specifying at least one search category as a preferred search category for the data entry; receiving a selection of a displayed preferred search category; and in response to the received selection, providing for display a second ordered set of data entries and a second ordered set of preferred search categories, the second ordered set of data entries selected from the data store based at least in part on the received search query and the selected preferred search category, wherein the second ordered set of data entries is able to include at least one data entry not included in the first ordered set of data entries, and wherein selecting the first ordered set of preferred search categories includes selecting a pre-defined number of search categories based at least in part upon the corresponding search category preference information. | 1. A computer-implemented method for refining search results using dynamically displayed categorization information, comprising: under control of one or more computer systems configured with executable instructions, in response to receiving a search query, providing for display a first ordered set of data entries and at least one of a first ordered set of preferred search categories, the first ordered set of data entries selected from a data store based at least in part on the received search query, the first ordered set of preferred search categories selected based at least in part upon search category preference information for at least a portion of the data entries in the first ordered set of data entries, the search category preference information for each data entry in the first ordered set of data entries specifying at least one search category as a preferred search category for the data entry; receiving a selection of a displayed preferred search category; and in response to the received selection, providing for display a second ordered set of data entries and a second ordered set of preferred search categories, the second ordered set of data entries selected from the data store based at least in part on the received search query and the selected preferred search category, wherein the second ordered set of data entries is able to include at least one data entry not included in the first ordered set of data entries, and wherein selecting the first ordered set of preferred search categories includes selecting a pre-defined number of search categories based at least in part upon the corresponding search category preference information. 5. The computer-implemented method of claim 1 , further comprising: applying search category selection rules to identify and order the preferred search categories. | 0.640969 |
7,657,504 | 1 | 2 | 1. A computing system for identifying sights for locations, comprising: a memory storing computer-executable instructions of: a component that receives a start location and an end location; a component that identifies travel locations on a travel path from the start location to the end location; a component that identifies sights associated with the identified travel locations by, for each travel location, submitting, to an image search service, an indication of the travel location as a search request; receiving, from the image search service, metadata relating to images determined by the image search service to match the search request, the metadata for each image including a text description of the image; identifying, from the text description of the received metadata, candidate sights; submitting, to a geographic name service, an indication each candidate sight to determine whether the candidate sight corresponds to a geographic name; and when the geographic name service indicates that a candidate sight corresponds to a geographic name, indicating that the candidate sight is a sight identified with the travel location; a component that retrieves images associated with the identified sights; and a component that displays one or more of the retrieved; and a processor for executing the computer-executable instructions stored in the memory. | 1. A computing system for identifying sights for locations, comprising: a memory storing computer-executable instructions of: a component that receives a start location and an end location; a component that identifies travel locations on a travel path from the start location to the end location; a component that identifies sights associated with the identified travel locations by, for each travel location, submitting, to an image search service, an indication of the travel location as a search request; receiving, from the image search service, metadata relating to images determined by the image search service to match the search request, the metadata for each image including a text description of the image; identifying, from the text description of the received metadata, candidate sights; submitting, to a geographic name service, an indication each candidate sight to determine whether the candidate sight corresponds to a geographic name; and when the geographic name service indicates that a candidate sight corresponds to a geographic name, indicating that the candidate sight is a sight identified with the travel location; a component that retrieves images associated with the identified sights; and a component that displays one or more of the retrieved; and a processor for executing the computer-executable instructions stored in the memory. 2. The computing system of claim 1 wherein the component that displays the retrieved images simultaneously displays a map encompassing the start location and the end location. | 0.5 |
10,135,887 | 4 | 6 | 4. The method of claim 1 , further comprising: receiving an indication of a selection to view the source video content and the associated first annotation; and providing the first annotation and associated metadata for display or play of the first annotation, wherein the first annotation is synchronized with the source video content. | 4. The method of claim 1 , further comprising: receiving an indication of a selection to view the source video content and the associated first annotation; and providing the first annotation and associated metadata for display or play of the first annotation, wherein the first annotation is synchronized with the source video content. 6. The method of claim 4 , wherein receiving an indication of a selection to view the source video content and the first annotation comprises receiving an indication of a selection to view the source video content and the first annotation on a same device or on separate devices. | 0.818123 |
8,417,712 | 13 | 15 | 13. A system for presenting images selected from an image store in response to a query, the system comprising: an image selecting component configured to select images from the image store relating to the query; an image query relevance score computing component configured to, for respective selected images, compute a query relevance score relating to the query; an image instance generating component configured to, for respective selected images: generate a first image instance of the image scaled at a first zoom level proportional to the query relevance score; and upon receiving a request to generate 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, provide the differential image data set for the selected image instance; an image instance set preparing component configured to prepare an image instance set of image instances; and an image instance set presenting component configured to: present the image instance set of the first image instances, and upon receiving a selection of a selected image: selecting a second zoom level for the selected image instance that is greater than the first image instance; requesting from the image instance generating component 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, at least one component comprising a set of software instructions stored in a memory of a device and executable on a processor of the device. | 13. A system for presenting images selected from an image store in response to a query, the system comprising: an image selecting component configured to select images from the image store relating to the query; an image query relevance score computing component configured to, for respective selected images, compute a query relevance score relating to the query; an image instance generating component configured to, for respective selected images: generate a first image instance of the image scaled at a first zoom level proportional to the query relevance score; and upon receiving a request to generate 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, provide the differential image data set for the selected image instance; an image instance set preparing component configured to prepare an image instance set of image instances; and an image instance set presenting component configured to: present the image instance set of the first image instances, and upon receiving a selection of a selected image: selecting a second zoom level for the selected image instance that is greater than the first image instance; requesting from the image instance generating component 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, at least one component comprising a set of software instructions stored in a memory of a device and executable on a processor of the device. 15. The system of claim 13 , the image instance set preparing component configured to prepare an arbitrarily zoomable image set of arbitrarily zoomable image instances. | 0.787879 |
7,647,210 | 1 | 8 | 1. An electronic method for parametric modeling of a conceptual vehicle design, the method comprising the steps of: (a) receiving dimensional input including one or more vehicle level parameters and one or more component level parameters; (b) receiving geometrical input including one or more non-dimensional design inputs; and (c) generating a parametric concept model based on the dimensional input and the geometrical input, wherein the parametric concept model includes a parametric skeleton having one or more control profiles and one or more control openings associated therewith; and (d) adjusting the one or more control profiles or the one or more control openings to modify the parametric concept model; (e) generating a generic skeleton, having generic geometry associated therewith, based on the parametric skeleton; (f) generating a design skeleton, having vehicle specific geometry associated therewith; and (g) either iterating the generic skeleton without updating the design skeleton or iterating the design skeleton without updating the generic skeleton. | 1. An electronic method for parametric modeling of a conceptual vehicle design, the method comprising the steps of: (a) receiving dimensional input including one or more vehicle level parameters and one or more component level parameters; (b) receiving geometrical input including one or more non-dimensional design inputs; and (c) generating a parametric concept model based on the dimensional input and the geometrical input, wherein the parametric concept model includes a parametric skeleton having one or more control profiles and one or more control openings associated therewith; and (d) adjusting the one or more control profiles or the one or more control openings to modify the parametric concept model; (e) generating a generic skeleton, having generic geometry associated therewith, based on the parametric skeleton; (f) generating a design skeleton, having vehicle specific geometry associated therewith; and (g) either iterating the generic skeleton without updating the design skeleton or iterating the design skeleton without updating the generic skeleton. 8. The electronic method of claim 1 wherein the one or more component level parameters is a flange length. | 0.788 |
7,885,468 | 1 | 8 | 1. A system for grouping cluster spines into a two-dimensional visual display space, comprising: a spine generator to obtain clusters of concepts each extracted from one or more documents and to form spines by placing the clusters sharing at least one of the concepts along a vector; a spine ordering module to order the spines based on a length of each spine; a spine placement module to select one or more of the spines, each unique from the other spines, as unique spines and to place the unique spines into a visual display space; a similarity module to determine a similarity between at least one of the spines not placed and each of the placed unique spines and to identify the placed unique spine most similar; an anchor selection module to select at least one anchor cluster on the most similar unique spine that satisfies a threshold similarity with the unplaced spine; a grafting module to identify one of the clusters on the unplaced spine that is most similar to the selected anchor cluster and to graft the most similar cluster to the selected anchor cluster such that the unplaced spine is positioned along a vector extending from a center of the selected anchor cluster to form a group of cluster spines; and a display to display the group of cluster spines in the visual display space. | 1. A system for grouping cluster spines into a two-dimensional visual display space, comprising: a spine generator to obtain clusters of concepts each extracted from one or more documents and to form spines by placing the clusters sharing at least one of the concepts along a vector; a spine ordering module to order the spines based on a length of each spine; a spine placement module to select one or more of the spines, each unique from the other spines, as unique spines and to place the unique spines into a visual display space; a similarity module to determine a similarity between at least one of the spines not placed and each of the placed unique spines and to identify the placed unique spine most similar; an anchor selection module to select at least one anchor cluster on the most similar unique spine that satisfies a threshold similarity with the unplaced spine; a grafting module to identify one of the clusters on the unplaced spine that is most similar to the selected anchor cluster and to graft the most similar cluster to the selected anchor cluster such that the unplaced spine is positioned along a vector extending from a center of the selected anchor cluster to form a group of cluster spines; and a display to display the group of cluster spines in the visual display space. 8. A system according to claim 1 , further comprising: a unique spine identification module to select, based on the spine length, the longest spine as an initial unique spine for placement into the visual display space. | 0.752822 |
8,744,989 | 11 | 12 | 11. A system, comprising: one or more processors; and memory having instructions stored thereon, the instructions, when executed by the one or more processors cause the processors to perform operations comprising: determining a respective approval metric for each of a plurality of content submissions, the respective approval metric being at least partially based on a number of favorability indications associated with the content submission, wherein each favorability indication indicates either positive or negative favorability; determining a statistical upper bound for the respective approval metric of each content submission according to a first scaling method, wherein the first scaling method scales up the current value of the respective approval metric by a decreasing amount with an increasing number of favorability indications associated with the content submission; generating a priority ranking for the plurality of content submissions according to the statistical upper bound calculated for the respective approval metric of each of the content submissions; and selecting one or more content submissions in the priority ranking as featured content submissions for eliciting votes form one or more users, the selecting being according to respective ranks of the one or more content submissions in the priority ranking. | 11. A system, comprising: one or more processors; and memory having instructions stored thereon, the instructions, when executed by the one or more processors cause the processors to perform operations comprising: determining a respective approval metric for each of a plurality of content submissions, the respective approval metric being at least partially based on a number of favorability indications associated with the content submission, wherein each favorability indication indicates either positive or negative favorability; determining a statistical upper bound for the respective approval metric of each content submission according to a first scaling method, wherein the first scaling method scales up the current value of the respective approval metric by a decreasing amount with an increasing number of favorability indications associated with the content submission; generating a priority ranking for the plurality of content submissions according to the statistical upper bound calculated for the respective approval metric of each of the content submissions; and selecting one or more content submissions in the priority ranking as featured content submissions for eliciting votes form one or more users, the selecting being according to respective ranks of the one or more content submissions in the priority ranking. 12. The system of claim 11 , wherein the first scaling method scales the current value of the respective approval metric to an upper bound of a Wilson score interval calculated for the current value of the respective approval ratio. | 0.835227 |
8,078,463 | 1 | 25 | 1. A computerized method for spotting an at least one call interaction out of a multiplicity of call interactions, in which an at least one target speaker participates, the method comprising: capturing at least one target speaker speech sample of the at least one target speaker by a speech capture device; generating by a computerized engine a multiplicity of speaker models based on a multiplicity of speaker speech samples from the at least one call interaction; matching by a computerized server the at least one target speaker speech sample with speaker models the multiplicity of speaker models to determine a target speaker model; determining a score for each call interaction of the multiplicity of call interactions according to a comparison between the target speaker model and the multiplicity of speaker models; and based on scores that are higher than a predetermined threshold, determining call interactions, of the multiplicity of call interactions, in which the at least one target speaker participates. | 1. A computerized method for spotting an at least one call interaction out of a multiplicity of call interactions, in which an at least one target speaker participates, the method comprising: capturing at least one target speaker speech sample of the at least one target speaker by a speech capture device; generating by a computerized engine a multiplicity of speaker models based on a multiplicity of speaker speech samples from the at least one call interaction; matching by a computerized server the at least one target speaker speech sample with speaker models the multiplicity of speaker models to determine a target speaker model; determining a score for each call interaction of the multiplicity of call interactions according to a comparison between the target speaker model and the multiplicity of speaker models; and based on scores that are higher than a predetermined threshold, determining call interactions, of the multiplicity of call interactions, in which the at least one target speaker participates. 25. The method of claim 1 , further comprising the step of outputting at least one of the multiplicity of call interactions in which the at least one target speaker participates. | 0.785542 |
7,849,089 | 1 | 2 | 1. A method in a computer system for calculating importance of a document, the method comprising: providing user, query, and document triplets indicating that the user submitted the query and that the user selected the document from a result of the query; receiving an input user, an input query, and an input document as an input triplet, the input query having been submitted by the input user and the input document being a document of a search result for the input query; and determining a probability that the user will find the input document important by performing a smoothing of the provided triplets to account for triplets not provided and calculating the probability based on the smoothing wherein the determined probability is based at least in part on the provided triplets for users other than the input user and wherein the probability is the probability of the input document given the input user and the input query when the corresponding user, query, and document triplet is provided and is the probability of the input document given the input query otherwise and wherein a back-off factor is applied to the probability. | 1. A method in a computer system for calculating importance of a document, the method comprising: providing user, query, and document triplets indicating that the user submitted the query and that the user selected the document from a result of the query; receiving an input user, an input query, and an input document as an input triplet, the input query having been submitted by the input user and the input document being a document of a search result for the input query; and determining a probability that the user will find the input document important by performing a smoothing of the provided triplets to account for triplets not provided and calculating the probability based on the smoothing wherein the determined probability is based at least in part on the provided triplets for users other than the input user and wherein the probability is the probability of the input document given the input user and the input query when the corresponding user, query, and document triplet is provided and is the probability of the input document given the input query otherwise and wherein a back-off factor is applied to the probability. 2. The method of claim 1 wherein the probability is based on the probability that the input document is in a document cluster and the probability of the document cluster given a user cluster and query cluster. | 0.5 |
