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30. The system of claim 29 wherein the one or more computing devices are configured to generate at least one of the query candidates by translation of a natural language question entered at the user device, and wherein translation of the natural language question includes generation of at least one sub-string from a string of text corresponding to the natural language question, and selection of at least one of a plurality of query template components corresponding to the at least one sub-string.
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30. The system of claim 29 wherein the one or more computing devices are configured to generate at least one of the query candidates by translation of a natural language question entered at the user device, and wherein translation of the natural language question includes generation of at least one sub-string from a string of text corresponding to the natural language question, and selection of at least one of a plurality of query template components corresponding to the at least one sub-string. 32. The system of claim 30 wherein the one or more computing devices are further configured to translate the natural language question by generation of a query template including the plurality of query template components by designation of a plurality of predefined text sub-strings and a plurality of variables to which the predefined text sub-strings correspond, and by definition of a query generator with respect to the variables, the query generator being operable to generate the query from selected ones of the predefined text sub-strings substituted for the corresponding variables.
| 0.728195 |
3. The method of claim 1 , further comprising the step of the information objects implementing a common interface, wherein the common interface allows access to instances of information objects by name or position, and allows display of a current instance of the information object.
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3. The method of claim 1 , further comprising the step of the information objects implementing a common interface, wherein the common interface allows access to instances of information objects by name or position, and allows display of a current instance of the information object. 4. The method of claim 3 , further comprising the steps of: describing an infuriation source as a combination of network access type, message structure, and source characteristics; creating components that represent the different types of network-accessible sources, message structures, and characteristics and that collectively expose the common information object interface; and assembling the types of named components related to the descriptions to create an information source with the desired behavior.
| 0.788036 |
26. The method of claim 1, wherein said document includes roman characters, and step (a) comprises, using said computer, determining a frequency of occurrence of words defined by said roman characters in the document not contained in said stop list and having at least a predetermined number of said roman characters.
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26. The method of claim 1, wherein said document includes roman characters, and step (a) comprises, using said computer, determining a frequency of occurrence of words defined by said roman characters in the document not contained in said stop list and having at least a predetermined number of said roman characters. 27. The method of claim 26, wherein said document is an English-language document.
| 0.929392 |
9. A system comprising: a processor; a computer-readable memory communicatively coupled with the processor; a voice-message application resident on the computer-readable memory to generate user-generated voice message; a context-data application to automatically determine a context-data related to a meaning of a voice-message component and to link the context-data with the voice-message component, to convert a voice-message component into a text format, to automatically determine a meaning of the voice-message component, and to automatically determine a context data, wherein the context data describes an environmental attribute of the mobile device relating to the meaning of the voice-message component; and a sensor to automatically acquire the context data.
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9. A system comprising: a processor; a computer-readable memory communicatively coupled with the processor; a voice-message application resident on the computer-readable memory to generate user-generated voice message; a context-data application to automatically determine a context-data related to a meaning of a voice-message component and to link the context-data with the voice-message component, to convert a voice-message component into a text format, to automatically determine a meaning of the voice-message component, and to automatically determine a context data, wherein the context data describes an environmental attribute of the mobile device relating to the meaning of the voice-message component; and a sensor to automatically acquire the context data. 11. The system of claim 9 further comprising: a machine-learning application to incorporate intelligence into the context-data application.
| 0.590547 |
16. A central server for a digital content review and distribution system comprising: a communication interface communicatively coupling the central server to user devices associated with a plurality of authors of digital content via a network; and a control system associated with the communication interface and configured to: identify the plurality of authors of digital content via a registration process wherein the plurality of authors agree to review digital content in exchange for review of their own digital content; receive a submission from one of the plurality of authors including metadata for digital content to be reviewed where the metadata includes a target quality level for the digital content; and effect review of the digital content by at least one group of reviewers selected from others of the plurality of authors based on the metadata for the digital content and reviewer credentials for the others of the plurality of authors, wherein effecting review of the digital content includes selecting a group of reviewers from the others of the plurality of authors based on the target quality level and the reviewer credentials of the others of the plurality of authors and feedback is provided to the one of the plurality of authors based on the review.
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16. A central server for a digital content review and distribution system comprising: a communication interface communicatively coupling the central server to user devices associated with a plurality of authors of digital content via a network; and a control system associated with the communication interface and configured to: identify the plurality of authors of digital content via a registration process wherein the plurality of authors agree to review digital content in exchange for review of their own digital content; receive a submission from one of the plurality of authors including metadata for digital content to be reviewed where the metadata includes a target quality level for the digital content; and effect review of the digital content by at least one group of reviewers selected from others of the plurality of authors based on the metadata for the digital content and reviewer credentials for the others of the plurality of authors, wherein effecting review of the digital content includes selecting a group of reviewers from the others of the plurality of authors based on the target quality level and the reviewer credentials of the others of the plurality of authors and feedback is provided to the one of the plurality of authors based on the review. 27. The central server of claim 16 wherein the control system is further configured to: receive feedback regarding the digital content from the at least one group of reviewers; and provide the feedback to the one of the plurality of authors based on the feedback from the at least one group of reviewers.
| 0.621829 |
9. The method of claim 8 wherein the step of forming comprises: storing status data which indicates that status of the user is to be retrieved from the server computer system within the new user registration record.
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9. The method of claim 8 wherein the step of forming comprises: storing status data which indicates that status of the user is to be retrieved from the server computer system within the new user registration record. 10. The method of claim 9 wherein the step of storing status data comprises: storing data which indicates no valid status as the status data.
| 0.937665 |
11. An article of manufacture comprising: a non-transitory computer-readable medium including instructions that, when executed by at least one processor of a machine, cause the machine to perform operations comprising: receiving a serial data stream input, the serial data stream input representing one or more computer files, the serial data stream input comprising data type descriptions of the one or more computer files and program elements corresponding to the data type descriptions; detecting a first one of the data type descriptions in the serial data stream input; detecting a first annotation for a first one of the program elements corresponding to the first one of the data type descriptions in the serial data stream input based on detecting the first one of the data type descriptions; determining if the first annotation and the program elements corresponding to the first one of the data type descriptions are of interest in response to detecting the first annotation; obtaining an annotation value for the first annotation in the serial data stream input in response to determining the first annotation and the program elements corresponding to the first one of the data type descriptions are of interest; and generating output comprising the annotation value for the first annotation in response to determining the first annotation and the program elements corresponding to the first one of the data type descriptions are of interest.
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11. An article of manufacture comprising: a non-transitory computer-readable medium including instructions that, when executed by at least one processor of a machine, cause the machine to perform operations comprising: receiving a serial data stream input, the serial data stream input representing one or more computer files, the serial data stream input comprising data type descriptions of the one or more computer files and program elements corresponding to the data type descriptions; detecting a first one of the data type descriptions in the serial data stream input; detecting a first annotation for a first one of the program elements corresponding to the first one of the data type descriptions in the serial data stream input based on detecting the first one of the data type descriptions; determining if the first annotation and the program elements corresponding to the first one of the data type descriptions are of interest in response to detecting the first annotation; obtaining an annotation value for the first annotation in the serial data stream input in response to determining the first annotation and the program elements corresponding to the first one of the data type descriptions are of interest; and generating output comprising the annotation value for the first annotation in response to determining the first annotation and the program elements corresponding to the first one of the data type descriptions are of interest. 12. The article of manufacture of claim 11 , wherein the medium further includes instructions that, when executed by the at least one processor, cause the machine to perform operations comprising: detecting a second annotation for one of the program elements corresponding to the first one of the data type descriptions based on the first annotation not being of interest.
| 0.5 |
16. The method of generating the record sentence of claim 14 , further comprising: generating a weight for the sentence having the unseen unit, wherein the weight for the sentence is determined according to a linguistic criterion for the unseen unit and/or a phonetic criterion for the unseen unit.
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16. The method of generating the record sentence of claim 14 , further comprising: generating a weight for the sentence having the unseen unit, wherein the weight for the sentence is determined according to a linguistic criterion for the unseen unit and/or a phonetic criterion for the unseen unit. 17. The method of generating the record sentence of claim 16 , wherein the weight for the sentence is determined according to at least one of the weight of the unseen unit included in the sentence, the weight of the word included in the sentence, and a type of the sentence.
| 0.877656 |
5. The computer storage medium of claim 2 , wherein the operation scenario type is one of a plurality of operation scenario types that include an online chat scenario and a document authoring scenario.
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5. The computer storage medium of claim 2 , wherein the operation scenario type is one of a plurality of operation scenario types that include an online chat scenario and a document authoring scenario. 8. The computer storage medium of claim 5 , wherein the context data includes at least one of application specific data, environmental data, or user status data.
| 0.926667 |
1. A computer implemented method comprising: selecting a first software program and a second software program by placing a first icon on a graphical display screen in a prespecified relationship with a second icon on said display screen; extracting first metadata from at least one database, wherein the first metadata describe a first set of constraints that apply to installation of the first software program on a data processing system; extracting second metadata from the at least one database, wherein the second metadata describe a second set of constraints that apply to installation of the second software program on the data processing system; generating a third set of constraints from the first metadata and the second metadata, wherein the third set of constraints comprise an intersection of the first set of constraints and the second set of constraints; determining whether installation of both the first program and the second program on the data processing system violates any constraint contained in the third set of constraints; and storing the third set of constraints in a tooling mechanism configured to perform extracting the first metadata, extracting the second metadata, generating the third set of constraints, and determining whether installation violates any constraint.
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1. A computer implemented method comprising: selecting a first software program and a second software program by placing a first icon on a graphical display screen in a prespecified relationship with a second icon on said display screen; extracting first metadata from at least one database, wherein the first metadata describe a first set of constraints that apply to installation of the first software program on a data processing system; extracting second metadata from the at least one database, wherein the second metadata describe a second set of constraints that apply to installation of the second software program on the data processing system; generating a third set of constraints from the first metadata and the second metadata, wherein the third set of constraints comprise an intersection of the first set of constraints and the second set of constraints; determining whether installation of both the first program and the second program on the data processing system violates any constraint contained in the third set of constraints; and storing the third set of constraints in a tooling mechanism configured to perform extracting the first metadata, extracting the second metadata, generating the third set of constraints, and determining whether installation violates any constraint. 9. The method of claim 1 , wherein the first software program and the second software program interact with one another.
| 0.632879 |
18. At least one computer-readable storage medium having instructions stored thereon that, when executed by a computing device, cause the computing device to perform acts, the acts comprising: receiving a user search topic; concurrently presenting, on a display of the computing device, a collage template and search results for the user search topic, wherein the collage template comprises a grid of rectangles representing a collage; automatically populating a first user-selected search result in a portion of the rectangles of the grid, a number and a relative positioning of the rectangles of the portion being based at least in part on a first aspect ratio of the first user-selected search result; subsequent to the automatically populating the first user-selected search result, automatically populating a second user-selected search result in the grid by repositioning the first user-selected search result based at least on a second aspect ratio of the second user-selected search result; and, automatically populating additional user-selected search results into unpopulated rectangles of the grid.
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18. At least one computer-readable storage medium having instructions stored thereon that, when executed by a computing device, cause the computing device to perform acts, the acts comprising: receiving a user search topic; concurrently presenting, on a display of the computing device, a collage template and search results for the user search topic, wherein the collage template comprises a grid of rectangles representing a collage; automatically populating a first user-selected search result in a portion of the rectangles of the grid, a number and a relative positioning of the rectangles of the portion being based at least in part on a first aspect ratio of the first user-selected search result; subsequent to the automatically populating the first user-selected search result, automatically populating a second user-selected search result in the grid by repositioning the first user-selected search result based at least on a second aspect ratio of the second user-selected search result; and, automatically populating additional user-selected search results into unpopulated rectangles of the grid. 20. The at least one computer-readable storage medium of claim 18 , the acts further comprising: updating the collage by populating remaining rectangles of the grid with additional items, the additional items being media items.
| 0.550462 |
20. The computer-implemented method of claim 19 , engaging in the electronic dialogue comprising the video/voice processor: determining a second response based at least in part upon at least one of the determined document type and determined tax data; and presenting the second response to the user through the mobile communication device.
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20. The computer-implemented method of claim 19 , engaging in the electronic dialogue comprising the video/voice processor: determining a second response based at least in part upon at least one of the determined document type and determined tax data; and presenting the second response to the user through the mobile communication device. 24. The computer-implemented method of claim 20 , the second response being determined by the video/voice processor with reference to a tree structure or table identifying which response should be presented to the user based on at least one of respective determined document types and respective determined tax data.
| 0.910602 |
11. The system of claim 8 , wherein the processing device is further configured to: repeat the translating operation using the vector of parameters responsive to evaluating a terminating condition.
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11. The system of claim 8 , wherein the processing device is further configured to: repeat the translating operation using the vector of parameters responsive to evaluating a terminating condition. 13. The system of claim 11 , wherein evaluating the terminating condition comprises determining that the vectors of modified parameters are equal to each other.
| 0.940801 |
22. A non-transitory computer readable storage medium storing one or more programs, the one or more programs comprising instructions, which when executed by a first electronic device with a display, the first electronic device including one or more user-interface devices for receiving editing inputs from a user of the first electronic device, cause the first electronic device to: maintain a directed acyclic graph to represent content collaboratively edited by the first electronic device and one or more second electronic devices of multiple collaborating devices, wherein the directed acyclic graph includes a plurality of nodes each representing a respective content object that is created or edited by one or more of the multiple collaborating devices, wherein at least a first node of the plurality of nodes represents a textual content object and at least a second node of the plurality of nodes represents a sketch content object, and wherein each node representing a corresponding sketch content object includes a respective command sequence used to create content of the corresponding sketch content object; during a respective synchronization period, receive one or more editing inputs from one or more devices of the multiple collaborating devices; modify the directed acyclic graph based on relationships between the editing inputs and existing content objects embodied in the directed acyclic graph; determine whether the one or more editing inputs modifies an existing sketch content object represented in the directed acyclic graph; and in accordance with a determination that a first editing input of the one or more editing inputs modifies a first existing sketch content object represented by a first command sequence in a first respective node in the directed acyclic graph, update the first command sequence in the first respective node by merging each individual drawing command included in the first editing input with the first command sequence to produce, in the first respective node, an updated first command sequence of two or more commands that represents the first existing sketch content object as modified by the first editing input.
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22. A non-transitory computer readable storage medium storing one or more programs, the one or more programs comprising instructions, which when executed by a first electronic device with a display, the first electronic device including one or more user-interface devices for receiving editing inputs from a user of the first electronic device, cause the first electronic device to: maintain a directed acyclic graph to represent content collaboratively edited by the first electronic device and one or more second electronic devices of multiple collaborating devices, wherein the directed acyclic graph includes a plurality of nodes each representing a respective content object that is created or edited by one or more of the multiple collaborating devices, wherein at least a first node of the plurality of nodes represents a textual content object and at least a second node of the plurality of nodes represents a sketch content object, and wherein each node representing a corresponding sketch content object includes a respective command sequence used to create content of the corresponding sketch content object; during a respective synchronization period, receive one or more editing inputs from one or more devices of the multiple collaborating devices; modify the directed acyclic graph based on relationships between the editing inputs and existing content objects embodied in the directed acyclic graph; determine whether the one or more editing inputs modifies an existing sketch content object represented in the directed acyclic graph; and in accordance with a determination that a first editing input of the one or more editing inputs modifies a first existing sketch content object represented by a first command sequence in a first respective node in the directed acyclic graph, update the first command sequence in the first respective node by merging each individual drawing command included in the first editing input with the first command sequence to produce, in the first respective node, an updated first command sequence of two or more commands that represents the first existing sketch content object as modified by the first editing input. 24. The non-transitory computer readable storage medium of claim 22 , wherein the directed acyclic graph includes multiple parallel paths each including at least one node that represents a respective one of multiple concurrent editing inputs received from distinct devices of the multiple collaborating devices.
| 0.625884 |
4. A method in accordance with claim 2 wherein the step of determining a language comprises routing said call to a translation agency and determining said language by a translator at said translation agency.
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4. A method in accordance with claim 2 wherein the step of determining a language comprises routing said call to a translation agency and determining said language by a translator at said translation agency. 5. A method in accordance with claim 4 wherein routing said call comprises bridging said call among said caller, said emergency services operator position and said translation agency.
| 0.882653 |
15. A SQL Visualizer as claimed in claim 1 , in which a user can make changes to either the graphical or the textual representation of a SQL statement or SQL procedure, and the SQL Visualizer can then automatically generate the other of the graphical or the textual representation such that the same change is incorporated in both the textual representation and the graphical representation.
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15. A SQL Visualizer as claimed in claim 1 , in which a user can make changes to either the graphical or the textual representation of a SQL statement or SQL procedure, and the SQL Visualizer can then automatically generate the other of the graphical or the textual representation such that the same change is incorporated in both the textual representation and the graphical representation. 16. A SQL Visualizer as claimed in claim 15 , wherein said graphical and textual representations are automatically synchronized.
| 0.879923 |
16. A method, comprising: detecting a predetermined operation input related to accessing document information using a hardware processor of a computer system; extracting a content of the document information in response to detection of the predetermined operation; acquiring policy information comprising labeling policy information and enforcement policy information, the labeling policy information representing a relationship between the content and a sensitivity label and the enforcement policy information defining details of control for the document information based on the sensitivity label; determining the sensitivity label of the document information by determining the sensitivity label corresponding to the content based on the policy information in response to extraction of the content; performing execution control for the document information in response to the determination of the sensitivity label, wherein the execution control for the document information is based on the sensitivity label and enforcement policy information included in the policy information, the enforcement policy information defining details of control for the document information based on the sensitivity label; and enabling or disabling printing for each content of the document information based on the enforcement policy information, wherein a printed matter of the document information includes at least one content of the document information printed as is and at least one content of the document information masked so that the masked content cannot be recognized, wherein the policy information is acquired from a policy server.