7,689,546 | 7 | 8 | 7. The search method of claim 6 , wherein the interactive database is a legal database, wherein the documents are legal cases, and wherein the ideas are points of law. | 7. The search method of claim 6 , wherein the interactive database is a legal database, wherein the documents are legal cases, and wherein the ideas are points of law. 8. The search method of claim 7 , wherein: the search form allows the user to specify a jurisdiction and a date restriction, and in step (c) the search request is generated based also on any jurisdiction and date restriction specified by the user | 0.5 |
8,195,772 | 37 | 39 | 37. A method according to claim 1 , wherein said modifying of data provided at a later time comprises determining a context. | 37. A method according to claim 1 , wherein said modifying of data provided at a later time comprises determining a context. 39. A method according to claim 37 , wherein determining a context comprises determining spatial positions of labels associated with an element to be modified based on the customization definitions. | 0.5 |
9,311,388 | 1 | 2 | 1. A method for semantic and contextual searching over a knowledge repository to provide a record for a target concept based on a search context set by records of at least one related concept previously authored in a project, wherein the method comprises: creating a search query for each of at least one concept related to the target concept to form a search context, wherein the search query for each of the at least one related concept comprises at least one word derived from at least one record of that concept previously authored in the project; running the search query on a search index of a knowledge repository to identify at least one record of the at least one related concept for which the search query is created; fetching the at least one record of the target concept from the repository as a search result such that the at least one fetched record of the target concept is linked in the knowledge repository to a record of the at least one related concept returned as a result of running the search query on at least one record of the at least one related concept; and generating a rank for each fetched record of the target concept, wherein the rank is a recursive weighted mean of a product of a relevance rank returned from the search query on the search index for the at least one record of the related concept and a relevance score between the target concept and the at least one related concept; wherein the recursive weighted mean is computed by: adding the product of the relevance rank and the relevance score to a first variable to obtain a first sum; adding the relevance score to a second variable to obtain a second sum; dividing the first sum by the second sum to obtain the rank for a given fetched record; designating the first sum as the first variable; designating the second sum as the second variable; and based on previously executed designating steps, repeating the adding steps, the dividing step, and the designating steps for each subsequent fetched record until each fetched record has been ranked; wherein the relevance score between all pairs of concepts is computed by computing: for each concept, a textual similarity between all pairs of records of that concept wherein when the textual similarity exceeds a threshold, the pair of records are considered related; and for all pairs of concepts, a probability of a first pair of records of a first concept being related to each other, given that a second pair of records of a second concept is related, and respectively linked to the first pair of records of the first concept; wherein the steps are carried out by at least one computer device; wherein the knowledge repository comprises records from at least one past project; wherein the records from the at least one past project are defined in an information schema; and wherein two or more records produced in the at least one past project are deemed linked if their respective concepts are related in the information schema. | 1. A method for semantic and contextual searching over a knowledge repository to provide a record for a target concept based on a search context set by records of at least one related concept previously authored in a project, wherein the method comprises: creating a search query for each of at least one concept related to the target concept to form a search context, wherein the search query for each of the at least one related concept comprises at least one word derived from at least one record of that concept previously authored in the project; running the search query on a search index of a knowledge repository to identify at least one record of the at least one related concept for which the search query is created; fetching the at least one record of the target concept from the repository as a search result such that the at least one fetched record of the target concept is linked in the knowledge repository to a record of the at least one related concept returned as a result of running the search query on at least one record of the at least one related concept; and generating a rank for each fetched record of the target concept, wherein the rank is a recursive weighted mean of a product of a relevance rank returned from the search query on the search index for the at least one record of the related concept and a relevance score between the target concept and the at least one related concept; wherein the recursive weighted mean is computed by: adding the product of the relevance rank and the relevance score to a first variable to obtain a first sum; adding the relevance score to a second variable to obtain a second sum; dividing the first sum by the second sum to obtain the rank for a given fetched record; designating the first sum as the first variable; designating the second sum as the second variable; and based on previously executed designating steps, repeating the adding steps, the dividing step, and the designating steps for each subsequent fetched record until each fetched record has been ranked; wherein the relevance score between all pairs of concepts is computed by computing: for each concept, a textual similarity between all pairs of records of that concept wherein when the textual similarity exceeds a threshold, the pair of records are considered related; and for all pairs of concepts, a probability of a first pair of records of a first concept being related to each other, given that a second pair of records of a second concept is related, and respectively linked to the first pair of records of the first concept; wherein the steps are carried out by at least one computer device; wherein the knowledge repository comprises records from at least one past project; wherein the records from the at least one past project are defined in an information schema; and wherein two or more records produced in the at least one past project are deemed linked if their respective concepts are related in the information schema. 2. The method of claim 1 , wherein the rank makes use of a pre-computed relevance score between concepts in an information schema in the knowledge repository. | 0.797436 |
9,263,048 | 15 | 16 | 15. A computer program product, encoded on a non-transitory computer-readable medium, operable to cause one or more processors to perform operations for correcting words in transcribed text, the operations comprising: providing a first transcription of an utterance, wherein the first transcription of the utterance includes one or more words; receiving data indicating a selection of a word from among the one or more words included in the first transcription of the utterance; in response to receiving the data indicating the selection of the word, providing one or more alternate words for the selected word; receiving data indicating a selection of a particular alternate word from among the one or more alternate words for the selected word; selecting a second transcription of the utterance that includes the particular alternate word and that is identified as having a speech recognition confidence measure value that satisfies one or more criteria; and replacing the first transcription of the utterance with the second transcription of the utterance. | 15. A computer program product, encoded on a non-transitory computer-readable medium, operable to cause one or more processors to perform operations for correcting words in transcribed text, the operations comprising: providing a first transcription of an utterance, wherein the first transcription of the utterance includes one or more words; receiving data indicating a selection of a word from among the one or more words included in the first transcription of the utterance; in response to receiving the data indicating the selection of the word, providing one or more alternate words for the selected word; receiving data indicating a selection of a particular alternate word from among the one or more alternate words for the selected word; selecting a second transcription of the utterance that includes the particular alternate word and that is identified as having a speech recognition confidence measure value that satisfies one or more criteria; and replacing the first transcription of the utterance with the second transcription of the utterance. 16. The computer program product of claim 15 , wherein the one or more words are selected from a hierarchical word lattice. | 0.823276 |
9,536,146 | 1 | 8 | 1. A method implemented at least partially by a processor, the method comprising: accessing, from a database associated with a content service provider, logs of global positioning system (GPS) points collected by geolocation sensors associated with service vehicles; identifying geographical locations from the GPS points to represent an area where the service vehicles travelled as recorded in the logs; generating a graph of regions associated with the area based at least in part on the logs associated with the area in a plurality of time frames; detecting outliers in the GPS points based at least in part on the graph; and providing, via a user interface that is presented via a display of a device, recommendations for travelling in the area based at least in part on the outliers. | 1. A method implemented at least partially by a processor, the method comprising: accessing, from a database associated with a content service provider, logs of global positioning system (GPS) points collected by geolocation sensors associated with service vehicles; identifying geographical locations from the GPS points to represent an area where the service vehicles travelled as recorded in the logs; generating a graph of regions associated with the area based at least in part on the logs associated with the area in a plurality of time frames; detecting outliers in the GPS points based at least in part on the graph; and providing, via a user interface that is presented via a display of a device, recommendations for travelling in the area based at least in part on the outliers. 8. The method of claim 1 , further comprising: creating a three-dimensional unit cube for individual time frames of the plurality of time frames, the three-dimensional unit cube including a feature vector comprising: a total number of service vehicles on a link between an origin region and a destination region of the regions associated with the area; a proportion of the service vehicles among the total number of the service vehicles moving out of the origin region during a particular time frame; and a proportion of the service vehicles among the total number of the service vehicles moving into the destination region in the particular time frame and identifying extreme points farthest away from a center data cluster as at least some of the outliers in the particular time frame. | 0.5 |
9,325,508 | 17 | 18 | 17. The method of claim 6 , wherein the location of the signature data structure in the to be signed electronic document is a signature field. | 17. The method of claim 6 , wherein the location of the signature data structure in the to be signed electronic document is a signature field. 18. The method of claim 17 , further comprising the step of the signature authority placing in a Reason entry of the signature field an assertion that the signature authority applied its digital signature to the to be signed electronic document for the purpose of certifying that the signing party has legally signed the to be signed electronic document. | 0.5 |
9,928,232 | 8 | 10 | 8. A computing device for generating a word candidate to assist a user providing an input to the computing device, comprising: a processor; and a memory having a set of computer-executable instructions stored thereupon which, when executed by the processor, cause the computing device to receive, at the computing device, the input containing a plurality of words; determine a conditional count; determine an unconditional count; determine an adjustment factor for a pair of words of the plurality of words based on the unconditional count and the conditional count; generate a data structure defining a plurality of word clusters, individual word clusters of the plurality of word clusters include at least one word of the plurality of words; reconstruct the adjustment factor of the pair of words based on a number of common clusters between individual words of the pair of words; determine a candidate probability associated with the word candidate based, at least in part, on the reconstructed adjustment factor, wherein the word candidate is selected from individual words associated with the plurality of word clusters; generate an output containing the word candidate based, at least in part, on the candidate probability; and display the word candidate on a display screen of the computing device. | 8. A computing device for generating a word candidate to assist a user providing an input to the computing device, comprising: a processor; and a memory having a set of computer-executable instructions stored thereupon which, when executed by the processor, cause the computing device to receive, at the computing device, the input containing a plurality of words; determine a conditional count; determine an unconditional count; determine an adjustment factor for a pair of words of the plurality of words based on the unconditional count and the conditional count; generate a data structure defining a plurality of word clusters, individual word clusters of the plurality of word clusters include at least one word of the plurality of words; reconstruct the adjustment factor of the pair of words based on a number of common clusters between individual words of the pair of words; determine a candidate probability associated with the word candidate based, at least in part, on the reconstructed adjustment factor, wherein the word candidate is selected from individual words associated with the plurality of word clusters; generate an output containing the word candidate based, at least in part, on the candidate probability; and display the word candidate on a display screen of the computing device. 10. The computing device of claim 8 , wherein the computer-executable instructions cause the computing device to: receive a text entry; determine one or more word clusters of the plurality of word clusters associated with the text entry; obtain a freshness factor associated with the one or more word clusters of the plurality of word clusters associated with the text entry; obtain a related adjustment factor associated with the one or more word clusters; obtain a language model value; and determine the candidate probability associated with the word candidate based, at least in part, on the language model value and the related adjustment factor, wherein the word candidate is selected from individual words associated with the plurality of word clusters. | 0.5 |
9,830,533 | 8 | 12 | 8. A computer system for analyzing image data, the system comprising: a memory; and a processor system communicatively coupled to the memory; the processor system configured to perform a method comprising: receiving image data of one or more images that have been posted to internet websites; analyzing the image data to extract image feature data for each one of the one or more images; analyzing the image data to extract metadata of the one or more images; creating multiple image files; wherein each one of the multiple image files comprises individual ones of the one or more images linked together based on overlaps between the image feature data of the individual ones of the one or more images; wherein each one of the multiple image files further comprises individual ones of the one or more images linked together based on overlaps between the metadata of the individual ones of the one or more images; indexing the multiple image files to form multiple indexed image files; wherein the indexing is based on the image feature data of the individual ones of the one or more images in an individual indexed image file; wherein the indexing is further based on the metadata of the individual ones of the one or more images in the individual indexed image file; and storing the multiple indexed image files in the memory, wherein the memory includes a searchable indexed data storage structure. | 8. A computer system for analyzing image data, the system comprising: a memory; and a processor system communicatively coupled to the memory; the processor system configured to perform a method comprising: receiving image data of one or more images that have been posted to internet websites; analyzing the image data to extract image feature data for each one of the one or more images; analyzing the image data to extract metadata of the one or more images; creating multiple image files; wherein each one of the multiple image files comprises individual ones of the one or more images linked together based on overlaps between the image feature data of the individual ones of the one or more images; wherein each one of the multiple image files further comprises individual ones of the one or more images linked together based on overlaps between the metadata of the individual ones of the one or more images; indexing the multiple image files to form multiple indexed image files; wherein the indexing is based on the image feature data of the individual ones of the one or more images in an individual indexed image file; wherein the indexing is further based on the metadata of the individual ones of the one or more images in the individual indexed image file; and storing the multiple indexed image files in the memory, wherein the memory includes a searchable indexed data storage structure. 12. The computer system of claim 8 , wherein: the processor system includes a machine learning processor; the analyzing of the image data to extract image features data for each one of the one or more images is performed using the machine learning processor; the analyzing of the image data to extract metadata of the one or more images is performed using the machine learning processor. | 0.557208 |
8,423,582 | 3 | 4 | 3. The method of claim 1 , wherein enabling the user to publish the displayed message to an external source further comprising: receiving a first input selecting a target application or network resource; receiving a second input specifying an intent to publish the message on the selected target application or network resource; responsive to the second input, replacing the token with structured data retrieved from a field of the database record corresponding to the token; and sending the message, including the retrieved structured and received unstructured data, to the target application or network resource for publishing. | 3. The method of claim 1 , wherein enabling the user to publish the displayed message to an external source further comprising: receiving a first input selecting a target application or network resource; receiving a second input specifying an intent to publish the message on the selected target application or network resource; responsive to the second input, replacing the token with structured data retrieved from a field of the database record corresponding to the token; and sending the message, including the retrieved structured and received unstructured data, to the target application or network resource for publishing. 4. The method of claim 3 , further comprising: displaying a current character count or character limit for the selected target application or network resource. | 0.5 |
8,775,162 | 5 | 6 | 5. A method in accordance with claim 1 wherein: the output communication includes information about the person originating the at least one communication. | 5. A method in accordance with claim 1 wherein: the output communication includes information about the person originating the at least one communication. 6. A method in accordance with claim 5 wherein the output is intended to change negative content in the at least one communication. | 0.5 |
7,555,711 | 7 | 8 | 7. The method of claim 6 , wherein the determining of the extendable ones of the bounding rectangles comprises computing distances between the candidate boundary and edges of the bounding rectangles on the unjustified side of the text block. | 7. The method of claim 6 , wherein the determining of the extendable ones of the bounding rectangles comprises computing distances between the candidate boundary and edges of the bounding rectangles on the unjustified side of the text block. 8. The method of claim 7 , wherein the determining of the extendable ones of the bounding rectangles additionally comprises comparing the distance computed for one of the text lines to a length of a beginning word of an adjacent one of the text lines. | 0.5 |