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16. A method, comprising: detecting a predetermined operation input related to accessing document information using a hardware processor of a computer system; extracting a content of the document information in response to detection of the predetermined operation; acquiring policy information comprising labeling policy information and enforcement policy information, the labeling policy information representing a relationship between the content and a sensitivity label and the enforcement policy information defining details of control for the document information based on the sensitivity label; determining the sensitivity label of the document information by determining the sensitivity label corresponding to the content based on the policy information in response to extraction of the content; performing execution control for the document information in response to the determination of the sensitivity label, wherein the execution control for the document information is based on the sensitivity label and enforcement policy information included in the policy information, the enforcement policy information defining details of control for the document information based on the sensitivity label; and enabling or disabling printing for each content of the document information based on the enforcement policy information, wherein a printed matter of the document information includes at least one content of the document information printed as is and at least one content of the document information masked so that the masked content cannot be recognized, wherein the policy information is acquired from a policy server. 17. The method as recited in claim 16 , wherein the policy information is acquired in response to extraction of the content, and wherein the sensitivity label of the document information is determined in response to the acquisition of the policy information.
| 0.542477 |
1. A method of managing network devices in a network, the method comprising: receiving a trigger for an operation command to be executed by a network device; connecting to the network device; supplying to the network device a command line interface command for execution of the operation command, a randomly generated string being included at the end of the command line interface command; receiving the output of the operation command from the network device; detecting an end of the operation command output based on the randomly generated string; and parsing the operation command output generated by the device using an XML based parser.
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1. A method of managing network devices in a network, the method comprising: receiving a trigger for an operation command to be executed by a network device; connecting to the network device; supplying to the network device a command line interface command for execution of the operation command, a randomly generated string being included at the end of the command line interface command; receiving the output of the operation command from the network device; detecting an end of the operation command output based on the randomly generated string; and parsing the operation command output generated by the device using an XML based parser. 7. The method of claim 1 , wherein the operation command is a show command.
| 0.78289 |
12. A method comprising: providing a user interface to a display device of a mobile device, the user interface including a first area to receive a text message and a second area to receive an identifier associated with an addressee device; receiving the text message and the identifier; submitting the text message for conversion into an audio message and for transmission of the audio message and an acknowledge message to the addressee device associated with the identifier, wherein the acknowledge message permits the addressee device to accept delivery of the audio message or to decline delivery of the audio message; receiving, at the mobile device, a reply voice message in response to the addressee device accepting delivery of the audio message; and providing a repeat input option at the user interface, the repeat input option to specify an option to automatically attempt one or more additional transmissions in response to the addressee device not accepting or declining delivery of the audio message.
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12. A method comprising: providing a user interface to a display device of a mobile device, the user interface including a first area to receive a text message and a second area to receive an identifier associated with an addressee device; receiving the text message and the identifier; submitting the text message for conversion into an audio message and for transmission of the audio message and an acknowledge message to the addressee device associated with the identifier, wherein the acknowledge message permits the addressee device to accept delivery of the audio message or to decline delivery of the audio message; receiving, at the mobile device, a reply voice message in response to the addressee device accepting delivery of the audio message; and providing a repeat input option at the user interface, the repeat input option to specify an option to automatically attempt one or more additional transmissions in response to the addressee device not accepting or declining delivery of the audio message. 20. The method of claim 12 , wherein the one or more additional transmissions are automatically attempted when the addressee device is engaged and does not answer a call attempting delivery of the audio message, and wherein the one or more additional transmissions are automatically attempted when the addressee device is free but does not answer the call attempting delivery of the audio message.
| 0.550753 |
1. A distributed computer system for full customization of process logic in an integrated system having multiple nodes, including dissimilar host computer systems each having an API and each using different communications and data protocols, each node being independent of other nodes in said system, said system capable of handling standard markup language data, including XML documents, from different communication protocols regardless of the underlying protocol, comprising: at least two host computer systems, each host computer system having process logic, said process logic integrating disparate systems having different communication protocols of each host computer system, wherein applications being integrated in said distributed computer system do not require any additional code; whereby, each individual unit of process logic is a service, multiple services are grouped together to form an agent, and each said incoming standard markup language data, regardless of the underlying communications protocol, is processed by each host computer system as a document object model (DOM); an interpreted, non-web-based scripting language not requiring compiling in which said process logic nodes in said host computers operate, said non-web-based scripting language allowing for remote administration and customization of said process logic nodes in said host computers, whereby extensions to said scripting language are used to create, manipulate, and modify XML documents through rules-based, simple declarative extensions of said scripting language in the form of a custom language binding to XML document syntax to facilitate manipulating an XML payload in a manner that is native to said process logic nodes' implementation of said scripting language, thereby greatly simplifying any necessary coding to integrate the dissimilar host computer systems; business logic for mapping said declarative extensions of said non-web-based scripting language between said dissimilar host systems; translation logic connected to said business logic for translating data formats and correlating events between said dissimilar host systems using said non-web-based scripting language for manipulation of the data formats; and a host adapter in which said host system API is in code accessible to said non-web-based scripting language utilized by said host computers.
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1. A distributed computer system for full customization of process logic in an integrated system having multiple nodes, including dissimilar host computer systems each having an API and each using different communications and data protocols, each node being independent of other nodes in said system, said system capable of handling standard markup language data, including XML documents, from different communication protocols regardless of the underlying protocol, comprising: at least two host computer systems, each host computer system having process logic, said process logic integrating disparate systems having different communication protocols of each host computer system, wherein applications being integrated in said distributed computer system do not require any additional code; whereby, each individual unit of process logic is a service, multiple services are grouped together to form an agent, and each said incoming standard markup language data, regardless of the underlying communications protocol, is processed by each host computer system as a document object model (DOM); an interpreted, non-web-based scripting language not requiring compiling in which said process logic nodes in said host computers operate, said non-web-based scripting language allowing for remote administration and customization of said process logic nodes in said host computers, whereby extensions to said scripting language are used to create, manipulate, and modify XML documents through rules-based, simple declarative extensions of said scripting language in the form of a custom language binding to XML document syntax to facilitate manipulating an XML payload in a manner that is native to said process logic nodes' implementation of said scripting language, thereby greatly simplifying any necessary coding to integrate the dissimilar host computer systems; business logic for mapping said declarative extensions of said non-web-based scripting language between said dissimilar host systems; translation logic connected to said business logic for translating data formats and correlating events between said dissimilar host systems using said non-web-based scripting language for manipulation of the data formats; and a host adapter in which said host system API is in code accessible to said non-web-based scripting language utilized by said host computers. 3. A system as set forth in claim 1 , wherein said process logic is business logic.
| 0.542923 |
17. A method for determining a set of rules for ordering text of a language, the method comprising: receiving a target order for the language, the target order including sequences of characters ordered according to rules for ordering the language; determining strengths of differences between the characters in the target order; identifying contractions in the target order, the contractions being strings of characters sorted in the target order as a shorter string of characters; identifying expansions in the target order, the expansions being strings of characters sorted in the target order as a longer string of characters; and determining, using a processor, the set of rules for ordering text of the language, the set of rules for ordering text of the language replicating the target order, the set of rules for ordering text of the language being determined based on the strengths of differences between the characters, the contractions and the expansions, wherein the target order is a sequential ordering among the sequences of the characters ordered according to rules for ordering the language.
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17. A method for determining a set of rules for ordering text of a language, the method comprising: receiving a target order for the language, the target order including sequences of characters ordered according to rules for ordering the language; determining strengths of differences between the characters in the target order; identifying contractions in the target order, the contractions being strings of characters sorted in the target order as a shorter string of characters; identifying expansions in the target order, the expansions being strings of characters sorted in the target order as a longer string of characters; and determining, using a processor, the set of rules for ordering text of the language, the set of rules for ordering text of the language replicating the target order, the set of rules for ordering text of the language being determined based on the strengths of differences between the characters, the contractions and the expansions, wherein the target order is a sequential ordering among the sequences of the characters ordered according to rules for ordering the language. 18. The method of claim 17 , wherein the strengths of differences between the characters include level one strength difference, level two strength difference and level three strength difference.
| 0.681963 |
11. The computerized system of claim 9 wherein said additional meal suggestion messages are meal substitution messages.
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11. The computerized system of claim 9 wherein said additional meal suggestion messages are meal substitution messages. 12. The computerized system of claim 11 wherein said meal substitution messages comprise suggested substitutions for food items in said meal suggestion messages.
| 0.959419 |
2. The method of claim 1 , wherein identifying one or more of the candidate terms as being candidate entries includes: identifying, as being candidate entries, only candidate terms that are associated with a respective first count greater than a respective second count.
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2. The method of claim 1 , wherein identifying one or more of the candidate terms as being candidate entries includes: identifying, as being candidate entries, only candidate terms that are associated with a respective first count greater than a respective second count. 8. The method of claim 2 wherein determining the second count comprises counting a number of search queries that include the candidate term and one or more other terms.
| 0.924351 |
5. A method as described in claim 4 further comprising the step of d) scheduling said plurality of C++ user processes for execution according to said concurrency, wherein said step d) is performed by a C++ scheduler and comprises the steps of: d1) representing clock signals of said circuit as clock objects in C++ within said plurality of C++ user processes, said clock objects declared as instances of a clock class of said C++ library; d2) synchronizing said plurality of C++ user processes to an edge of a respective clock object by sequentially scheduling said plurality of C++ user processes for execution upon the occurrence of said edge of said respective clock.sub.-- object, said edge obtained from a priority queue maintained in said memory; and d3) at the completion of a clock cycle of said clock object, computing the time of a next edge of said clock object and storing said time into said priority queue.
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5. A method as described in claim 4 further comprising the step of d) scheduling said plurality of C++ user processes for execution according to said concurrency, wherein said step d) is performed by a C++ scheduler and comprises the steps of: d1) representing clock signals of said circuit as clock objects in C++ within said plurality of C++ user processes, said clock objects declared as instances of a clock class of said C++ library; d2) synchronizing said plurality of C++ user processes to an edge of a respective clock object by sequentially scheduling said plurality of C++ user processes for execution upon the occurrence of said edge of said respective clock.sub.-- object, said edge obtained from a priority queue maintained in said memory; and d3) at the completion of a clock cycle of said clock object, computing the time of a next edge of said clock object and storing said time into said priority queue. 8. A method as described in claim 5 further comprising the steps of: representing multi-valued logic in said C++ library wherein signal values of: logical high ("1"); logical low ("0"); high impedance; and unknown are represented by separate internal values; and performing AND, OR, XOR and NOT functions on arguments of said multi-valued logic.
| 0.808715 |
13. The system of claim 12 , wherein the database engine server is further configured to generate the predefined feedback comment to include a name of the item.
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13. The system of claim 12 , wherein the database engine server is further configured to generate the predefined feedback comment to include a name of the item. 14. The system of claim 13 , wherein: the database includes an item table storing a plurality of item names including the name of the item; and the database engine server is further configured to accessing the item table.
| 0.870352 |
18. The digital system as set forth in claim 17 , wherein the soft bin regions are defined by products of sigmoid functions and the increasing of the number of soft bin regions comprises splitting a selected soft bin region into two smaller soft bin regions each defined by a product of sigmoid functions in which the two smaller soft bin regions together span the selected soft bin region.
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18. The digital system as set forth in claim 17 , wherein the soft bin regions are defined by products of sigmoid functions and the increasing of the number of soft bin regions comprises splitting a selected soft bin region into two smaller soft bin regions each defined by a product of sigmoid functions in which the two smaller soft bin regions together span the selected soft bin region. 19. The digital system as set forth in claim 18 , wherein the repetition of the estimating repeats the estimating of the integrals only for the two smaller soft bin regions generated by the splitting.
| 0.807731 |
1. A personalized consumer to business matchmaking system executed on a CPU and a memory unit of a computer system, the personalized consumer to business matchmaking system comprising: a business profile configured to contain business-relevant information, wherein the business-relevant information includes corporate-specific and corporate-desired data relevant to a particular business; a searching consumer's profile configured to contain searching consumer-relevant information, wherein the searching consumer-relevant information includes consumer-specific and consumer-desired data relevant to the searching consumer for desired businesses, and wherein the searching consumer's profile is compared against the business profile to derive a Searching Consumer to Business Match Score (SCBMS); a business evaluator's consumer profile, wherein the business evaluator has previously reviewed, rated, and/or evaluated the particular business and wherein the business evaluator's consumer profile is used to derive a Searching Consumer to Consumer Match Score (SCCMS) and a Consumer to Business Match Score (CBMS); a Consumer Status Score (CSS) calculator executed on the CPU and the memory unit of the computer system, wherein the Consumer Status Score (CSS) calculator is configured to measure a first business rating tendency displayed by the searching consumer against a second business rating tendency displayed by the business evaluator; and a consumer rating calculator executed on the CPU and the memory unit of the computer system, wherein the consumer rating calculator is configured to provide a weighted value to a Consumer Given Rating (CSGR) for the particular business given by the business evaluator by incorporating the Searching Consumer to Consumer Match Score (SCCMS), wherein the weighted value is called a Consumer to Business Rating (CSBR).
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1. A personalized consumer to business matchmaking system executed on a CPU and a memory unit of a computer system, the personalized consumer to business matchmaking system comprising: a business profile configured to contain business-relevant information, wherein the business-relevant information includes corporate-specific and corporate-desired data relevant to a particular business; a searching consumer's profile configured to contain searching consumer-relevant information, wherein the searching consumer-relevant information includes consumer-specific and consumer-desired data relevant to the searching consumer for desired businesses, and wherein the searching consumer's profile is compared against the business profile to derive a Searching Consumer to Business Match Score (SCBMS); a business evaluator's consumer profile, wherein the business evaluator has previously reviewed, rated, and/or evaluated the particular business and wherein the business evaluator's consumer profile is used to derive a Searching Consumer to Consumer Match Score (SCCMS) and a Consumer to Business Match Score (CBMS); a Consumer Status Score (CSS) calculator executed on the CPU and the memory unit of the computer system, wherein the Consumer Status Score (CSS) calculator is configured to measure a first business rating tendency displayed by the searching consumer against a second business rating tendency displayed by the business evaluator; and a consumer rating calculator executed on the CPU and the memory unit of the computer system, wherein the consumer rating calculator is configured to provide a weighted value to a Consumer Given Rating (CSGR) for the particular business given by the business evaluator by incorporating the Searching Consumer to Consumer Match Score (SCCMS), wherein the weighted value is called a Consumer to Business Rating (CSBR). 3. The personalized consumer to business matchmaking system of claim 1 , further comprising a Business Rating Average (BRA) calculator executed on the CPU and the memory unit of the computer system, wherein the Business Rating Average (BRA) calculator is configured to provide a BRA value for the particular business rated by a plurality of consumers and an Average Match Score (AMS) calculator configured to provide an AMS value by averaging the SCBMS, the SCCMS, the CBMS, the CSS, and the CSBR.
| 0.5 |
16. A data processing system comprising: a processing system coupled to memory, the processing system configured to generate a graphical user interface (GUI) and execute instructions stored in the memory, the instructions to cause the processing system to: display a text input field within the GUI; receive text input from the text input field and generate search suggestions from the text input by searching through content and metadata of files, the search suggestions associated with the text input; display a field within the GUI that contains a set of selectable search suggestions generated from the text input, the selectable search suggestions including a scope and an entity; receive an input to select one of the selectable search suggestions; generate a tokenized search suggestion for the search suggestion selected by the input, the tokenized search suggestion including the scope and the entity of the search suggestion selected by the input; display a graphical representation of the tokenized search suggestion to replace the text input in the text input field, the graphical representation of the first tokenized search suggestion including a selectable scope field including a drop-down menu; and display an input adjacent to the selectable scope field, the input to search in the scope of the search suggestion selected by the input.
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16. A data processing system comprising: a processing system coupled to memory, the processing system configured to generate a graphical user interface (GUI) and execute instructions stored in the memory, the instructions to cause the processing system to: display a text input field within the GUI; receive text input from the text input field and generate search suggestions from the text input by searching through content and metadata of files, the search suggestions associated with the text input; display a field within the GUI that contains a set of selectable search suggestions generated from the text input, the selectable search suggestions including a scope and an entity; receive an input to select one of the selectable search suggestions; generate a tokenized search suggestion for the search suggestion selected by the input, the tokenized search suggestion including the scope and the entity of the search suggestion selected by the input; display a graphical representation of the tokenized search suggestion to replace the text input in the text input field, the graphical representation of the first tokenized search suggestion including a selectable scope field including a drop-down menu; and display an input adjacent to the selectable scope field, the input to search in the scope of the search suggestion selected by the input. 19. The system of claim 16 , including additional instructions to cause the processing system to: perform a first search using a first search query generated using the tokenized search suggestion; receive an indication to change the default suggestion scope to a second suggestion scope; and display a graphical representation of an updated tokenized search suggestion generated using the selected suggestion and the second suggestion scope.
| 0.5 |
13. The method of claim 12 , further comprising: performing an action associated with the target object based on the score.