9,904,676 | 21 | 29 | 21. A computer program product comprising: at least one computer readable non-transitory memory medium having program code instructions stored thereon, the program code instructions which when executed by an apparatus cause the apparatus at least to: identify a time period to be described linguistically in an output text; identify a communicative context for the output text; determine one or more temporal reference frames that are applicable to the time period and a domain defined by the communicative context; and generate a phrase specification that linguistically describes the time period based on a descriptor that is defined by a temporal reference frame of the one or more temporal reference frames, wherein the descriptor specifies a time window that is inclusive of at least a portion of the time period to be described linguistically. | 21. A computer program product comprising: at least one computer readable non-transitory memory medium having program code instructions stored thereon, the program code instructions which when executed by an apparatus cause the apparatus at least to: identify a time period to be described linguistically in an output text; identify a communicative context for the output text; determine one or more temporal reference frames that are applicable to the time period and a domain defined by the communicative context; and generate a phrase specification that linguistically describes the time period based on a descriptor that is defined by a temporal reference frame of the one or more temporal reference frames, wherein the descriptor specifies a time window that is inclusive of at least a portion of the time period to be described linguistically. 29. A computer program product according to claim 21 , wherein the time period comprises at least one of a time point or a series of time points. | 0.854125 |
7,711,560 | 9 | 11 | 9. The speech recognition apparatus according to claim 1 , wherein said estimate value calculation unit includes a non-language phenomenon estimation unit operable to calculate an estimate value of a non-language phenomenon which is related to the non-language speech based on user's information interlocking the non-language speech of one of the plurality of necessary words, and said garbage acoustic score correction unit corrects the garbage acoustic score so as to raise the score using the estimate value in the frame where the non-language phenomenon which is calculated by said non-language phenomenon estimation unit is inputted. | 9. The speech recognition apparatus according to claim 1 , wherein said estimate value calculation unit includes a non-language phenomenon estimation unit operable to calculate an estimate value of a non-language phenomenon which is related to the non-language speech based on user's information interlocking the non-language speech of one of the plurality of necessary words, and said garbage acoustic score correction unit corrects the garbage acoustic score so as to raise the score using the estimate value in the frame where the non-language phenomenon which is calculated by said non-language phenomenon estimation unit is inputted. 11. The speech recognition apparatus according to claim 9 , further comprising an agent control unit operable to control an agent's movement which is displayed on a screen and composite tones of an agent's speech based on the recognition result outputted by said recognition result output unit and the estimate value estimated by said non-language speech estimation unit. | 0.58686 |
9,633,048 | 17 | 18 | 17. The system of claim 15 , wherein the text/image comparison module is further configured for: determining whether the first image matches all of the at least one semantic roles of the first sentence fragment and whether the second image matches all of the at least one semantic roles of the second sentence fragment. | 17. The system of claim 15 , wherein the text/image comparison module is further configured for: determining whether the first image matches all of the at least one semantic roles of the first sentence fragment and whether the second image matches all of the at least one semantic roles of the second sentence fragment. 18. The system of claim 17 , wherein the text/image comparison module is further configured for: responsive to determining that at least one of the first image and the second image do not correspond to all semantic roles of the first sentence fragment and the second sentence fragment, instructing the text sentence analyzer to split at least one of the first sentence fragment and the second sentence fragment into two more sentence fragments. | 0.5 |
8,155,943 | 20 | 22 | 20. The computer system for converting a computer aided design drawing file of an electrical power system into one or more component objects for power analytic analysis and simulation, as recited in claim 1 , wherein the analytics engine is further configured to simulate an operational event of the electrical power system. | 20. The computer system for converting a computer aided design drawing file of an electrical power system into one or more component objects for power analytic analysis and simulation, as recited in claim 1 , wherein the analytics engine is further configured to simulate an operational event of the electrical power system. 22. The computer system for converting a computer aided design drawing file of an electrical power system into one or more component objects for power analytic analysis and simulation, as recited in claim 20 , wherein the event is a power system arc flash incident. | 0.5 |
8,972,370 | 3 | 15 | 3. The repetitive fusion search system according to claim 1 , wherein: the searcher information database is configured to store a history of input information and target selections received from the searcher; and the search engine is configured to estimate, with respect to the input information input by the searcher, search tendency information for the at least one instinct based search parameter and a search parameter significance level based on the history of input information, wherein the search engine is also configured to search the search target database by adjusting the input information using the estimation result or by adjusting a matching degree calculation between the input information and the one or more relevant targets. | 3. The repetitive fusion search system according to claim 1 , wherein: the searcher information database is configured to store a history of input information and target selections received from the searcher; and the search engine is configured to estimate, with respect to the input information input by the searcher, search tendency information for the at least one instinct based search parameter and a search parameter significance level based on the history of input information, wherein the search engine is also configured to search the search target database by adjusting the input information using the estimation result or by adjusting a matching degree calculation between the input information and the one or more relevant targets. 15. The repetitive fusion search system according to claim 3 , wherein the search engine is configured to learn how the searcher configures search settings during one or more search sessions and to identify a search condition setting with a highest usage frequency, and wherein the search engine is configured to search the search target database based on the search condition setting with the highest usage frequency. | 0.836463 |
7,890,512 | 2 | 3 | 2. The process of claim 1 , wherein, the vector of keyword annotations for each training image serves as a metadata tag for the image, said vector comprising one or more textual keywords, wherein, the keywords are drawn from a prescribed vocabulary of keywords, and each keyword describes a different low-level visual feature in the image. | 2. The process of claim 1 , wherein, the vector of keyword annotations for each training image serves as a metadata tag for the image, said vector comprising one or more textual keywords, wherein, the keywords are drawn from a prescribed vocabulary of keywords, and each keyword describes a different low-level visual feature in the image. 3. The process of claim 2 , wherein the prescribed vocabulary of keywords comprises a Corel keyword database. | 0.621528 |
9,122,318 | 10 | 11 | 10. The system of claim 1 , wherein a target area of each of the keys corresponds to the respective top surface of the key displayed by the input component. | 10. The system of claim 1 , wherein a target area of each of the keys corresponds to the respective top surface of the key displayed by the input component. 11. The system of claim 10 , wherein the processor recognizes a key selection input by the user upon contact within the respective target area of the key on the input component. | 0.5 |
9,928,060 | 8 | 11 | 8. A computer program product for tracking changes in a Javascript object notation structure, the computer program product comprising: one or more computer readable storage media and program instructions stored on the one or more computer readable storage media, the program instructions comprising: program instructions to adjust a Javascript object notation structure to comprise a tag on at least one object and a tag on at least one array; program instructions to receive data indicating a first set of at least one change to the Javascript object notation structure; program instructions to adjust the tags in the Javascript object notation structure to include the first set of the at least one change in the Javascript object notation structure; program instructions to receive data indicating the first set of the at least one change to the Javascript object notation structure is complete; and program instructions to display the first set of the at least one change to the Javascript object notation structure based upon the adjusted tags. | 8. A computer program product for tracking changes in a Javascript object notation structure, the computer program product comprising: one or more computer readable storage media and program instructions stored on the one or more computer readable storage media, the program instructions comprising: program instructions to adjust a Javascript object notation structure to comprise a tag on at least one object and a tag on at least one array; program instructions to receive data indicating a first set of at least one change to the Javascript object notation structure; program instructions to adjust the tags in the Javascript object notation structure to include the first set of the at least one change in the Javascript object notation structure; program instructions to receive data indicating the first set of the at least one change to the Javascript object notation structure is complete; and program instructions to display the first set of the at least one change to the Javascript object notation structure based upon the adjusted tags. 11. The computer program product of claim 8 , wherein program instructions to receive data indicating a first set of at least one change to the Javascript object notation structure comprises program instructions to: receive at least one change to more than one objects; and receive at least one change to more than one arrays. | 0.722317 |
7,965,405 | 7 | 10 | 7. An image forming method using an image forming apparatus, the method comprising the steps of: discriminating, by the language format discriminating section, a description language format of electronic data; and changing, by the image forming apparatus, a threshold value based on the discriminated description language format, there being a common storage area wherein a plurality of areas coexist in accordance with the threshold value, the plurality of areas being used to generate and store a plurality of pieces of data that have different formats based on the electronic data. | 7. An image forming method using an image forming apparatus, the method comprising the steps of: discriminating, by the language format discriminating section, a description language format of electronic data; and changing, by the image forming apparatus, a threshold value based on the discriminated description language format, there being a common storage area wherein a plurality of areas coexist in accordance with the threshold value, the plurality of areas being used to generate and store a plurality of pieces of data that have different formats based on the electronic data. 10. The image forming method of claim 7 , wherein in the changing step, the threshold value is changed in a case where there is an external storing section which is used to generate data other than data that makes an image be formed on a recording medium among the plurality of pieces of data in different formats, the external storage area having an area to store the generated data. | 0.837563 |
8,370,279 | 10 | 17 | 10. A computer storage medium encoded with computer program instructions that when executed by one or more computers cause the one or more computers to perform operations comprising: receiving initial training data, the initial training data comprising initial training records, each initial training record identifying input data as input and a category as output; generating a trained predictive model using the a first subset of initial training data and a training function; generating intermediate training records including inputting input data of a second subset of the initial training records to the trained predictive model, each intermediate training record having a score; and generating a score normalization model using a score normalization training function and the intermediate training records. | 10. A computer storage medium encoded with computer program instructions that when executed by one or more computers cause the one or more computers to perform operations comprising: receiving initial training data, the initial training data comprising initial training records, each initial training record identifying input data as input and a category as output; generating a trained predictive model using the a first subset of initial training data and a training function; generating intermediate training records including inputting input data of a second subset of the initial training records to the trained predictive model, each intermediate training record having a score; and generating a score normalization model using a score normalization training function and the intermediate training records. 17. The medium of claim 10 , further comprising instructions that when executed by one or more computers cause the one or more computers to perform operations comprising: receiving a predictive request from a client device, the predictive request including input data; generating an intermediate output by inputting the input data to the first trained predictive model; generating a predictive output by providing the intermediate output to the score normalization model; and providing the predictive output to the client device. | 0.604634 |
9,560,152 | 11 | 12 | 11. A computer program product for generating a summary of an online communication session, the computer program product comprising: one or more computer readable storage medium(s) and program instructions stored on the one or more computer readable storage medium(s), the program instructions comprising: program instructions to receive a particular topic and summary information for each of one or more episodes of a current online communication session, and information regarding topics of interests for each member of a group of users, wherein content of the current online communication session is generated by members of the group of users; program instructions to receive a pictorial representation of the particular topic for the one or more episodes of the current online communication session, wherein determining the pictorial representation of each episode of the one or more episodes of the current online communication session is based on the particular topic of each episode; and program instructions to generate a summary of the current online communication session that is personalized for members of the group of users, wherein the program instructions to generate the summary that is personalized includes: program instructions to determine a level of interest in a first topic of a first episode and a second topic of a second episode, of the one or more episodes of the current online communication session, for a member of the group of users, based on one or a combination of: an amount of participation and contribution by the member of the group of users during the current online communication session, an amount of participation and contribution by the member of the group of users during previous online communication sessions that include the first topic, and interest preferences input by the member of the group of users; program instructions to list the summary and pictorial representation of the one or more episodes of the current online communication session in an order that corresponds to the level of interest in a first topic of a first episode and a second topic of a second episode, of the one or more episodes of the current online communication session held by the member of the group of users; and responsive to determining the level of interest held by the member of the group of users, for the first topic of the first episode of the one or more episodes, to be greater than the level of interest held by the member of the group of users for the second topic of the second episode of the one or more episodes, program instructions to provide a complete summary of the first topic of the first episode positioned before a condensed summary of the second topic of the second episode. | 11. A computer program product for generating a summary of an online communication session, the computer program product comprising: one or more computer readable storage medium(s) and program instructions stored on the one or more computer readable storage medium(s), the program instructions comprising: program instructions to receive a particular topic and summary information for each of one or more episodes of a current online communication session, and information regarding topics of interests for each member of a group of users, wherein content of the current online communication session is generated by members of the group of users; program instructions to receive a pictorial representation of the particular topic for the one or more episodes of the current online communication session, wherein determining the pictorial representation of each episode of the one or more episodes of the current online communication session is based on the particular topic of each episode; and program instructions to generate a summary of the current online communication session that is personalized for members of the group of users, wherein the program instructions to generate the summary that is personalized includes: program instructions to determine a level of interest in a first topic of a first episode and a second topic of a second episode, of the one or more episodes of the current online communication session, for a member of the group of users, based on one or a combination of: an amount of participation and contribution by the member of the group of users during the current online communication session, an amount of participation and contribution by the member of the group of users during previous online communication sessions that include the first topic, and interest preferences input by the member of the group of users; program instructions to list the summary and pictorial representation of the one or more episodes of the current online communication session in an order that corresponds to the level of interest in a first topic of a first episode and a second topic of a second episode, of the one or more episodes of the current online communication session held by the member of the group of users; and responsive to determining the level of interest held by the member of the group of users, for the first topic of the first episode of the one or more episodes, to be greater than the level of interest held by the member of the group of users for the second topic of the second episode of the one or more episodes, program instructions to provide a complete summary of the first topic of the first episode positioned before a condensed summary of the second topic of the second episode. 12. The computer program product of claim 11 , wherein the summary of the one or more episodes of the current online communication session that is personalized, is tailored for each member of the group of users, based on information regarding topics of interest for each member of the group of users as determined by an analysis of participation and contribution of each member of the group of users, performed by a cognitive engine, during the current online communication session, and during previous online communication sessions of the group of users, and participation and contribution of members of the group of users during previous online communication sessions with other groups. | 0.5 |
8,983,958 | 12 | 15 | 12. A computer implemented method for improving indexing throughput, the computer implemented method comprising: receiving one or more documents for indexing; based on a document type, a document size and a processing priority, categorizing the one or more documents; assigning buckets to the categorized one or more documents according to the document type, the document size and the processing priority; calculating a document type priority for the document type of the one or more documents in the assigned buckets, wherein the document type priority is calculated as a function of an extraction efficiency for extracting content from the one or more documents for the corresponding document type; and based on the calculated document type priority, a bucket type and number of threads available to process the buckets, scheduling the buckets for indexing process. | 12. A computer implemented method for improving indexing throughput, the computer implemented method comprising: receiving one or more documents for indexing; based on a document type, a document size and a processing priority, categorizing the one or more documents; assigning buckets to the categorized one or more documents according to the document type, the document size and the processing priority; calculating a document type priority for the document type of the one or more documents in the assigned buckets, wherein the document type priority is calculated as a function of an extraction efficiency for extracting content from the one or more documents for the corresponding document type; and based on the calculated document type priority, a bucket type and number of threads available to process the buckets, scheduling the buckets for indexing process. 15. The computer implemented method of claim 12 , wherein scheduling the buckets for the indexing process comprises regulating an indexing load on a system for indexing the categorized one or more documents by altering the number of threads available to extract content of the categorized one or more documents. | 0.5 |