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13. The method of claim 12 , further comprising: performing an action associated with the target object based on the score. 14. The method of claim 13 , wherein performing the action associated with the target object based on the calculated score comprises: comparing the calculated score with a plurality of ranges of scores, each range of scores associated with an action; and selecting an action corresponding to a range of scores that includes the calculated score.
| 0.883108 |
1. A method for creating an electronic database of disambiguated entity mentions from a corpus of electronic documents using a microprocessor, the method comprising: (a) executing on the microprocessor a data harvesting module to automatically extract entity mentions from the electronic documents in the corpus and parse the entity mentions into mention objects; (b) executing on the microprocessor a mention group creation module to create one or more mention groups by automatically grouping the mention objects together according to a distinguishing attribute common to a given class of mention objects; (c) selecting a mention group from the one or more mention groups for comparison processing; (d) executing on the microprocessor a collection of comparison modules that automatically (i) compares every mention object in the selected mention group with every other mention object in the selected mention group to produce a collection of comparison algorithm scores for every pair of mention objects in the selected mention group, and (ii) generates an overall confidence score for every pair of mention objects in the selected mention group based on the collection of comparison algorithm scores for said every pair of mention objects; (e) executing on the microprocessor an entity object creation module to create one or more new entity objects for the selected mention group by automatically (i) grouping together mention objects with other mention objects, based on the confidence scores of each pair of mention objects and a specified confidence threshold, wherein pairs of mention objects having a confidence score greater than or equal to the specified threshold are assigned to the same new entity object, and (ii) merging previously-created entity objects with other previously-created entity objects, based on the confidence scores of each pair of entity objects, and a specified confidence threshold, wherein pairs of entity objects having a confidence score greater than or equal to the specified threshold are assigned to the same new entity object; (f) storing said one or more new entity objects in the electronic database of disambiguated entity mentions; and (g) repeating steps (c) through (f) above until all of the one or more mention groups have been comparison processed.
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1. A method for creating an electronic database of disambiguated entity mentions from a corpus of electronic documents using a microprocessor, the method comprising: (a) executing on the microprocessor a data harvesting module to automatically extract entity mentions from the electronic documents in the corpus and parse the entity mentions into mention objects; (b) executing on the microprocessor a mention group creation module to create one or more mention groups by automatically grouping the mention objects together according to a distinguishing attribute common to a given class of mention objects; (c) selecting a mention group from the one or more mention groups for comparison processing; (d) executing on the microprocessor a collection of comparison modules that automatically (i) compares every mention object in the selected mention group with every other mention object in the selected mention group to produce a collection of comparison algorithm scores for every pair of mention objects in the selected mention group, and (ii) generates an overall confidence score for every pair of mention objects in the selected mention group based on the collection of comparison algorithm scores for said every pair of mention objects; (e) executing on the microprocessor an entity object creation module to create one or more new entity objects for the selected mention group by automatically (i) grouping together mention objects with other mention objects, based on the confidence scores of each pair of mention objects and a specified confidence threshold, wherein pairs of mention objects having a confidence score greater than or equal to the specified threshold are assigned to the same new entity object, and (ii) merging previously-created entity objects with other previously-created entity objects, based on the confidence scores of each pair of entity objects, and a specified confidence threshold, wherein pairs of entity objects having a confidence score greater than or equal to the specified threshold are assigned to the same new entity object; (f) storing said one or more new entity objects in the electronic database of disambiguated entity mentions; and (g) repeating steps (c) through (f) above until all of the one or more mention groups have been comparison processed. 16. The method of claim 1 , wherein: the electronic documents in the corpus comprise one or more records of an electronic database; and the data harvesting module comprises a database query tool that, when executed by the microprocessor, causes the microprocessor to extract and parse the entity mentions from said one or more records of the electronic database.
| 0.54481 |
10. The method of claim 9 , wherein the demographic profile reference comprises a URN of a demographic profile stored in at least one of a web portal, a server, a database and a local file, and wherein the electronic device communicates the response to the distribution server along with the demographic profile reference.
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10. The method of claim 9 , wherein the demographic profile reference comprises a URN of a demographic profile stored in at least one of a web portal, a server, a database and a local file, and wherein the electronic device communicates the response to the distribution server along with the demographic profile reference. 11. The method of claim 10 , wherein the electronic device communicates the response to the distribution server along with the demographic profile reference only when the questionnaire received from the distribution server is not accompanied by another demographic profile reference.
| 0.811019 |
1. A method executed by a processor, the method comprising: receiving a sample at a context-dependent combination of a Deep Belief Network (DBN) and a Hidden Markov Model (HMM), wherein the sample is a spoken utterance outputting, at the DBN, a posterior probability distribution over labeled senones; outputting, at the HMM, transition probabilities between the labeled senones, the transition probabilities based upon the posterior probability distribution over the labeled senones; and decoding the sample based at least in part upon the posterior probability distribution over the labeled senones and the transition probabilities between the labeled senones.
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1. A method executed by a processor, the method comprising: receiving a sample at a context-dependent combination of a Deep Belief Network (DBN) and a Hidden Markov Model (HMM), wherein the sample is a spoken utterance outputting, at the DBN, a posterior probability distribution over labeled senones; outputting, at the HMM, transition probabilities between the labeled senones, the transition probabilities based upon the posterior probability distribution over the labeled senones; and decoding the sample based at least in part upon the posterior probability distribution over the labeled senones and the transition probabilities between the labeled senones. 4. The method of claim 1 , further comprising, during a training phase for the combination of the DBN and the HMM, deriving the combination of the DBN and the HMM from a Gaussian Mixture Model (GMM)-HMM system.
| 0.600995 |
1. A method comprising: receiving input data, the input data being data for conversion from a phonetic representation or speech to written text characters of one or more words of a language, the input data having a plurality of data pieces; calculating, by a processor of a computer via an iterative process, a vector for the input data in a textual format of the language, the iterative process including starting with a first data piece of the input data converted to a selected textual format to form a current vector of the input data and iteratively updating the current vector with a next data piece of the input data using elements of the current vector until all the data pieces are converted into the textual format; selecting a subset of a plurality of documents based on a closeness of the vector for the input data to a plurality of vectors for text of the plurality of documents, the text of the plurality of documents being written in the language; determining a frequency of the text in the subset of the plurality of documents; and converting the input data to a representation of one or more written text characters of the language based on the frequency of the text in the subset of the plurality of documents.
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1. A method comprising: receiving input data, the input data being data for conversion from a phonetic representation or speech to written text characters of one or more words of a language, the input data having a plurality of data pieces; calculating, by a processor of a computer via an iterative process, a vector for the input data in a textual format of the language, the iterative process including starting with a first data piece of the input data converted to a selected textual format to form a current vector of the input data and iteratively updating the current vector with a next data piece of the input data using elements of the current vector until all the data pieces are converted into the textual format; selecting a subset of a plurality of documents based on a closeness of the vector for the input data to a plurality of vectors for text of the plurality of documents, the text of the plurality of documents being written in the language; determining a frequency of the text in the subset of the plurality of documents; and converting the input data to a representation of one or more written text characters of the language based on the frequency of the text in the subset of the plurality of documents. 3. The method of claim 1 , wherein the input data further comprises phonetic characters.
| 0.640687 |
14. The program product of claim 11, wherein the browser automatically catalogs the entry into a bookmark-folder list based on a cataloging criteria, wherein the bookmark-folder list comprises a plurality of bookmark folders.
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14. The program product of claim 11, wherein the browser automatically catalogs the entry into a bookmark-folder list based on a cataloging criteria, wherein the bookmark-folder list comprises a plurality of bookmark folders. 16. The program product of claim 14, wherein each of the plurality of bookmark folders contains a keyword field and a synonym-list field, wherein the browser determines the contents of the synonym-list field using the keyword field and a thesaurus, and wherein the cataloging criteria comprises the browser matching a word in the viewed page to a portion of the synonym-list field.
| 0.717139 |
40. A computer-implemented method of providing a graphical user interface for displaying search results and related information, the method comprising: under the control of one or more computer systems configured with executable instructions, providing for electronic display a search element that enables a user to submit a search query and a plurality of side by side panes, wherein each of the panes is configured to, responsive to submission of the search query, each display independent search related information or results, with at least a first one of the panes being arranged to display search results corresponding to an associated category of search information, different from other categories of search information corresponding to other panes, said associated category and at least one of said other categories selected from the group consisting of: general web search results, image search results, book search results, movie search results, map search results, local area search results, reference search results, dictionary search results, address based search results, news search results, diary search results, bookmark search results, search history search results and tracking number search results, wherein each of the panes is configured to display search results organized into one or more pages of search results; and providing for electronic display a page navigation widget that permits a user to cause all of the panes concurrently to display a different page of the one or more pages of search results within each pane if more than one page of search related information or results is available for any of the panes.
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40. A computer-implemented method of providing a graphical user interface for displaying search results and related information, the method comprising: under the control of one or more computer systems configured with executable instructions, providing for electronic display a search element that enables a user to submit a search query and a plurality of side by side panes, wherein each of the panes is configured to, responsive to submission of the search query, each display independent search related information or results, with at least a first one of the panes being arranged to display search results corresponding to an associated category of search information, different from other categories of search information corresponding to other panes, said associated category and at least one of said other categories selected from the group consisting of: general web search results, image search results, book search results, movie search results, map search results, local area search results, reference search results, dictionary search results, address based search results, news search results, diary search results, bookmark search results, search history search results and tracking number search results, wherein each of the panes is configured to display search results organized into one or more pages of search results; and providing for electronic display a page navigation widget that permits a user to cause all of the panes concurrently to display a different page of the one or more pages of search results within each pane if more than one page of search related information or results is available for any of the panes. 43. The computer-implemented method of claim 40 , wherein the pane navigation widget takes the form of a scroll bar.
| 0.651794 |
18. A method for embedding a message into a document, comprising: representing at least part of a document as a distance field including distance values; representing a symbol in a message to be embedded in the document as a modification of a subset of the values in the distance field; and modifying the subset of the values in the distance field according to the modification to produce a modified document, wherein the symbol in the message is embedded in the modified document, wherein steps of the method are performed by a processor.
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18. A method for embedding a message into a document, comprising: representing at least part of a document as a distance field including distance values; representing a symbol in a message to be embedded in the document as a modification of a subset of the values in the distance field; and modifying the subset of the values in the distance field according to the modification to produce a modified document, wherein the symbol in the message is embedded in the modified document, wherein steps of the method are performed by a processor. 23. The method of claim 18 , further comprising: representing the modified document with a modified distance field; detecting a modification associated with a subset of the modified distance values in the modified distance field; and determining the symbol of the message based on the modification.
| 0.539024 |
1. A system for problem solving, comprising: a brain agent configured to receive input data representing an input query from a peripheral device, the brain agent configured as processor-readable software code stored on a processor readable medium, the brain agent being configured to identify a predetermined data format associated with the input data and invokes a decomposition process associated with that predetermined data format, the decomposition step including outputting the data to a first intelligent agent configured as processor-readable software code stored on a computer readable medium, and the brain agent being configured to receive the input data in a textual form and conceptually parse the input data in textual form and a plurality of sub-queries; and a plurality of second intelligent agents, each configured to receive at least one of the plurality of sub-queries and the corresponding conceptually parsed text and provide responsive output to the brain agent based on the conceptually parsed text; the brain agent being further configured to generate an answer to the input query based upon at least the responsive output of the plurality of second intelligent agents.
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1. A system for problem solving, comprising: a brain agent configured to receive input data representing an input query from a peripheral device, the brain agent configured as processor-readable software code stored on a processor readable medium, the brain agent being configured to identify a predetermined data format associated with the input data and invokes a decomposition process associated with that predetermined data format, the decomposition step including outputting the data to a first intelligent agent configured as processor-readable software code stored on a computer readable medium, and the brain agent being configured to receive the input data in a textual form and conceptually parse the input data in textual form and a plurality of sub-queries; and a plurality of second intelligent agents, each configured to receive at least one of the plurality of sub-queries and the corresponding conceptually parsed text and provide responsive output to the brain agent based on the conceptually parsed text; the brain agent being further configured to generate an answer to the input query based upon at least the responsive output of the plurality of second intelligent agents. 5. The system as recited in claim 1 , further comprising: a personality agent configured as processor-readable software code stored on a processor readable medium, and wherein said brain agent is further adapted to selectively interact with said personality agent to interpret the input query and provide output in response to the input query.
| 0.555063 |
16. The system as set forth in claim 11 wherein the source reference data item recorder is configured to record a publication date for the one or more source reference data items.
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16. The system as set forth in claim 11 wherein the source reference data item recorder is configured to record a publication date for the one or more source reference data items. 17. The system as set forth in claim 16 wherein the monitor report generator is further configured to generate one or more reports selected from the group consisting of an electronic mail message, a text file, a binary data file, and a report on a printer.
| 0.946525 |
15. A system for determining sentiment from user-generated text content, the system comprising: one or more processors; and one or more memory resources storing instructions that, when executed by the one or more processors, cause the one or more processors to: analyze a user-generated text content, including determining (i) a subject of the user-generated text content, (ii) a sentiment score for individual terms that are present in the user-generated text content, and (iii) a sentiment value for the user-generated text content based at least in part on the determined sentiment score for the individual terms, wherein determining (iii) includes weighing the sentiment score for each of the individual terms based on a relationship between the term and the subject and a proximity of the term to the subject; and associate the sentiment value with the subject.
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15. A system for determining sentiment from user-generated text content, the system comprising: one or more processors; and one or more memory resources storing instructions that, when executed by the one or more processors, cause the one or more processors to: analyze a user-generated text content, including determining (i) a subject of the user-generated text content, (ii) a sentiment score for individual terms that are present in the user-generated text content, and (iii) a sentiment value for the user-generated text content based at least in part on the determined sentiment score for the individual terms, wherein determining (iii) includes weighing the sentiment score for each of the individual terms based on a relationship between the term and the subject and a proximity of the term to the subject; and associate the sentiment value with the subject. 16. The system of claim 15 , wherein the instructions, when executed by the one or more processors, further cause the one or more processors to store data that is based on the sentiment value in association with the determined subject.
| 0.509901 |
1. Non-transitory computer storage having stored thereon executable code that directs a computing system to detect and correct a writing problem in text by a process that comprises: searching a sentence of the text for at least one sign that indicates the possible occurrence or absence of a writing problem, the at least one sign comprising the word “what” and one or more of a verb unit and a “to be” verb; in response to determining that the word “what” is present, determining if one or more of a verb unit and a “to be” verb is present in the sentence; and selecting a proposed edit to suggest to a user, the proposed edit comprising deleting at least the word “what”.
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1. Non-transitory computer storage having stored thereon executable code that directs a computing system to detect and correct a writing problem in text by a process that comprises: searching a sentence of the text for at least one sign that indicates the possible occurrence or absence of a writing problem, the at least one sign comprising the word “what” and one or more of a verb unit and a “to be” verb; in response to determining that the word “what” is present, determining if one or more of a verb unit and a “to be” verb is present in the sentence; and selecting a proposed edit to suggest to a user, the proposed edit comprising deleting at least the word “what”. 10. The non-transitory computer storage of claim 1 , in combination with a computer system programmed with said executable code to identify writing problems and associated corrections.
| 0.645387 |
1. A spinal fixation system, comprising: at least two bone anchors having a bone-engaging portion and a rod receiving portion with opposed arms that receive a rod therebetween; a rod disposable between the opposed arms of the rod receiving portion of one of the at least two bone anchors; and a connecting plate having a distal surface that bears against a proximal terminal end surface of each of the opposed arms of the rod receiving portion of at least one of the bone anchors, the connecting plate connecting the at least two bone anchors.
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1. A spinal fixation system, comprising: at least two bone anchors having a bone-engaging portion and a rod receiving portion with opposed arms that receive a rod therebetween; a rod disposable between the opposed arms of the rod receiving portion of one of the at least two bone anchors; and a connecting plate having a distal surface that bears against a proximal terminal end surface of each of the opposed arms of the rod receiving portion of at least one of the bone anchors, the connecting plate connecting the at least two bone anchors. 15. The spinal fixation system of claim 1 , wherein the connecting plate is oriented at an angle in a range between about 20° and about 160° relative to the rod.