8,984,165 | 9 | 17 | 9. A system comprising: a memory; and a processing device coupled to the memory to: wrap text in a markup language file with directives of a web server type page; identify an internal link in the markup language file using regular expression pattern matching, the internal link comprising a markup language extension; and convert the internal link into a web server type page link by replacing the markup language extension with a web server type page extension. | 9. A system comprising: a memory; and a processing device coupled to the memory to: wrap text in a markup language file with directives of a web server type page; identify an internal link in the markup language file using regular expression pattern matching, the internal link comprising a markup language extension; and convert the internal link into a web server type page link by replacing the markup language extension with a web server type page extension. 17. The system of claim 9 , wherein the file is part of a directory tree of markup language files and the processor is to transform the directory tree into multiple web server type page files using recursion. | 0.687688 |
9,317,594 | 1 | 32 | 1. A computer-implemented method for identifying data files that have a common characteristic, the method comprising: receiving a plurality of data files including one or more data files having the common characteristic; generating a list of key terms from the plurality of data files; classifying each data file of the plurality of data files within a hierarchical structure, the hierarchical structure including upper nodes and lower nodes configured to group data files having similar characteristics, wherein a data file is classified within a lower node of the hierarchical structure based on a psychological characteristic of the classified data file, wherein the psychological characteristic indicates a psychological state of the creator of the classified data file; identifying data files from the plurality of data files having an association with a social community, the social community being a homogenous sub-group of a larger population defined by one or more features, wherein the identified data files having the association with the social community are classified within a particular node of the hierarchical structure that is defined by the one or more features; updating the list of key terms based on an analysis of the identified data files; and using the updated list of key terms to identify other data files that have the common characteristic. | 1. A computer-implemented method for identifying data files that have a common characteristic, the method comprising: receiving a plurality of data files including one or more data files having the common characteristic; generating a list of key terms from the plurality of data files; classifying each data file of the plurality of data files within a hierarchical structure, the hierarchical structure including upper nodes and lower nodes configured to group data files having similar characteristics, wherein a data file is classified within a lower node of the hierarchical structure based on a psychological characteristic of the classified data file, wherein the psychological characteristic indicates a psychological state of the creator of the classified data file; identifying data files from the plurality of data files having an association with a social community, the social community being a homogenous sub-group of a larger population defined by one or more features, wherein the identified data files having the association with the social community are classified within a particular node of the hierarchical structure that is defined by the one or more features; updating the list of key terms based on an analysis of the identified data files; and using the updated list of key terms to identify other data files that have the common characteristic. 32. The method of claim 1 , wherein the identified data files are created by members of the social community, and wherein a social context of the social community influences a meaning of vocabulary in the identified data files. | 0.842142 |
9,843,668 | 1 | 3 | 1. A method for determining a number of authorized and unauthorized participants of a telephone conversation, the method comprising: continuously sampling voice data from the telephone conversation; analyzing the sampled voice data using biometric voice identification to identify one or more unique voiceprints; comparing each of the identified one or more unique voiceprints to a first registered voiceprint of a resident of a facility and a second registered voiceprint of a party remote from the facility, wherein the second registered voiceprint was obtained via a remote participant registration process conducted prior to the telephone conversation; identifying the number of authorized and unauthorized participants of the telephone conversation based on the comparison; taking a first predefined action when the number of authorized or unauthorized participants is greater than a first predetermined number; and taking a second predefined action when the number of unauthorized participants is greater than a second predetermined number. | 1. A method for determining a number of authorized and unauthorized participants of a telephone conversation, the method comprising: continuously sampling voice data from the telephone conversation; analyzing the sampled voice data using biometric voice identification to identify one or more unique voiceprints; comparing each of the identified one or more unique voiceprints to a first registered voiceprint of a resident of a facility and a second registered voiceprint of a party remote from the facility, wherein the second registered voiceprint was obtained via a remote participant registration process conducted prior to the telephone conversation; identifying the number of authorized and unauthorized participants of the telephone conversation based on the comparison; taking a first predefined action when the number of authorized or unauthorized participants is greater than a first predetermined number; and taking a second predefined action when the number of unauthorized participants is greater than a second predetermined number. 3. The method of claim 1 , wherein the first predetermined number is two. | 0.940262 |
8,055,674 | 32 | 43 | 32. A computer implemented method of querying a fact repository, performed at a computer system having one or more processors and memory storing one or more programs for execution by the one or more processors, the method comprising: receiving a search query; retrieving at least one fact from the fact repository, the at least one fact corresponding to the received search query and having an attribute and a value, wherein the fact repository includes a plurality of facts associated with objects, wherein a respective fact in the fact repository includes a respective attribute and a respective value, wherein the respective value is a text string, and wherein the plurality of facts are extracted from a plurality of documents; retrieving at least one annotation associated with the at least one fact, the at least one annotation having a value corresponding to the value of the at least one fact, wherein an annotation includes additional information about a fact, and wherein a value of the annotation is indexed to a substring of a fact's value; and sending the attribute and value of the retrieved fact and the retrieved annotation in response to the query. | 32. A computer implemented method of querying a fact repository, performed at a computer system having one or more processors and memory storing one or more programs for execution by the one or more processors, the method comprising: receiving a search query; retrieving at least one fact from the fact repository, the at least one fact corresponding to the received search query and having an attribute and a value, wherein the fact repository includes a plurality of facts associated with objects, wherein a respective fact in the fact repository includes a respective attribute and a respective value, wherein the respective value is a text string, and wherein the plurality of facts are extracted from a plurality of documents; retrieving at least one annotation associated with the at least one fact, the at least one annotation having a value corresponding to the value of the at least one fact, wherein an annotation includes additional information about a fact, and wherein a value of the annotation is indexed to a substring of a fact's value; and sending the attribute and value of the retrieved fact and the retrieved annotation in response to the query. 43. The method of claim 32 , wherein the annotation is a GeoPoint annotation. | 0.78125 |
9,077,933 | 6 | 9 | 6. The method as defined in claim 1 , further comprising weighting the relevance ranking score based on a user profile. | 6. The method as defined in claim 1 , further comprising weighting the relevance ranking score based on a user profile. 9. The method as defined in claim 6 , further comprising monitoring media selections made by a user to form the user profile. | 0.640805 |
8,836,652 | 15 | 21 | 15. A non-transitory computer readable storage medium storing one or more programs for execution by one or more processors of an electronic device with a touch-sensitive surface, the one or more programs including instructions for: receiving a document from a server, the document including an embedded script; rendering and displaying the document at the electronic device and executing the embedded script, including: establishing a touchevent interface object that includes a plurality of touchlists; and upon detecting one or more touches on the touch-sensitive surface: updating the touchevent interface object with touch data, including values in two or more of the touchlists; and further executing the embedded script in accordance with the values in at least one of the two or more touchlists, wherein: the touchevent interface object includes a plurality of distinct touchlists, a respective touchlist including two or more concurrent touches; and updating the touchevent interface object with touch data includes updating values in two or more of the touchlists. | 15. A non-transitory computer readable storage medium storing one or more programs for execution by one or more processors of an electronic device with a touch-sensitive surface, the one or more programs including instructions for: receiving a document from a server, the document including an embedded script; rendering and displaying the document at the electronic device and executing the embedded script, including: establishing a touchevent interface object that includes a plurality of touchlists; and upon detecting one or more touches on the touch-sensitive surface: updating the touchevent interface object with touch data, including values in two or more of the touchlists; and further executing the embedded script in accordance with the values in at least one of the two or more touchlists, wherein: the touchevent interface object includes a plurality of distinct touchlists, a respective touchlist including two or more concurrent touches; and updating the touchevent interface object with touch data includes updating values in two or more of the touchlists. 21. The computer readable storage medium of claim 15 , wherein the one or more programs include instructions for: upon detecting a change in at least one of the one or more touches, updating two or more of the plurality of touchlists. | 0.880368 |
9,600,218 | 2 | 3 | 2. The apparatus of claim 1 wherein: the properties indicate the new state for the document and include a time stamp defining when the new state was reached. | 2. The apparatus of claim 1 wherein: the properties indicate the new state for the document and include a time stamp defining when the new state was reached. 3. The apparatus of claim 2 wherein: the controller is configured to analyze the progress information by identifying a current page of the print job indicated by the progress information, and correlating the current page with a document of the print job. | 0.5 |
9,280,562 | 1 | 4 | 1. A method of ranking multimedia works with respect to a query, comprising: (a) storing, in a memory, information defining a conditional probability framework representing a plurality of concepts represented within at least two different media types of multimedia works comprising semantic concepts and visual feature concepts, based on analysis of at least a subset of the plurality of works; (b) receiving a query comprising information defining at least one of the semantic concepts and the visual feature concepts; (c) automatically determining a set of multimedia works of the at least two different media types corresponding to the at least one of said concepts associated with the query by associating semantic concepts of a first multimedia work with visual feature concepts of a second multimedia work as annotations, using a probabilistic framework comprising a visual feature layer, a semantic layer, and a hidden concept layer which connects the visual feature layer and the semantic layer, with at least one automated processor, wherein conditional probabilities of the visual feature concepts and the annotations given a hidden concept class are determined based on an Expectation-Maximization (EM) based iterative learning procedure; (d) automatically ranking the determined set of multimedia works of the at least two different media types determined to correspond to the at least one of said concepts associated with the query in accordance with a relevance to the query according to a joint probability distribution which models a probability that the query associated with the at least one of said concepts is associated with a respective multimedia work, with the at least one automated processor; and (e) at least one of (i) storing in the memory, and (ii) outputting information selectively dependent on the ranking. | 1. A method of ranking multimedia works with respect to a query, comprising: (a) storing, in a memory, information defining a conditional probability framework representing a plurality of concepts represented within at least two different media types of multimedia works comprising semantic concepts and visual feature concepts, based on analysis of at least a subset of the plurality of works; (b) receiving a query comprising information defining at least one of the semantic concepts and the visual feature concepts; (c) automatically determining a set of multimedia works of the at least two different media types corresponding to the at least one of said concepts associated with the query by associating semantic concepts of a first multimedia work with visual feature concepts of a second multimedia work as annotations, using a probabilistic framework comprising a visual feature layer, a semantic layer, and a hidden concept layer which connects the visual feature layer and the semantic layer, with at least one automated processor, wherein conditional probabilities of the visual feature concepts and the annotations given a hidden concept class are determined based on an Expectation-Maximization (EM) based iterative learning procedure; (d) automatically ranking the determined set of multimedia works of the at least two different media types determined to correspond to the at least one of said concepts associated with the query in accordance with a relevance to the query according to a joint probability distribution which models a probability that the query associated with the at least one of said concepts is associated with a respective multimedia work, with the at least one automated processor; and (e) at least one of (i) storing in the memory, and (ii) outputting information selectively dependent on the ranking. 4. The method according to claim 1 , wherein the query comprises an image, and wherein the image is processed to determine implicit semantic image content characteristics. | 0.885542 |
9,530,405 | 6 | 10 | 6. An intention estimating method of estimating a user's intention from the user's language input, said method comprising: slitting the inputted language into a plurality of morphemes and extracting, from the morphemes, one or more intention estimation units each of which is a unit on which an estimation of said intention is to be performed, each of the intention estimation units consisting of one or more morphemes; estimating a respective partial intention which indicates a respective intention for each of said extracted intention estimation units; calculating an intention co-occurrence weight having a value which depends on a relationship between said estimated partial intentions; and generating an intention sequence corresponding to said inputted language by using said estimated one or more said partial intentions, and generating an intention estimation result corresponding to said inputted language by using both a score showing a likelihood of said generated intention sequence and the intention co-occurrence weight calculated for said partial intentions which construct said generated intention sequence. | 6. An intention estimating method of estimating a user's intention from the user's language input, said method comprising: slitting the inputted language into a plurality of morphemes and extracting, from the morphemes, one or more intention estimation units each of which is a unit on which an estimation of said intention is to be performed, each of the intention estimation units consisting of one or more morphemes; estimating a respective partial intention which indicates a respective intention for each of said extracted intention estimation units; calculating an intention co-occurrence weight having a value which depends on a relationship between said estimated partial intentions; and generating an intention sequence corresponding to said inputted language by using said estimated one or more said partial intentions, and generating an intention estimation result corresponding to said inputted language by using both a score showing a likelihood of said generated intention sequence and the intention co-occurrence weight calculated for said partial intentions which construct said generated intention sequence. 10. The intention estimating method according to claim 6 , further comprising: an intention sequence conversion table that holds an intention sequence conversion rule for converting said partial intentions according to a relationship between a contiguous sequence of said partial intentions which construct said intention sequence; and converting said partial intention having a relationship matching the intention sequence conversion rule described in said intention sequence conversion table, among the contiguous sequence of said partial intentions which construct said generated intention sequence, according to said intention sequence conversion rule. | 0.5 |
8,560,935 | 8 | 10 | 8. The system of claim 7 , further comprising: the master field component segregates the set of questions and the respective set of fill-in fields into a third subset of questions targeted to a third user; the user segregation manager component enables the following: the third user to review at least one answer stored in the master field list from at least one of the first user, the second user, or the third user; and the third user to transmit an answer to at least one question within the third subset of questions independent of the first user and the first subset of questions, the second user and the second subset of questions, the answer is stored to the master field list; the user segregation manager component employs secure data communication between the third user and the master field list such that information communicated by the third user and the master field list is isolated from the first user and the second user; and the form aggregation component that leverages the master field list to collect information received from the first user related to the first subset of questions, information received from the second user related to the second subset of questions, and information received from the third user related to the third subset of questions to update the set of questions and the respective set of fill-in fields on the electronic document. | 8. The system of claim 7 , further comprising: the master field component segregates the set of questions and the respective set of fill-in fields into a third subset of questions targeted to a third user; the user segregation manager component enables the following: the third user to review at least one answer stored in the master field list from at least one of the first user, the second user, or the third user; and the third user to transmit an answer to at least one question within the third subset of questions independent of the first user and the first subset of questions, the second user and the second subset of questions, the answer is stored to the master field list; the user segregation manager component employs secure data communication between the third user and the master field list such that information communicated by the third user and the master field list is isolated from the first user and the second user; and the form aggregation component that leverages the master field list to collect information received from the first user related to the first subset of questions, information received from the second user related to the second subset of questions, and information received from the third user related to the third subset of questions to update the set of questions and the respective set of fill-in fields on the electronic document. 10. The system of claim 8 , the first user is at least one of a doctor or a dentist, the second user is at least one of a doctor or a dentist, and the third user is an administrator for at least one of a medical practice or a dentistry practice. | 0.5 |