| 0.710398 |
9. A computer program product for distinguishing a business rule from a non-business rule in a computer program, the computer program product comprising: one or more computer-readable storage devices and program instructions stored on the one or more storage devices, the one or more computer-readable storage devices not being one or more signals or signal propagation media, the program instructions comprising: program instructions to identify a first rule in the computer program based on a conditional statement within the first rule; program instructions to determine whether the first rule performs an underlying operation of the program, the underlying operation being independent of a business function of the program, by determining whether the first rule includes a first key word which indicates the underlying operation, which is a housekeeping process, exception handling, error checking, data validation, parameter cleanup, a reservation of computer memory, or a buffer setup; program instructions to determine whether a sequence of program steps in the first rule matches a predetermined sequence of program steps indicative of the underlying operation of the program independent of the business function of the program; program instructions to, if the first rule includes the first key word or the sequence of program steps in the first rule matches the predetermined sequence of steps indicative of the underlying operation of the program independent of the business function of the program, determine the first rule is a non-business rule, or if the first rule does not include the first key word and no sequence of program steps in the first rule matches the predetermined sequence of program steps indicative of the underlying operation of the program independent of the business function of the program: program instructions to search the first rule and metadata of the first rule for a second key word which indicates part of a business transaction with a customer of a business using the computer program; program instructions to determine whether a sequence of program steps in the first rule matches a predetermined sequence of program steps indicative of a business rule; and program instructions to, if the first rule includes the second key word, the metadata of the first rule includes the second key word, or the sequence of program steps in the first rule matches the predetermined sequence of program steps indicative of the business rule, determine the first rule is the business rule, or if the first rule and the metadata of the first rule do not include the second key word and no sequence of program steps in the first rule matches the predetermined sequence of program steps indicative of the business rule, determine the first rule is not classifiable as the business rule or the non-business rule; program instructions to receive a first set of one or more semantic tags specifying a first candidate business rule in the computer program, the first candidate business rule being initially not classifiable as a first actual business rule or a first actual non-business rule; program instructions to, based on the first set of one or more semantic tags, determine the first candidate business rule is specified by a pattern expressed in a context-free grammar for a programming language of the computer program, the pattern specifying a code structure included in the first candidate business rule, the pattern being included in a class of an ontology, and the class identifying a concept of the programming language; program instructions to determine that a confidence level of the pattern is less than a first threshold, the confidence level indicating how likely the first candidate business rule is the first actual business rule, and the ontology associating the pattern with the confidence level; program instructions to, based on the confidence level being less than the first threshold, determine a lack of confidence in the first candidate business rule being the first actual business rule; program instructions to receive other sets of one or more semantic tags specifying other candidate business rules, each of the other sets of one or more semantic tags being identical to the first set of one or more semantic tags, and each of the other candidate business rules being not classifiable as an actual business rule or an actual non-business rule; program instructions to, based on the other sets of one or more semantic tags being identical to the first set of one or more semantic tags, determine the other candidate business rules are specified by the pattern that also specifies the first candidate business rule; program instructions to, based on the other candidate business rules being specified by the pattern that also specifies the first candidate business rule, determine that the other candidate business rules include the code structure specified by the pattern; program instructions to determine a count of candidate business rules among the first candidate business rule and the other candidate business rules that include the code structure specified by the pattern; program instructions to determine that the count of the candidate business rules exceeds a second threshold; program instructions to, based on the count of the candidate business rules exceeding the second threshold, increase the confidence level of the pattern which indicates an increase in a likelihood that the candidate business rules are actual business rules; program instructions to update the ontology to associate the pattern with the increased confidence level; program instructions to determine the increased confidence level of the pattern is greater than the first threshold; program instructions to, subsequent to the step of determining the increased confidence level is greater than the first threshold, receive a second set of one or more semantic tags specifying a second candidate business rule in the computer program or in another computer program; program instructions to determine that the second candidate business rule includes the code structure specified by the pattern and determine that the second set of one or more semantic tags matches the first set of one or more semantic tags; and program instructions to, based on the second candidate business rule including the code structure specified by the pattern, the second set of one or more semantic tags matching the first set of one or more semantic tags, and the updated ontology associating the pattern with the increased confidence level, automatically determine that the second candidate business rule is a second actual business rule, without a manual classification of the second candidate business rule as the second actual business rule by a human expert; and program instructions to display the second candidate business rule as the second actual business rule.
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9. A computer program product for distinguishing a business rule from a non-business rule in a computer program, the computer program product comprising: one or more computer-readable storage devices and program instructions stored on the one or more storage devices, the one or more computer-readable storage devices not being one or more signals or signal propagation media, the program instructions comprising: program instructions to identify a first rule in the computer program based on a conditional statement within the first rule; program instructions to determine whether the first rule performs an underlying operation of the program, the underlying operation being independent of a business function of the program, by determining whether the first rule includes a first key word which indicates the underlying operation, which is a housekeeping process, exception handling, error checking, data validation, parameter cleanup, a reservation of computer memory, or a buffer setup; program instructions to determine whether a sequence of program steps in the first rule matches a predetermined sequence of program steps indicative of the underlying operation of the program independent of the business function of the program; program instructions to, if the first rule includes the first key word or the sequence of program steps in the first rule matches the predetermined sequence of steps indicative of the underlying operation of the program independent of the business function of the program, determine the first rule is a non-business rule, or if the first rule does not include the first key word and no sequence of program steps in the first rule matches the predetermined sequence of program steps indicative of the underlying operation of the program independent of the business function of the program: program instructions to search the first rule and metadata of the first rule for a second key word which indicates part of a business transaction with a customer of a business using the computer program; program instructions to determine whether a sequence of program steps in the first rule matches a predetermined sequence of program steps indicative of a business rule; and program instructions to, if the first rule includes the second key word, the metadata of the first rule includes the second key word, or the sequence of program steps in the first rule matches the predetermined sequence of program steps indicative of the business rule, determine the first rule is the business rule, or if the first rule and the metadata of the first rule do not include the second key word and no sequence of program steps in the first rule matches the predetermined sequence of program steps indicative of the business rule, determine the first rule is not classifiable as the business rule or the non-business rule; program instructions to receive a first set of one or more semantic tags specifying a first candidate business rule in the computer program, the first candidate business rule being initially not classifiable as a first actual business rule or a first actual non-business rule; program instructions to, based on the first set of one or more semantic tags, determine the first candidate business rule is specified by a pattern expressed in a context-free grammar for a programming language of the computer program, the pattern specifying a code structure included in the first candidate business rule, the pattern being included in a class of an ontology, and the class identifying a concept of the programming language; program instructions to determine that a confidence level of the pattern is less than a first threshold, the confidence level indicating how likely the first candidate business rule is the first actual business rule, and the ontology associating the pattern with the confidence level; program instructions to, based on the confidence level being less than the first threshold, determine a lack of confidence in the first candidate business rule being the first actual business rule; program instructions to receive other sets of one or more semantic tags specifying other candidate business rules, each of the other sets of one or more semantic tags being identical to the first set of one or more semantic tags, and each of the other candidate business rules being not classifiable as an actual business rule or an actual non-business rule; program instructions to, based on the other sets of one or more semantic tags being identical to the first set of one or more semantic tags, determine the other candidate business rules are specified by the pattern that also specifies the first candidate business rule; program instructions to, based on the other candidate business rules being specified by the pattern that also specifies the first candidate business rule, determine that the other candidate business rules include the code structure specified by the pattern; program instructions to determine a count of candidate business rules among the first candidate business rule and the other candidate business rules that include the code structure specified by the pattern; program instructions to determine that the count of the candidate business rules exceeds a second threshold; program instructions to, based on the count of the candidate business rules exceeding the second threshold, increase the confidence level of the pattern which indicates an increase in a likelihood that the candidate business rules are actual business rules; program instructions to update the ontology to associate the pattern with the increased confidence level; program instructions to determine the increased confidence level of the pattern is greater than the first threshold; program instructions to, subsequent to the step of determining the increased confidence level is greater than the first threshold, receive a second set of one or more semantic tags specifying a second candidate business rule in the computer program or in another computer program; program instructions to determine that the second candidate business rule includes the code structure specified by the pattern and determine that the second set of one or more semantic tags matches the first set of one or more semantic tags; and program instructions to, based on the second candidate business rule including the code structure specified by the pattern, the second set of one or more semantic tags matching the first set of one or more semantic tags, and the updated ontology associating the pattern with the increased confidence level, automatically determine that the second candidate business rule is a second actual business rule, without a manual classification of the second candidate business rule as the second actual business rule by a human expert; and program instructions to display the second candidate business rule as the second actual business rule. 11. The computer program product of claim 9 , further comprising: program instructions, stored on at least one of the one or more storage devices, to determine, for each time a sequence of program steps included in a second rule occurs in multiple computer programs, the sequence of program steps included in the second rule includes a conditional statement but does not match a predetermined sequence of program steps indicative of the business rule, does not match a predetermined sequence of program steps indicative of an underlying operation of a program included in the multiple programs independent of a business function of the program included in the multiple programs, does not include a key word indicative of the business rule, and does not include a key word indicative of the underlying operation of the program included in the multiple programs independent of the business function of the program included in the multiple programs; program instructions, stored on at least one of the one or more storage devices, to determine a number of times the sequence of program steps in the second rule occurs in the multiple computer programs and determine the number exceeds a predetermined threshold; program instructions, stored on at least one of the one or more storage devices, to change, based on the number exceeding the threshold, a classification of the second rule from being unclassifiable as the business rule or as a rule that is not any business rule to being classified as the rule that is not any business rule; program instructions, stored on at least one of the one or more storage devices, to identify, subsequent to a change of the classification by an execution of the program instructions to change, the second rule in another computer program; and program instructions, stored on at least one of the one or more storage devices, to automatically determine, based on the changed classification, the identified second rule is the rule that is not any business rule.
| 0.553874 |
20. The method of claim 1, wherein the training data comprises individual speech samples, each of which represents a single speech element uttered by a single speaker.
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20. The method of claim 1, wherein the training data comprises individual speech samples, each of which represents a single speech element uttered by a single speaker. 23. The computer program of claim 20, further comprising instructions for causing the processor to analyze the training data to designate different portions of the training data as being related to different speech elements.
| 0.889753 |
5. The method of claim 1 , further comprising: updating the context-sensitive content based on information obtained from the human subject.