10,025,861 | 1 | 2 | 1. A method for sending a customized news stream to be displayed on a user device, the method comprising: identifying, by one or more servers, dwell times spent by a plurality of users while accessing a plurality of items, each item belonging to one media type from a plurality of media types, the plurality of media types having varying average dwell times, wherein the dwell time of an item is based on an amount of time that the item is displayed to each user that views the item; for each media type, determining, by the one or more servers, statistical parameters for the each media type based on the items belonging to the each media type, wherein determining the statistical parameters for each media type includes, calculating an average dwell time for items of the each media type, and calculating a standard deviation of the dwell times for items of the each media type; obtaining, at the one or more servers, a normalized dwell time for each item, the normalized dwell time of an item based upon a quotient of a difference between the dwell time of the item and the average dwell time for items of the media type, and the standard deviation of the dwell times for items of the media type, wherein normalized dwell times are a standardized measurement of the dwell times that enable ranking of items of different media types; determining, at the one or more servers, a priority for each item of the plurality of items based on the normalized dwell time of each item and a profile of a user; and sending, from the one or more servers, a news stream to the user device for presentation on a display associated with the user device, the news stream configured to be presented on the display associated with the user device in an order based on the priority of the items of the plurality of items, wherein the news stream includes items of two or more different media types, wherein operations of the method are executed by a processor. | 1. A method for sending a customized news stream to be displayed on a user device, the method comprising: identifying, by one or more servers, dwell times spent by a plurality of users while accessing a plurality of items, each item belonging to one media type from a plurality of media types, the plurality of media types having varying average dwell times, wherein the dwell time of an item is based on an amount of time that the item is displayed to each user that views the item; for each media type, determining, by the one or more servers, statistical parameters for the each media type based on the items belonging to the each media type, wherein determining the statistical parameters for each media type includes, calculating an average dwell time for items of the each media type, and calculating a standard deviation of the dwell times for items of the each media type; obtaining, at the one or more servers, a normalized dwell time for each item, the normalized dwell time of an item based upon a quotient of a difference between the dwell time of the item and the average dwell time for items of the media type, and the standard deviation of the dwell times for items of the media type, wherein normalized dwell times are a standardized measurement of the dwell times that enable ranking of items of different media types; determining, at the one or more servers, a priority for each item of the plurality of items based on the normalized dwell time of each item and a profile of a user; and sending, from the one or more servers, a news stream to the user device for presentation on a display associated with the user device, the news stream configured to be presented on the display associated with the user device in an order based on the priority of the items of the plurality of items, wherein the news stream includes items of two or more different media types, wherein operations of the method are executed by a processor. 2. The method as recited in claim 1 , wherein determining the priority further includes: determining a first priority value for each item based on features within the item determined to be of interest to the user. | 0.714477 |
8,380,503 | 1 | 15 | 1. A method embodied in a computer readable medium for generating challenge data to be used for accessing data and/or resources of an electronic computing system comprising: (a) automatically generating a candidate challenge sentence from a first set of words and phrases using a computing system; (b) automatically generating at least one first utterance from a first machine text to speech system of said candidate challenge sentence using the computing system, said at least one first utterance including first acoustical characteristics; (c) acquiring at least one second utterance of said candidate challenge sentence known to be from a human speaker using the computing system, said at least one second utterance including second acoustical characteristics; (d) automatically determining a difference in said first and second acoustical characteristics using the computing system to determine a challenge sentence acoustic score for said candidate challenge sentence; (e) automatically selecting said candidate challenge sentence as a suitable challenge item in an utterance-based challenge system and storing said at least one second utterance and said candidate challenge sentence in a challenge item database using the computer system when said candidate challenge sentence acoustic score exceeds a target threshold, indicating that said at least one first utterance from the first machine text to speech system is sufficiently different from said at least one second utterance from the human speaker for said candidate challenge sentence to be used to automatically distinguish between humans and machines attempting to access the electronic computing system. | 1. A method embodied in a computer readable medium for generating challenge data to be used for accessing data and/or resources of an electronic computing system comprising: (a) automatically generating a candidate challenge sentence from a first set of words and phrases using a computing system; (b) automatically generating at least one first utterance from a first machine text to speech system of said candidate challenge sentence using the computing system, said at least one first utterance including first acoustical characteristics; (c) acquiring at least one second utterance of said candidate challenge sentence known to be from a human speaker using the computing system, said at least one second utterance including second acoustical characteristics; (d) automatically determining a difference in said first and second acoustical characteristics using the computing system to determine a challenge sentence acoustic score for said candidate challenge sentence; (e) automatically selecting said candidate challenge sentence as a suitable challenge item in an utterance-based challenge system and storing said at least one second utterance and said candidate challenge sentence in a challenge item database using the computer system when said candidate challenge sentence acoustic score exceeds a target threshold, indicating that said at least one first utterance from the first machine text to speech system is sufficiently different from said at least one second utterance from the human speaker for said candidate challenge sentence to be used to automatically distinguish between humans and machines attempting to access the electronic computing system. 15. The method of claim 1 wherein a time required by the human speaker to generate said first utterance is measured. | 0.828402 |
9,978,182 | 1 | 5 | 1. A method of operating a virtual image generation system, the method comprising: allowing an end user to visualize an object of interest in a three-dimensional scene; spatially associating a text region within a field of view of the user, wherein the text region is spatially associated with the object of interest; generating a gesture reference associated with the object of interest; generating a textual message that identifies at least one characteristic of the object of interest; streaming the textual message within the text region; sensing gestural commands from the end user by detecting an angular position of an anatomical part of the end user relative to a plurality of different regions of the gesture reference; and controlling the streaming of the textual message in response to the sensed gestural commands, wherein the gesture reference is an annular ring surrounding the object of interest, and wherein a first side of the annular ring forms one of the different regions, and a second side of the annular ring opposite of the first side of the annular ring forms another one of the different regions. | 1. A method of operating a virtual image generation system, the method comprising: allowing an end user to visualize an object of interest in a three-dimensional scene; spatially associating a text region within a field of view of the user, wherein the text region is spatially associated with the object of interest; generating a gesture reference associated with the object of interest; generating a textual message that identifies at least one characteristic of the object of interest; streaming the textual message within the text region; sensing gestural commands from the end user by detecting an angular position of an anatomical part of the end user relative to a plurality of different regions of the gesture reference; and controlling the streaming of the textual message in response to the sensed gestural commands, wherein the gesture reference is an annular ring surrounding the object of interest, and wherein a first side of the annular ring forms one of the different regions, and a second side of the annular ring opposite of the first side of the annular ring forms another one of the different regions. 5. The method of claim 1 , wherein the gesture reference is separate and distinct from the object of interest. | 0.876957 |
8,996,371 | 9 | 12 | 9. A system for adapting a language model to a specific environment, the system comprising: a memory; a recorder to capture a plurality of interactions in the specific environment; and a computer: to retrieve a plurality of external documents based on a plurality of query expressions, wherein query expressions are generated by applying a topic detection algorithm on textual transcripts in order to detect the different topics discussed in the plurality of interactions, to generate an external corpus by selecting a plurality of documents from the documents retrieved, to detect in the external corpus terms related to the environment that are not included in an initial language model, and to adapt the initial language model to include a plurality of the terms detected. | 9. A system for adapting a language model to a specific environment, the system comprising: a memory; a recorder to capture a plurality of interactions in the specific environment; and a computer: to retrieve a plurality of external documents based on a plurality of query expressions, wherein query expressions are generated by applying a topic detection algorithm on textual transcripts in order to detect the different topics discussed in the plurality of interactions, to generate an external corpus by selecting a plurality of documents from the documents retrieved, to detect in the external corpus terms related to the environment that are not included in an initial language model, and to adapt the initial language model to include a plurality of the terms detected. 12. The system of claim 9 , wherein the computer is to detect the terms related to the environment by performing topic modeling on a corpora comprising the external corpus. | 0.650407 |
9,514,113 | 8 | 9 | 8. A hardware computer-readable storage medium including program instructions executable by one or more processors that, when executed, cause the one or more processors to perform operations, the operations comprising: accessing, at one or more computing devices, a document; identifying a plurality of sentences of the document, each sentence identified based on punctuation in the document; and executing, for each of the sentences of the document and using the one or more computing devices, a document modification operation that includes: generating a ranking score for each of a plurality of passages from external documents, wherein the ranking score is based at least on a degree of semantic similarity of each of the plurality of passages from the external documents with respect to the sentence of the document, modifying the sentence to include a footnote link for the sentence in the document, the footnote link including a link to the external document having a highest ranked passage therein if the ranking score of the highest ranked passage with respect to the sentence exceeds a threshold value, and skipping modification of the sentence if the ranking score of the highest ranked passage with respect to the sentence does not exceed the threshold value. | 8. A hardware computer-readable storage medium including program instructions executable by one or more processors that, when executed, cause the one or more processors to perform operations, the operations comprising: accessing, at one or more computing devices, a document; identifying a plurality of sentences of the document, each sentence identified based on punctuation in the document; and executing, for each of the sentences of the document and using the one or more computing devices, a document modification operation that includes: generating a ranking score for each of a plurality of passages from external documents, wherein the ranking score is based at least on a degree of semantic similarity of each of the plurality of passages from the external documents with respect to the sentence of the document, modifying the sentence to include a footnote link for the sentence in the document, the footnote link including a link to the external document having a highest ranked passage therein if the ranking score of the highest ranked passage with respect to the sentence exceeds a threshold value, and skipping modification of the sentence if the ranking score of the highest ranked passage with respect to the sentence does not exceed the threshold value. 9. The hardware computer-readable storage medium of claim 8 , wherein the degree of semantic similarity of each of the plurality of passages from the external documents with respect to the sentence of the document is based on the entirety of the sentence. | 0.747024 |
7,957,968 | 12 | 18 | 12. A computer based system for automatically generating a hierarchical grammar associated with a plurality of tasks comprising: creation means for creating a sub-grammar for each of the plurality of tasks, wherein the creation means comprises: receiving means for receiving data representing the task based from responses received from a distributed database; tagging means for automatically tagging the data into parts of speech to form tagged data; identification means for identifying filler words and core words from said tagged data by applying rules to differentiate between filler words and core words; modeling means for automatically modeling sentence structure based upon said tagged data using a set of modeling rules; synonym means for automatically identifying synonyms of said core words; and grammar means for automatically creating a sub-grammar using the modeled sentence structure, the tagged data, the identified synonyms, and the identified core words; and creation means for creating a high-level grammar by combining filler words identified in the creation of the sub-grammars. | 12. A computer based system for automatically generating a hierarchical grammar associated with a plurality of tasks comprising: creation means for creating a sub-grammar for each of the plurality of tasks, wherein the creation means comprises: receiving means for receiving data representing the task based from responses received from a distributed database; tagging means for automatically tagging the data into parts of speech to form tagged data; identification means for identifying filler words and core words from said tagged data by applying rules to differentiate between filler words and core words; modeling means for automatically modeling sentence structure based upon said tagged data using a set of modeling rules; synonym means for automatically identifying synonyms of said core words; and grammar means for automatically creating a sub-grammar using the modeled sentence structure, the tagged data, the identified synonyms, and the identified core words; and creation means for creating a high-level grammar by combining filler words identified in the creation of the sub-grammars. 18. The system of claim 12 , wherein said parts of speech include verbs and nouns. | 0.682171 |
9,823,904 | 1 | 4 | 1. A computer program product, comprising: A non-transitory computer-readable storage medium having computer readable program code embodied therewith, the computer readable program code configured to perform an operation for managing assertions, the operation comprising: receiving, from a user interacting with an integrated development environment (IDE) tool, a request to add an assertion at a specified location within source code of an application, wherein the source code is stored in a first file associated with a development project and wherein the first file does not include information about the assertion; receiving a definition for the assertion, wherein the definition includes one or more lines of source code, an expiration time, and a number of times to insert the assertion into the source code, when compiling the application from the source code; storing the definition for the assertion in a second file associated with the development project; creating an association in the development project between the source code of the application and the assertion, wherein the association is not stored in the first file and indicates at least a line in the first file at which to insert the assertion prior to compiling the source code; receiving a request to compile the source code of the application; modifying the source code stored in the first file by inserting the one or more lines of source code included in the assertion definition at the specified location in the source code; and compiling the application from the source code in the first file. | 1. A computer program product, comprising: A non-transitory computer-readable storage medium having computer readable program code embodied therewith, the computer readable program code configured to perform an operation for managing assertions, the operation comprising: receiving, from a user interacting with an integrated development environment (IDE) tool, a request to add an assertion at a specified location within source code of an application, wherein the source code is stored in a first file associated with a development project and wherein the first file does not include information about the assertion; receiving a definition for the assertion, wherein the definition includes one or more lines of source code, an expiration time, and a number of times to insert the assertion into the source code, when compiling the application from the source code; storing the definition for the assertion in a second file associated with the development project; creating an association in the development project between the source code of the application and the assertion, wherein the association is not stored in the first file and indicates at least a line in the first file at which to insert the assertion prior to compiling the source code; receiving a request to compile the source code of the application; modifying the source code stored in the first file by inserting the one or more lines of source code included in the assertion definition at the specified location in the source code; and compiling the application from the source code in the first file. 4. The computer program product of claim 1 , wherein the operations further comprise: receiving a request to edit the source code stored in the first file; identifying the specified location in the source code of the assertion based on the association between the source code of the application and the assertion; and presenting an indication of the assertion in an editing pane of the IDE tool at the specified location. | 0.529083 |
9,442,828 | 1 | 8 | 1. A method of generating a test script from a pre-existing script for testing a graphical user interface (GUI) comprising: parsing a pre-existing test script for a graphical user interface (GUI) to identify, using keywords and associated parameters, a first GUI action in a first line of the pre-existing test script; parsing a model associated with the GUI to identify GUI actions and associated elements for the GUI actions in the model; identifying a corresponding element for the first GUI action in a second line, following the first line, that are identified by keywords and associated parameters that exist in the pre-existing test script and match actions in the model; identifying GUI actions in the pre-existing test script that match GUI actions in the model; and generating a new test script by adding the first GUI action and corresponding element to the pre-existing test script. | 1. A method of generating a test script from a pre-existing script for testing a graphical user interface (GUI) comprising: parsing a pre-existing test script for a graphical user interface (GUI) to identify, using keywords and associated parameters, a first GUI action in a first line of the pre-existing test script; parsing a model associated with the GUI to identify GUI actions and associated elements for the GUI actions in the model; identifying a corresponding element for the first GUI action in a second line, following the first line, that are identified by keywords and associated parameters that exist in the pre-existing test script and match actions in the model; identifying GUI actions in the pre-existing test script that match GUI actions in the model; and generating a new test script by adding the first GUI action and corresponding element to the pre-existing test script. 8. The method of claim 1 , wherein after a generated test script is produced, any successful decisions from the candidate list or any input that the user was required to solve are added to a global set of βkeyword <->elementβ pairs for future test script parsing so that the process is refined over time. | 0.5 |
8,818,801 | 1 | 2 | 1. A dialogue speech recognition system comprising: a speech recognition unit, implemented by at least one central processing unit (CPU), that receives a speech signal of each speaker in a dialog among a plurality of speakers and turn information indicating whether a speaker having generated the speech signal has turn to speak or indicating a probability that the speaker has turn to speak and performs speech recognition for the speech signal, wherein the speech recognition unit at least includes: an acoustic likelihood computation unit that provides a likelihood of occurrence of an input speech signal from a given phoneme sequence; a linguistic likelihood computation unit that provides a likelihood of occurrence of a given word sequence; and a maximum likelihood candidate search unit that provides a word sequence with a maximum likelihood of occurrence from a speech signal by using the likelihoods provided by the acoustic likelihood computation nit and the linguistic likelihood computation unit, and the linguistic likelihood computation unit provides different linguistic likelihoods when a speaker having generated a speech signal input to the speech recognition unit has the turn to speak and when not. | 1. A dialogue speech recognition system comprising: a speech recognition unit, implemented by at least one central processing unit (CPU), that receives a speech signal of each speaker in a dialog among a plurality of speakers and turn information indicating whether a speaker having generated the speech signal has turn to speak or indicating a probability that the speaker has turn to speak and performs speech recognition for the speech signal, wherein the speech recognition unit at least includes: an acoustic likelihood computation unit that provides a likelihood of occurrence of an input speech signal from a given phoneme sequence; a linguistic likelihood computation unit that provides a likelihood of occurrence of a given word sequence; and a maximum likelihood candidate search unit that provides a word sequence with a maximum likelihood of occurrence from a speech signal by using the likelihoods provided by the acoustic likelihood computation nit and the linguistic likelihood computation unit, and the linguistic likelihood computation unit provides different linguistic likelihoods when a speaker having generated a speech signal input to the speech recognition unit has the turn to speak and when not. 2. The dialogue speech recognition system according to claim 1 , wherein the linguistic likelihood computation unit includes: a first linguistic likelihood identification unit that identifies a likelihood from a first linguistic model indicating a linguistic likelihood when a speaker having generated a speech signal has the turn to speak; and a second linguistic likelihood identification unit that identifies a likelihood from a second linguistic model indicating a linguistic likelihood when a speaker having generated a speech signal does not have the turn to speak, and the maximum likelihood candidate search unit acquires a candidate for a speech recognition result by using at least one of a linguistic likelihood identified by the first linguistic likelihood identification unit and a linguistic likelihood identified by the second linguistic likelihood identification unit according to the turn information. | 0.5 |
10,065,104 | 10 | 11 | 10. An apparatus having at least one processor in connection or communication with at least one memory and configured to: receive at a receiver of the apparatus information of at least one word spelled by one or more users on one or more respective game boards of one or more respective displays; determine for each word which is received, if the respective word is of a first type or of a second type, the plurality of words of the second type being categorised as more common than the plurality of words of the first type; and selectively update at least one dictionary stored in the at least one memory in response to the determining and satisfying of at least one condition, said at least one dictionary comprising the words of the first type and the words of the second type, wherein said at least one dictionary is selectively updated to change a status of a received word of the first type to be a word of the second type. | 10. An apparatus having at least one processor in connection or communication with at least one memory and configured to: receive at a receiver of the apparatus information of at least one word spelled by one or more users on one or more respective game boards of one or more respective displays; determine for each word which is received, if the respective word is of a first type or of a second type, the plurality of words of the second type being categorised as more common than the plurality of words of the first type; and selectively update at least one dictionary stored in the at least one memory in response to the determining and satisfying of at least one condition, said at least one dictionary comprising the words of the first type and the words of the second type, wherein said at least one dictionary is selectively updated to change a status of a received word of the first type to be a word of the second type. 11. An apparatus as set forth in claim 10 , wherein the plurality of words of the first type are stored in a first dictionary, and the plurality of words of the second type are stored in a second dictionary. | 0.5 |
8,074,211 | 1 | 7 | 1. A grouping method for threads, each thread comprising basic modules and data, the thread being assigned to processors in a program for a multiprocessor system, the program comprising the basic modules and a parallel statement describing relationships between parallel threads for the basic modules, the grouping method executed in the multiprocessor system comprising: displaying a dataflow graph visually showing a parallel relationship between the data and the basic modules based on the parallel statement when the program is executed; selecting a basic module group for grouping a basic module indicated by a node of the dataflow graph and a priority for grouping in accordance with a user input; changing graph data structure generation information for displaying the dataflow graph in accordance with a selection of the basic module group; and making the basic module group to be reflected in the parallel statement in accordance with the changed graph data structure generation information, wherein the dataflow graph comprises data entries, nodes in the basic modules, and edges configured to connect the data entries and the nodes; wherein at least said displaying is implemented by computer hardware. | 1. A grouping method for threads, each thread comprising basic modules and data, the thread being assigned to processors in a program for a multiprocessor system, the program comprising the basic modules and a parallel statement describing relationships between parallel threads for the basic modules, the grouping method executed in the multiprocessor system comprising: displaying a dataflow graph visually showing a parallel relationship between the data and the basic modules based on the parallel statement when the program is executed; selecting a basic module group for grouping a basic module indicated by a node of the dataflow graph and a priority for grouping in accordance with a user input; changing graph data structure generation information for displaying the dataflow graph in accordance with a selection of the basic module group; and making the basic module group to be reflected in the parallel statement in accordance with the changed graph data structure generation information, wherein the dataflow graph comprises data entries, nodes in the basic modules, and edges configured to connect the data entries and the nodes; wherein at least said displaying is implemented by computer hardware. 7. The method of claim 1 , wherein selecting the basic module group comprises selecting basic modules related to different entries in a same data arrangement or basic modules related to different data arrangements as a group. | 0.5 |
9,881,077 | 1 | 7 | 1. A computer-implemented method comprising: aggregating news documents from one or more news sources; grouping the news documents into a plurality of news collections, each of the plurality of news collections including a sub-set of the news documents having related content; determining objects described by the plurality of news collections, the objects collectively forming a set of objects; determining an overall relevance of each of the plurality of news collections by determining a number of news sources in each of the plurality of news collections reporting on a related topic; determining a level of interest in the objects described by the plurality of news collections by determining a number of other news collections mentioning the objects and a number of search queries searching for information about the objects during a predetermined timeframe; determining a significance of the objects in the plurality of news collections by determining a number of times that the objects appear in titles of the news documents of the plurality of news collections, a centrality of the objects in the news documents of the plurality of news collections, the centrality of the objects being based on where the objects are mentioned in a body of the news documents, and a pertinence of events described by the plurality of news collections involving the objects; determining a relevance of each of the plurality of news collections with respect to the objects respectively described by the plurality of news collections, the relevance being based on the overall relevance of each of the plurality of news collections, the level of interest in the objects described by the plurality of news collections, and the significance of the objects in the plurality of news collections; and determining one or more news collections from the plurality of news collections to be associated with a first object included in the set of objects based on the relevance of the one or more news collections to the first object. | 1. A computer-implemented method comprising: aggregating news documents from one or more news sources; grouping the news documents into a plurality of news collections, each of the plurality of news collections including a sub-set of the news documents having related content; determining objects described by the plurality of news collections, the objects collectively forming a set of objects; determining an overall relevance of each of the plurality of news collections by determining a number of news sources in each of the plurality of news collections reporting on a related topic; determining a level of interest in the objects described by the plurality of news collections by determining a number of other news collections mentioning the objects and a number of search queries searching for information about the objects during a predetermined timeframe; determining a significance of the objects in the plurality of news collections by determining a number of times that the objects appear in titles of the news documents of the plurality of news collections, a centrality of the objects in the news documents of the plurality of news collections, the centrality of the objects being based on where the objects are mentioned in a body of the news documents, and a pertinence of events described by the plurality of news collections involving the objects; determining a relevance of each of the plurality of news collections with respect to the objects respectively described by the plurality of news collections, the relevance being based on the overall relevance of each of the plurality of news collections, the level of interest in the objects described by the plurality of news collections, and the significance of the objects in the plurality of news collections; and determining one or more news collections from the plurality of news collections to be associated with a first object included in the set of objects based on the relevance of the one or more news collections to the first object. 7. The computer-implemented method of claim 1 , wherein determining the overall relevance of each of the plurality of news collections includes determining a level of user interest in each of the plurality of news collections, and determining the level of interest in the objects respectively described by the plurality of news collections includes determining a trend for each of the objects on one or more social networks during the predetermined timeframe. | 0.542829 |
7,769,234 | 3 | 4 | 3. The computer-readable medium according to claim 2 , wherein the extracting is adapted to select a graphic that makes the element parameter to be found within the corresponding parameter range from the document image and consolidate a plurality of ruled line elements that make the consolidation parameter to be found within the corresponding parameter range to generate the ruled line candidate. | 3. The computer-readable medium according to claim 2 , wherein the extracting is adapted to select a graphic that makes the element parameter to be found within the corresponding parameter range from the document image and consolidate a plurality of ruled line elements that make the consolidation parameter to be found within the corresponding parameter range to generate the ruled line candidate. 4. The computer-readable medium according to claim 3 , wherein the ruled line is a broken ruled line; the parameter range of the element parameter is defined by a threshold value for sizes of circumscribed rectangles of the black pixel link components; and the extracting is adapted to extract the black pixel link components from the document image expressed by a binary system and select the black pixel link components that make the element parameter to be found within the corresponding parameter range as ruled line elements. | 0.560531 |
9,641,681 | 8 | 9 | 8. The method of claim 2 , wherein the one or more metric values include a value of the concern-addressing metric for the conversation, and wherein the value of the concern-addressing metric indicates an extent to which the first participant addresses one or more concerns of the second participant. | 8. The method of claim 2 , wherein the one or more metric values include a value of the concern-addressing metric for the conversation, and wherein the value of the concern-addressing metric indicates an extent to which the first participant addresses one or more concerns of the second participant. 9. The method of claim 8 , further comprising determining the value of the concern-addressing metric based, at least in part, on a tone of speech of the second participant and/or on words used in the communication of the second participant. | 0.5 |
7,620,624 | 5 | 6 | 5. A method according to claim 3 , wherein assigning includes enforcing the uniqueness of a timestamp when it is generated by resolving the conflict with another pre- existing time stamp. | 5. A method according to claim 3 , wherein assigning includes enforcing the uniqueness of a timestamp when it is generated by resolving the conflict with another pre- existing time stamp. 6. A method according to claim 5 , wherein said enforcing includes modifying at least one bit of the timestamp. | 0.856218 |
7,739,658 | 45 | 46 | 45. A method of responding to a request message sent from a remote user device for web page information by generating web page code capable of being interpreted by a browser within the remote user device for displaying one or more web pages and for outputting a response message comprising the web page code, the method comprising: extracting from the request message information determining a device type identifier identifying the remote user device which sent the request message as being one of a set of possible remote user device types having different capabilities of processing and displaying web page code; operating a code generating engine to generate the web page code; storing the web page information in a first memory means as a content document comprising a set of instructions authored in a script language for generating the web page code; and storing device dependent information for each of the set of different remote user device types in a second memory means; wherein the code generating engine interprets the instructions with reference to selected device dependent information corresponding to the device type identifier of the remote user device which sent the request message, the code generating engine thereby generating the web page code in a form in which the web page code is tailored to the capabilities of the remote user device for processing and displaying web page code; wherein the content document comprises at least one component name identifying a respective data component, and wherein the method comprises accessing a data structure in which at least one data component exists as a set of data objects defining multiple versions of the data component where the data objects have different data properties suited to the different capabilities of processing and displaying web page code of the different remote user devices, and further comprising selecting a data object from the set of data objects identified by a component name for inclusion in the web page code on the basis of the device type identifier; wherein the selecting step comprises looking up a predetermined selection of data object using a component policy table in a case where selection of the version of data component for the remote user device is predetermined by an author of the content document, and wherein the selecting step further comprises determining technical attributes of the remote user device and selecting the data object by comparing the technical attributes with data properties of each data object in a case where no version of the data component for the remote user device has been predetermined by the author of the content document, wherein the technical attributes of the remote user device are defined in a device policy table. | 45. A method of responding to a request message sent from a remote user device for web page information by generating web page code capable of being interpreted by a browser within the remote user device for displaying one or more web pages and for outputting a response message comprising the web page code, the method comprising: extracting from the request message information determining a device type identifier identifying the remote user device which sent the request message as being one of a set of possible remote user device types having different capabilities of processing and displaying web page code; operating a code generating engine to generate the web page code; storing the web page information in a first memory means as a content document comprising a set of instructions authored in a script language for generating the web page code; and storing device dependent information for each of the set of different remote user device types in a second memory means; wherein the code generating engine interprets the instructions with reference to selected device dependent information corresponding to the device type identifier of the remote user device which sent the request message, the code generating engine thereby generating the web page code in a form in which the web page code is tailored to the capabilities of the remote user device for processing and displaying web page code; wherein the content document comprises at least one component name identifying a respective data component, and wherein the method comprises accessing a data structure in which at least one data component exists as a set of data objects defining multiple versions of the data component where the data objects have different data properties suited to the different capabilities of processing and displaying web page code of the different remote user devices, and further comprising selecting a data object from the set of data objects identified by a component name for inclusion in the web page code on the basis of the device type identifier; wherein the selecting step comprises looking up a predetermined selection of data object using a component policy table in a case where selection of the version of data component for the remote user device is predetermined by an author of the content document, and wherein the selecting step further comprises determining technical attributes of the remote user device and selecting the data object by comparing the technical attributes with data properties of each data object in a case where no version of the data component for the remote user device has been predetermined by the author of the content document, wherein the technical attributes of the remote user device are defined in a device policy table. 46. A method as claimed in claim 45 , wherein the script language comprises a first mark up language. | 0.904717 |
8,938,403 | 1 | 2 | 1. A method for computing token-dependent affective response baseline levels for a user, comprising: receiving a certain temporal window of token instances; receiving, from a database, first and third affective response annotations corresponding to first and third temporal windows of token instances, respectively; wherein the database further stores a second affective response annotation corresponding to a second temporal window of token instances, the user was exposed to the first, second, and third, temporal windows of token instances during first, second, and third time periods, respectively; and wherein the first time period precedes the second time period, and the second time period precedes the third time period; using a distance function, calculating a first metric, a second metric, and a third metric between the certain temporal window of token instances and of the first temporal window of token instances, the second temporal window of token instances, and the third temporal window of token instances, respectively; wherein the first and third metrics are below a predefined threshold, while the second metric is not below the predefined threshold; computing a first affective response baseline level associated with the certain temporal window of token instances based on data comprising the first and third affective response annotations; receiving an additional temporal window of token instances; receiving, from the database, the second affective response annotation; wherein a fourth metric between the additional temporal window of token instances and the second temporal window of token instances is below the predefined threshold; and computing a second affective response baseline level associated with the additional temporal window of token instances based on data comprising the second affective response annotation; wherein the computed first affective response baseline level is different from the computed second affective response baseline level; wherein the first and second affective response baseline levels each represent a value comprising at least one of: an emotional response, and a value of a user measurement channel. | 1. A method for computing token-dependent affective response baseline levels for a user, comprising: receiving a certain temporal window of token instances; receiving, from a database, first and third affective response annotations corresponding to first and third temporal windows of token instances, respectively; wherein the database further stores a second affective response annotation corresponding to a second temporal window of token instances, the user was exposed to the first, second, and third, temporal windows of token instances during first, second, and third time periods, respectively; and wherein the first time period precedes the second time period, and the second time period precedes the third time period; using a distance function, calculating a first metric, a second metric, and a third metric between the certain temporal window of token instances and of the first temporal window of token instances, the second temporal window of token instances, and the third temporal window of token instances, respectively; wherein the first and third metrics are below a predefined threshold, while the second metric is not below the predefined threshold; computing a first affective response baseline level associated with the certain temporal window of token instances based on data comprising the first and third affective response annotations; receiving an additional temporal window of token instances; receiving, from the database, the second affective response annotation; wherein a fourth metric between the additional temporal window of token instances and the second temporal window of token instances is below the predefined threshold; and computing a second affective response baseline level associated with the additional temporal window of token instances based on data comprising the second affective response annotation; wherein the computed first affective response baseline level is different from the computed second affective response baseline level; wherein the first and second affective response baseline levels each represent a value comprising at least one of: an emotional response, and a value of a user measurement channel. 2. The method of claim 1 , further comprising utilizing the second affective response annotation for computing the first affective response baseline level; wherein, for purpose of computing the first affective response baseline level, the second affective response annotation is assigned lower weight than weight assigned to at least one of the first and third affective response annotations. | 0.5 |
9,710,431 | 20 | 21 | 20. The method of claim 19 , wherein the subsequent text includes at least one clinical assertion. | 20. The method of claim 19 , wherein the subsequent text includes at least one clinical assertion. 21. The method of claim 20 , wherein the at least one clinical assertion is an assertion within the narrative content that describes clinical information related to a patient. | 0.5 |
9,330,667 | 8 | 13 | 8. A system for detecting an endpoint of an audio record, wherein a mute duration threshold is preset as a first time threshold; and the system further comprises: a first determining unit adapted to obtain an audio record text and determine an acoustic model for a text endpoint of the audio record text; a first obtaining unit adapted to obtain each frame of audio record data in turn starting from an audio record start frame of the audio record data; a second determining unit adapted to determine a characteristics acoustic model of a decoding optimal path for an obtained current frame of the audio record data; and a threshold determining unit adapted to update the mute duration threshold to the second time threshold if it is determined that the characteristics acoustic model of the decoding optimal path for the current frame of the audio record data is the same as an acoustic model for the endpoint, wherein the second time threshold is smaller than the first time threshold. | 8. A system for detecting an endpoint of an audio record, wherein a mute duration threshold is preset as a first time threshold; and the system further comprises: a first determining unit adapted to obtain an audio record text and determine an acoustic model for a text endpoint of the audio record text; a first obtaining unit adapted to obtain each frame of audio record data in turn starting from an audio record start frame of the audio record data; a second determining unit adapted to determine a characteristics acoustic model of a decoding optimal path for an obtained current frame of the audio record data; and a threshold determining unit adapted to update the mute duration threshold to the second time threshold if it is determined that the characteristics acoustic model of the decoding optimal path for the current frame of the audio record data is the same as an acoustic model for the endpoint, wherein the second time threshold is smaller than the first time threshold. 13. The system according to claim 8 , further comprising: a receiving unit adapted to receive the audio record data and determine the audio record start frame of the audio record data. | 0.788991 |
8,620,659 | 23 | 39 | 23. A method of processing natural language utterances, the method being implemented on a computer system that includes one or more processors, the method comprising: receiving a first input of a user that comprises a natural language utterance; generating an interpretation of the natural language utterance based on one or more recognized words of the natural language utterances; generating a request based on the interpretation of the natural language utterance; invoking a domain agent to process the request; monitoring one or more actions associated with the domain agent processing the request; and determining whether the interpretation of the natural language utterance is correct or incorrect based on whether a second input is received from the user within an amount of time shorter than an expected execution time associated with the request; updating a personalized cognitive model associated with the user based on the determination of whether the interpretation is correct or incorrect, wherein the personalized cognitive model is based on a tracking of a pattern of interactions between the user and the system; and predicting one or more actions associated with the user based on the updated personalized cognitive model. | 23. A method of processing natural language utterances, the method being implemented on a computer system that includes one or more processors, the method comprising: receiving a first input of a user that comprises a natural language utterance; generating an interpretation of the natural language utterance based on one or more recognized words of the natural language utterances; generating a request based on the interpretation of the natural language utterance; invoking a domain agent to process the request; monitoring one or more actions associated with the domain agent processing the request; and determining whether the interpretation of the natural language utterance is correct or incorrect based on whether a second input is received from the user within an amount of time shorter than an expected execution time associated with the request; updating a personalized cognitive model associated with the user based on the determination of whether the interpretation is correct or incorrect, wherein the personalized cognitive model is based on a tracking of a pattern of interactions between the user and the system; and predicting one or more actions associated with the user based on the updated personalized cognitive model. 39. The method of claim 23 , further comprising: determining that the interpretation of the natural language utterance was incorrect in response to the user repeating the natural language utterance. | 0.892741 |
9,818,141 | 13 | 14 | 13. The computer program product of claim 10 , further comprising: program instructions, stored on at least one of the one or more storage devices, to receive an input, wherein the input changes a selection of a provenance attribute in the first set of provenance attributes, forming a second set of provenance attributes; and program instructions, stored on at least one of the one or more storage devices, to add, responsive to the input, a second data cube to the set of data cubes, forming a second set of data cubes. | 13. The computer program product of claim 10 , further comprising: program instructions, stored on at least one of the one or more storage devices, to receive an input, wherein the input changes a selection of a provenance attribute in the first set of provenance attributes, forming a second set of provenance attributes; and program instructions, stored on at least one of the one or more storage devices, to add, responsive to the input, a second data cube to the set of data cubes, forming a second set of data cubes. 14. The computer program product of claim 13 , further comprising: program instructions, stored on at least one of the one or more storage devices, to compute a second confidence level of a second result set of the query obtained using the second data cube; and program instructions, stored on at least one of the one or more storage devices, to present the second set of data cubes, the second set of provenance attributes, and the second confidence level in the preview. | 0.5 |
9,507,852 | 2 | 3 | 2. The computer-implemented method of claim 1 , wherein solving the MAP inference problem further comprises solving an integer linear program (ILP) defined as: max x β X , y β Y , z β { 0 , 1 } ο J ο β’ β’ ΞΈ T T β’ F ( w ) β’ x + ΞΈ P T β’ Hz , β’ β j β J : j arc = a β’ β’ z β‘ ( j ) = x β‘ ( a ) β’ β’ β a β A , β’ β j β J : j mt = b β’ β’ z β‘ ( j ) = y β‘ ( b ) β’ β’ β b β B , and β j β J : j ht = b , j mod = m β’ β’ z β‘ ( j ) = y β‘ ( b ) β’ β’ β b β B , m β [ n ] , where J represents a set of joint features j, each joint feature j corresponding to a head position h, a modifier position m, a trigram context t centered at the head, and a trigram context ti centered at the modifier, H represents a parsing feature matrix based on the feature matrix G (x,w) but having a dependency on x removed, z represents a variable replacing cubic terms y(j arc )x(j ht )x(j mt ) with j arc =(h,m), j ht =(h,t), j mt =(m,u), and j mod =m, a represents a specific arc of a set of first-order dependency arcs A, b represents a specific trigram of a set of trigrams B, and [n] represents a set of the tokens. | 2. The computer-implemented method of claim 1 , wherein solving the MAP inference problem further comprises solving an integer linear program (ILP) defined as: max x β X , y β Y , z β { 0 , 1 } ο J ο β’ β’ ΞΈ T T β’ F ( w ) β’ x + ΞΈ P T β’ Hz , β’ β j β J : j arc = a β’ β’ z β‘ ( j ) = x β‘ ( a ) β’ β’ β a β A , β’ β j β J : j mt = b β’ β’ z β‘ ( j ) = y β‘ ( b ) β’ β’ β b β B , and β j β J : j ht = b , j mod = m β’ β’ z β‘ ( j ) = y β‘ ( b ) β’ β’ β b β B , m β [ n ] , where J represents a set of joint features j, each joint feature j corresponding to a head position h, a modifier position m, a trigram context t centered at the head, and a trigram context ti centered at the modifier, H represents a parsing feature matrix based on the feature matrix G (x,w) but having a dependency on x removed, z represents a variable replacing cubic terms y(j arc )x(j ht )x(j mt ) with j arc =(h,m), j ht =(h,t), j mt =(m,u), and j mod =m, a represents a specific arc of a set of first-order dependency arcs A, b represents a specific trigram of a set of trigrams B, and [n] represents a set of the tokens. 3. The computer-implemented method of claim 2 , wherein solving the ILP includes utilizing, at the computing device, an exact dynamic programming algorithm. | 0.538462 |
9,471,943 | 8 | 9 | 8. The method of claim 1 , wherein a setting, of the one or more settings for preventing inclusion of the first action associated with the acting user in one or more sponsored story units, identifies a type of user and prevents generation of one or more sponsored story units based on sponsored story requests received from the identified type of user. | 8. The method of claim 1 , wherein a setting, of the one or more settings for preventing inclusion of the first action associated with the acting user in one or more sponsored story units, identifies a type of user and prevents generation of one or more sponsored story units based on sponsored story requests received from the identified type of user. 9. The method of claim 8 , wherein the type of user comprises an application. | 0.5 |
4,128,737 | 1 | 3 | 1. In an electronic device for phonetically synthesizing human speech by synthetically generating and combining the basic phonetic sounds in speech including input means responsive to successive input data identifying a desired sequence of phonemes for producing control signals comprising the parameters electronically defining the articulation patterns of said desired sequence of phonemes, a vocal source adapted to produce a voiced excitation signal having associated therewith a fundamental frequency, and output means responsive to said control signals for electronically forming the articulation patterns of said desired sequence of phonemes and further responsive to said voiced excitation signal for producing said desired sequence of phonemes; the improvement comprising: inflection control means connected to said vocal source for automatically varying the fundamental frequency of said voiced excitation signal in accordance with certain of said control signals produced by said input means. | 1. In an electronic device for phonetically synthesizing human speech by synthetically generating and combining the basic phonetic sounds in speech including input means responsive to successive input data identifying a desired sequence of phonemes for producing control signals comprising the parameters electronically defining the articulation patterns of said desired sequence of phonemes, a vocal source adapted to produce a voiced excitation signal having associated therewith a fundamental frequency, and output means responsive to said control signals for electronically forming the articulation patterns of said desired sequence of phonemes and further responsive to said voiced excitation signal for producing said desired sequence of phonemes; the improvement comprising: inflection control means connected to said vocal source for automatically varying the fundamental frequency of said voiced excitation signal in accordance with certain of said control signals produced by said input means. 3. The speech synthesizer of claim 1 wherein said inflection control means is further responsive to said input data to vary the fundamental frequency of said voiced excitation signal. | 0.823017 |
8,401,836 | 12 | 14 | 12. The computer program product of claim 11 , wherein the operations further include applying SCFG rules to line segments represented in the plurality of error surfaces to expand the plurality of error surfaces, and wherein combining the plurality of error surfaces further includes calculating a sum of one or more first error surfaces and calculating a union of one or more second error surfaces. | 12. The computer program product of claim 11 , wherein the operations further include applying SCFG rules to line segments represented in the plurality of error surfaces to expand the plurality of error surfaces, and wherein combining the plurality of error surfaces further includes calculating a sum of one or more first error surfaces and calculating a union of one or more second error surfaces. 14. The computer program product of claim 12 , wherein the operations further include calculating the union of the one or more second error surfaces using a sweep line technique. | 0.5 |
8,832,015 | 1 | 6 | 1. A computer-implemented method for identifying data files that have a common characteristic, the method comprising: receiving a plurality of data files, the plurality of data files including one or more data files having the common characteristic; generating, using one or more processors, a list that includes key terms from the plurality of data files; using the list to generate a rule set, the rule set being generated using the one or more processors by: generating a potential rule by selecting one or more key terms from the list that satisfy a term evaluation metric; evaluating the potential rule using a rule evaluation metric configured to determine a relevancy of the potential rule to the one or more data files having the common characteristic, the rule evaluation metric being further configured to determine an applicability of the potential rule to data not included in the plurality of data files; adding the potential rule to the rule set if the rule evaluation metric is satisfied; based upon the potential rule being added to the rule set, removing data files covered by the potential rule from the plurality of data files; and repeating the potential rule generation and evaluation until a stopping criterion is met; and after the stopping criterion has been met, identifying with the rule set, other data files that have the common characteristic using the one or more processors. | 1. A computer-implemented method for identifying data files that have a common characteristic, the method comprising: receiving a plurality of data files, the plurality of data files including one or more data files having the common characteristic; generating, using one or more processors, a list that includes key terms from the plurality of data files; using the list to generate a rule set, the rule set being generated using the one or more processors by: generating a potential rule by selecting one or more key terms from the list that satisfy a term evaluation metric; evaluating the potential rule using a rule evaluation metric configured to determine a relevancy of the potential rule to the one or more data files having the common characteristic, the rule evaluation metric being further configured to determine an applicability of the potential rule to data not included in the plurality of data files; adding the potential rule to the rule set if the rule evaluation metric is satisfied; based upon the potential rule being added to the rule set, removing data files covered by the potential rule from the plurality of data files; and repeating the potential rule generation and evaluation until a stopping criterion is met; and after the stopping criterion has been met, identifying with the rule set, other data files that have the common characteristic using the one or more processors. 6. The method of claim 1 , further comprising encoding the plurality of data files in a sparse data format. | 0.90395 |
9,433,310 | 14 | 15 | 14. The sleeve system for beverage containers set forth in claim 9 , further characterized in that said second face comprises a second illustration section. | 14. The sleeve system for beverage containers set forth in claim 9 , further characterized in that said second face comprises a second illustration section. 15. The sleeve system for beverage containers set forth in claim 14 , further characterized in that said second illustration section comprising second text having second information, song lyrics, short stories, rhymes, poems, and/or messages. | 0.5 |
7,912,722 | 17 | 23 | 17. A computer-implemented method for use in the computerized development of sentence-based test items, the method comprising: displaying a graphical user interface on a display, the graphical user interface comprising a database selection field, a sentence pattern entry field, an option pane, and an output pane; searching a database for one or more sentences based upon a user-specified syntax pattern entered in the sentence pattern entry field, the one or more sentences matching the user-specified syntax pattern, the one or more sentences to be used for producing one or more sentence-based test items; generating one or more responses corresponding to the one or more sentences; and producing the one or more sentence-based test items based upon the one or more sentences and the one or more responses. | 17. A computer-implemented method for use in the computerized development of sentence-based test items, the method comprising: displaying a graphical user interface on a display, the graphical user interface comprising a database selection field, a sentence pattern entry field, an option pane, and an output pane; searching a database for one or more sentences based upon a user-specified syntax pattern entered in the sentence pattern entry field, the one or more sentences matching the user-specified syntax pattern, the one or more sentences to be used for producing one or more sentence-based test items; generating one or more responses corresponding to the one or more sentences; and producing the one or more sentence-based test items based upon the one or more sentences and the one or more responses. 23. The method of claim 17 , wherein the user interface further comprises a language search icon. | 0.794492 |
8,762,880 | 3 | 4 | 3. The method of claim 1 , wherein displaying the status of the document in the out-space user interface includes displaying the status of the document in a document status display pane that is displayed in the out-space user interface. | 3. The method of claim 1 , wherein displaying the status of the document in the out-space user interface includes displaying the status of the document in a document status display pane that is displayed in the out-space user interface. 4. The method of claim 3 , further comprising displaying a selectable control in proximity to the displayed status of the document for accessing the feature for changing the status of the document. | 0.5 |
9,239,763 | 10 | 15 | 10. One or more non-transitory computer-readable media storing sequences of instructions, which, when executed by one or more processors, cause: a database server establishing a plurality of database sessions for access to a plurality of pluggable databases in a container database, wherein said container database includes a separate database dictionary for each pluggable database of said plurality of pluggable databases, wherein said database server establishing a plurality of database sessions includes, for each database session of said plurality of database sessions: receiving a connection request; determining that the connection request is for access to a particular pluggable database of said plurality of said pluggable databases; and attaching the respective database dictionary of said particular pluggable database to said each database session. | 10. One or more non-transitory computer-readable media storing sequences of instructions, which, when executed by one or more processors, cause: a database server establishing a plurality of database sessions for access to a plurality of pluggable databases in a container database, wherein said container database includes a separate database dictionary for each pluggable database of said plurality of pluggable databases, wherein said database server establishing a plurality of database sessions includes, for each database session of said plurality of database sessions: receiving a connection request; determining that the connection request is for access to a particular pluggable database of said plurality of said pluggable databases; and attaching the respective database dictionary of said particular pluggable database to said each database session. 15. The non-transitory computer-readable media of claim 10 , wherein at least two pluggable databases of said plurality of pluggable databases each define a tablespace having a same tablespace name. | 0.899696 |
8,768,698 | 1 | 3 | 1. A method, comprising: receiving, at a computing device, information indicative of popularity of a search query comprising a sequence of words; based on the information, determining one or more subsequences of words, each subsequence comprising one or more words of the search query based on an order in which the one or more words occur in the sequence of words of the search query; and providing information indicative of the one or more subsequences of words to update a speech recognition system configured to convert a given spoken utterance into a given sequence of words. | 1. A method, comprising: receiving, at a computing device, information indicative of popularity of a search query comprising a sequence of words; based on the information, determining one or more subsequences of words, each subsequence comprising one or more words of the search query based on an order in which the one or more words occur in the sequence of words of the search query; and providing information indicative of the one or more subsequences of words to update a speech recognition system configured to convert a given spoken utterance into a given sequence of words. 3. The method of claim 1 , wherein the speech recognition system is configured to include probabilities of occurrence for given sequences of words, and wherein providing the information indicative of the one or more subsequences of words to update the speech recognition system comprises: updating the probabilities of occurrence based on the one or more subsequences and the information indicative of the popularity of the search query. | 0.5 |
8,904,524 | 15 | 16 | 15. A system constructed and arranged to identify a malicious communication, the system comprising: a network interface; a memory; and a controller which includes controlling circuitry coupled to the memory, the controlling circuitry constructed and arranged to: read a domain name identifier from a network transmission; perform a lightweight evaluation of the domain name identifier to ascertain whether the network transmission corresponds to a fast flux network to generate a lightweight evaluation result; if the lightweight evaluation result indicates a likelihood that the network transmission corresponds to a FFN, provide an evaluation command to a backend evaluator, the evaluation command directing the backend evaluator to perform a backend evaluation of the domain name identifier to confirm whether the network transmission corresponds to a FFN; and if the result of the lightweight evaluation result indicates a likelihood that the network transmission does not correspond to a FFN, not provide the evaluation command to the backend evaluator; evaluator; wherein performing the lightweight evaluation includes: performing a query of the domain name on a local database of known network transactions for all electronic devices connected to the local network, results of the query including IP addresses to which the domain name has resolved in prior transactions and values of a time-to-live (TTL) parameter for each of those IP addresses; and applying a set of heuristics to the results, the set of heuristics being arranged to compute a risk score indicating the likelihood that the transmission is a FFN; and wherein the controlling circuitry is further constructed and arranged to: prioritize the evaluation commands provided to the backend evaluator according to the risk score, wherein high scoring domain names are moved to the front of a queue of evaluation commands. | 15. A system constructed and arranged to identify a malicious communication, the system comprising: a network interface; a memory; and a controller which includes controlling circuitry coupled to the memory, the controlling circuitry constructed and arranged to: read a domain name identifier from a network transmission; perform a lightweight evaluation of the domain name identifier to ascertain whether the network transmission corresponds to a fast flux network to generate a lightweight evaluation result; if the lightweight evaluation result indicates a likelihood that the network transmission corresponds to a FFN, provide an evaluation command to a backend evaluator, the evaluation command directing the backend evaluator to perform a backend evaluation of the domain name identifier to confirm whether the network transmission corresponds to a FFN; and if the result of the lightweight evaluation result indicates a likelihood that the network transmission does not correspond to a FFN, not provide the evaluation command to the backend evaluator; evaluator; wherein performing the lightweight evaluation includes: performing a query of the domain name on a local database of known network transactions for all electronic devices connected to the local network, results of the query including IP addresses to which the domain name has resolved in prior transactions and values of a time-to-live (TTL) parameter for each of those IP addresses; and applying a set of heuristics to the results, the set of heuristics being arranged to compute a risk score indicating the likelihood that the transmission is a FFN; and wherein the controlling circuitry is further constructed and arranged to: prioritize the evaluation commands provided to the backend evaluator according to the risk score, wherein high scoring domain names are moved to the front of a queue of evaluation commands. 16. A system according to claim 15 , wherein performing the query of the domain name includes: performing a lookup operation on the domain name identifier in the local database, the local database including a set of entries, each entry of the set of entries corresponding to an attempt to access data from a data source at a network location identified by including a domain name identifier, each entry including a domain name identifier, a timestamp and an IP address to which the domain name identifier resolved. | 0.5 |
7,810,021 | 21 | 35 | 21. An apparatus for producing an electronic literary macramΓ© of texts from a literary work, comprising: a computer, input means for the computer; display means for the computer; information storage and retrieval means for holding data and instructions for the computer; a repository for information connected to the computer; a set of files residing in the repository for information; a database program or spreadsheet program operating within the computer; a database supported by the database program or spreadsheet program for holding information concerning the characteristics of one or more scenes presented in the literary work; a set of processing control files residing in the repository for information, for linking the scenes of the literary work; a set of scene text files residing in the repository for information and containing the literary work; a set of reference text files residing in the repository for information and containing information supportive of the literary work; a set of link records residing in the repository for information and containing link information interconnecting the contents of the scene text files and reference text files; a set of utility programs and scripts operating within the computer for converting the set of scene text files into a set of scene hypertext files linking among scene text files of the literary work and linking to reference hypertext files and for converting the set of reference text files to reference hypertext files derived from the reference text files and linked among themselves and linked to the scene hypertext; a browser program operating within the computer for displaying interlinked hypertext files; and a set of display processing programs operating within the computer for adapting the presentation of interlinked hypertext files, wherein the database contains one or more data elements comprising attributes of each scene of a story and wherein the one or more data elements comprise for each scene: a scene title; a scene locale; a scene designator or identifier; the point of view from which the scene is rendered; the date and time of the scene within the narrative text; the copyright year of the writing of the scene; one or more keywords characterizing the scene; a designator of the chapter in which the scene appears; a designator of the section of the chapter in which the scene appears; the source file from which the scene text file is taken; the style of presentation required for the scene text; and the location or window of presentation required for the scene text file. | 21. An apparatus for producing an electronic literary macramΓ© of texts from a literary work, comprising: a computer, input means for the computer; display means for the computer; information storage and retrieval means for holding data and instructions for the computer; a repository for information connected to the computer; a set of files residing in the repository for information; a database program or spreadsheet program operating within the computer; a database supported by the database program or spreadsheet program for holding information concerning the characteristics of one or more scenes presented in the literary work; a set of processing control files residing in the repository for information, for linking the scenes of the literary work; a set of scene text files residing in the repository for information and containing the literary work; a set of reference text files residing in the repository for information and containing information supportive of the literary work; a set of link records residing in the repository for information and containing link information interconnecting the contents of the scene text files and reference text files; a set of utility programs and scripts operating within the computer for converting the set of scene text files into a set of scene hypertext files linking among scene text files of the literary work and linking to reference hypertext files and for converting the set of reference text files to reference hypertext files derived from the reference text files and linked among themselves and linked to the scene hypertext; a browser program operating within the computer for displaying interlinked hypertext files; and a set of display processing programs operating within the computer for adapting the presentation of interlinked hypertext files, wherein the database contains one or more data elements comprising attributes of each scene of a story and wherein the one or more data elements comprise for each scene: a scene title; a scene locale; a scene designator or identifier; the point of view from which the scene is rendered; the date and time of the scene within the narrative text; the copyright year of the writing of the scene; one or more keywords characterizing the scene; a designator of the chapter in which the scene appears; a designator of the section of the chapter in which the scene appears; the source file from which the scene text file is taken; the style of presentation required for the scene text; and the location or window of presentation required for the scene text file. 35. The apparatus of claim 21 wherein the set of processing control files residing in the repository for information resides in the database. | 0.915468 |
9,165,032 | 1 | 20 | 1. An automated method to allocate resources of a highly parallel computing system for processing a database query, the method comprising: receiving the database query from an application at a client system; compiling the query and computing the number of executive server processes (ESPs) in each ESP layer of the query; generating an affinity value, wherein the affinity value specifies a subset of processors for a range of processor subset sizes; executing the query wherein placement of ESP layers of the query onto processors of the computing system is determined using the affinity value; and returning results of the execution to the application. | 1. An automated method to allocate resources of a highly parallel computing system for processing a database query, the method comprising: receiving the database query from an application at a client system; compiling the query and computing the number of executive server processes (ESPs) in each ESP layer of the query; generating an affinity value, wherein the affinity value specifies a subset of processors for a range of processor subset sizes; executing the query wherein placement of ESP layers of the query onto processors of the computing system is determined using the affinity value; and returning results of the execution to the application. 20. The method of claim 1 , comprising: computing an estimated memory resource (EMR) required by the query, and an estimated CPU resource (ECR) required by the query; computing a first ratio of the EMR to an amount of memory available per CPU, and a second ratio of the ECR to an amount of work acceptable to be assigned per CPU; and computing a maximum degree of parallelism for the query as a function of greater of the first ratio and the second ratio. | 0.5 |
9,898,528 | 11 | 13 | 11. A computer system comprising: one or more hardware computer processors programmed, via executable code instructions, to: receive, in a user interface, a first concept and a second concept, wherein the first concept is associated with a first plurality of related terms and the second concept is associated with a second plurality of related terms; query data store comprising a plurality of segments based at least on the first concept and the second concept to retrieve a result set, the result set comprising a first segment and a second segment from the plurality of segments; determine a first quantity of occurrences of the first concept in the first segment, and a second quantity of occurrences of the second concept in the first segment; access first statistical distribution data associated with occurrences of the first concept within the plurality of segments, and second statistical distribution data associated with occurences of the second concept within the plurality of segments; determine a ranking of the first segment relative to the second segment by at least: generating a first weight by comparing the first quantity of occurrences against the first statistical distribution data; generating a second weight by comparing the second quantity of occurrences against the second statistical distribution data; and combining the first weight with the first quantity of occurrences, and the second weight with the second quantity of occurrences; calculate a first recency score associated with the first segment, wherein the ranking is based at least on the first recency score; and cause presentation, in a user interface, of the first segment and the second segment, wherein the presentation indicates the ranking. | 11. A computer system comprising: one or more hardware computer processors programmed, via executable code instructions, to: receive, in a user interface, a first concept and a second concept, wherein the first concept is associated with a first plurality of related terms and the second concept is associated with a second plurality of related terms; query data store comprising a plurality of segments based at least on the first concept and the second concept to retrieve a result set, the result set comprising a first segment and a second segment from the plurality of segments; determine a first quantity of occurrences of the first concept in the first segment, and a second quantity of occurrences of the second concept in the first segment; access first statistical distribution data associated with occurrences of the first concept within the plurality of segments, and second statistical distribution data associated with occurences of the second concept within the plurality of segments; determine a ranking of the first segment relative to the second segment by at least: generating a first weight by comparing the first quantity of occurrences against the first statistical distribution data; generating a second weight by comparing the second quantity of occurrences against the second statistical distribution data; and combining the first weight with the first quantity of occurrences, and the second weight with the second quantity of occurrences; calculate a first recency score associated with the first segment, wherein the ranking is based at least on the first recency score; and cause presentation, in a user interface, of the first segment and the second segment, wherein the presentation indicates the ranking. 13. The computer system of claim 11 , wherein determining the ranking for the first segment relative to the second segment further comprises: combining the first quantity, the first weight, the second quantity, and the second weight. | 0.815079 |
9,710,431 | 14 | 17 | 14. A method for transforming narrative content into structured output that defines where individual information resides within the output, the method comprising the steps of: receiving narrative content; scanning the narrative content using a natural language processing engine to identify one section, one clinical assertion within that section, and one element that annotates at least one of the section and the clinical assertion; extracting information from the narrative content including the section, the clinical assertion, and the element; and describing the element with a label selected from a predetermined list of labels within a clinical model, wherein the predetermined list of labels differs according to a type of information to be described. | 14. A method for transforming narrative content into structured output that defines where individual information resides within the output, the method comprising the steps of: receiving narrative content; scanning the narrative content using a natural language processing engine to identify one section, one clinical assertion within that section, and one element that annotates at least one of the section and the clinical assertion; extracting information from the narrative content including the section, the clinical assertion, and the element; and describing the element with a label selected from a predetermined list of labels within a clinical model, wherein the predetermined list of labels differs according to a type of information to be described. 17. The method of claim 14 , further comprising a step of storing or using the information within at least one of an electronic health record, data warehouse, or health information exchange. | 0.597458 |
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