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5. The method of claim 1 , further comprising: updating the context-sensitive content based on information obtained from the human subject. 6. The method of claim 5 , wherein the information is obtained from the human subject by querying the human subject with one or more episodic questions.
| 0.973364 |
6. An apparatus for retracing a deposited counterfeit banknote, comprising: a control device configured to store characteristic data of deposited banknotes together with data of a depositor of said banknotes; a testing device in communication with said control device, the testing device being configured to recognize a banknote of said deposited banknotes as a counterfeit banknote and to determine and forward characteristic data of said counterfeit banknote to said control device; wherein said control device is configured to receive said characteristic data of a counterfeit banknote and to identify a depositor of the counterfeit banknote by comparing the stored characteristic data with the received characteristic data of the counterfeit banknote.
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6. An apparatus for retracing a deposited counterfeit banknote, comprising: a control device configured to store characteristic data of deposited banknotes together with data of a depositor of said banknotes; a testing device in communication with said control device, the testing device being configured to recognize a banknote of said deposited banknotes as a counterfeit banknote and to determine and forward characteristic data of said counterfeit banknote to said control device; wherein said control device is configured to receive said characteristic data of a counterfeit banknote and to identify a depositor of the counterfeit banknote by comparing the stored characteristic data with the received characteristic data of the counterfeit banknote. 9. The apparatus according to claim 6 , wherein the characteristic data are printed images or serial numbers.
| 0.668478 |
33. The computer system of claim 14 wherein the query answering module is operable to facilitate transmission of the factual knowledge in response to a request transmitted from a first one of the computers to a second one of the computers.
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33. The computer system of claim 14 wherein the query answering module is operable to facilitate transmission of the factual knowledge in response to a request transmitted from a first one of the computers to a second one of the computers. 34. The computer system of claim 33 wherein the query answering module is operable to transmit more of the factual knowledge than requested thereby transmitting commercially valuable information in the network.
| 0.876703 |
1. A method comprising: receiving a string from an application at a first computing device; generating a first plurality of string predictions based on the received string by the first computing device, wherein each string prediction comprises a string and a confidence value and each string comprises a phrase that has been previously entered in response to the received string; providing one or more of the strings of the first plurality of string predictions according to the associated confidence values by the first computing device; receiving an indication of selection of one of the provided one or more strings by the first computing device; and in response to the indication of selection, providing the selected string as an input to the application by the first computing device.
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1. A method comprising: receiving a string from an application at a first computing device; generating a first plurality of string predictions based on the received string by the first computing device, wherein each string prediction comprises a string and a confidence value and each string comprises a phrase that has been previously entered in response to the received string; providing one or more of the strings of the first plurality of string predictions according to the associated confidence values by the first computing device; receiving an indication of selection of one of the provided one or more strings by the first computing device; and in response to the indication of selection, providing the selected string as an input to the application by the first computing device. 3. The method of claim 1 , wherein generating the first plurality of string predictions based on the received string comprises generating the first plurality of string predictions based on the received string and at least one prediction model.
| 0.555681 |
5. The method of claim 1 wherein determining comprises predicting an average precision of the search query using a combination of the features of the temporal profile.
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5. The method of claim 1 wherein determining comprises predicting an average precision of the search query using a combination of the features of the temporal profile. 6. The method of claim 5 wherein determining comprises predicting an average precision of the search query using a combination of the features of the temporal profile and a content feature.
| 0.916919 |
1. A computer system comprising: one or more processors; one or more computer readable memories; and one or more non-transitory computer readable storage mediums, wherein program instructions are stored on at least one of the one or more non-transitory storage mediums for execution by at least one of the one or more processors via at least one of the one or more computer readable memories to perform a method comprising: associating a first non-contextual data object with a first context object to define a first synthetic context-based object, wherein the first non-contextual data object describes multiple types of persons, wherein the first context object provides a context that identifies a specific type of person from the multiple types of persons, and wherein the first context object further describes a location of a computer that is being used by a requester of data as being a public Wi-Fi hot spot that provides the computer with access to a network; associating the first synthetic context-based object with at least one specific data store in a data structure; receiving a string of binary data that describes a request, from the requester, for data from said at least one specific data store in the data structure; determining the context according to the physical location of the computer being used, by the requester, to send the request to a security module; generating a new synthetic context-based object for the requester; determining whether the new synthetic context-based object matches the first synthetic context-based object; in response to determining that the new synthetic context-based object matches the first synthetic context-based object, locating, via the first synthetic context-based object, said at least one specific data store; providing the requester access to said at least one specific data store; constructing a dimensionally constrained hierarchical synthetic context-based object library for multiple synthetic context-based objects, wherein synthetic context-based objects within a same dimension of the dimensionally constrained hierarchical synthetic context-based object library share data from a same non-contextual data object, and wherein synthetic context-based objects within the same dimension of the dimensionally constrained hierarchical synthetic context-based object library contain disparate data from different context objects; receiving the request for data from at least one data store that is associated with synthetic context-based objects within the same dimension of the dimensionally constrained hierarchical synthetic context-based object library; receiving a time window for receiving the data from said at least one data store that is associated with synthetic context-based objects within the same dimension of the dimensionally constrained hierarchical synthetic context-based object library, wherein the time window describes an amount of time that the requester of data is willing to wait for at least one data store that is associated with synthetic context-based objects within the same dimension of the dimensionally constrained hierarchical synthetic context-based object library; determining a security level of the requester based on the time window received from the requester, wherein a first time window that the requester to willing to wait for the at least one data store is longer than a second time window that the requester is willing to wait for the at least one data store, and wherein the first time window is indicative of a higher security level for the requester than the second time window; matching, based on the time window for the requester, the security level of the requester to data from said at least one specific data store that is associated with synthetic context-based objects within the same dimension of the dimensionally constrained hierarchical synthetic context-based object library; and returning, to the requester, data from said at least one specific data store that is associated with synthetic context-based objects within the same dimension of the dimensionally constrained hierarchical synthetic context-based object library and that matches the security level of the requester.
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1. A computer system comprising: one or more processors; one or more computer readable memories; and one or more non-transitory computer readable storage mediums, wherein program instructions are stored on at least one of the one or more non-transitory storage mediums for execution by at least one of the one or more processors via at least one of the one or more computer readable memories to perform a method comprising: associating a first non-contextual data object with a first context object to define a first synthetic context-based object, wherein the first non-contextual data object describes multiple types of persons, wherein the first context object provides a context that identifies a specific type of person from the multiple types of persons, and wherein the first context object further describes a location of a computer that is being used by a requester of data as being a public Wi-Fi hot spot that provides the computer with access to a network; associating the first synthetic context-based object with at least one specific data store in a data structure; receiving a string of binary data that describes a request, from the requester, for data from said at least one specific data store in the data structure; determining the context according to the physical location of the computer being used, by the requester, to send the request to a security module; generating a new synthetic context-based object for the requester; determining whether the new synthetic context-based object matches the first synthetic context-based object; in response to determining that the new synthetic context-based object matches the first synthetic context-based object, locating, via the first synthetic context-based object, said at least one specific data store; providing the requester access to said at least one specific data store; constructing a dimensionally constrained hierarchical synthetic context-based object library for multiple synthetic context-based objects, wherein synthetic context-based objects within a same dimension of the dimensionally constrained hierarchical synthetic context-based object library share data from a same non-contextual data object, and wherein synthetic context-based objects within the same dimension of the dimensionally constrained hierarchical synthetic context-based object library contain disparate data from different context objects; receiving the request for data from at least one data store that is associated with synthetic context-based objects within the same dimension of the dimensionally constrained hierarchical synthetic context-based object library; receiving a time window for receiving the data from said at least one data store that is associated with synthetic context-based objects within the same dimension of the dimensionally constrained hierarchical synthetic context-based object library, wherein the time window describes an amount of time that the requester of data is willing to wait for at least one data store that is associated with synthetic context-based objects within the same dimension of the dimensionally constrained hierarchical synthetic context-based object library; determining a security level of the requester based on the time window received from the requester, wherein a first time window that the requester to willing to wait for the at least one data store is longer than a second time window that the requester is willing to wait for the at least one data store, and wherein the first time window is indicative of a higher security level for the requester than the second time window; matching, based on the time window for the requester, the security level of the requester to data from said at least one specific data store that is associated with synthetic context-based objects within the same dimension of the dimensionally constrained hierarchical synthetic context-based object library; and returning, to the requester, data from said at least one specific data store that is associated with synthetic context-based objects within the same dimension of the dimensionally constrained hierarchical synthetic context-based object library and that matches the security level of the requester. 6. The computer system of claim 1 , wherein said at least one specific data store is owned by an enterprise, and wherein the method further comprises: further determining the context according to whether the requester is a full time employee of the enterprise, a contract employee of the enterprise, or a non-employee of the enterprise.
| 0.55932 |
2. The wireless communications device of claim 1 , further operations comprising determining whether the available voicemail comprises the keyword.
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2. The wireless communications device of claim 1 , further operations comprising determining whether the available voicemail comprises the keyword. 4. The wireless communications device of claim 2 , further operations comprising evaluating the transcribed portion of the available voicemail to determine whether the keyword is present in the transcribed portion.
| 0.939148 |
18. A non-transitory computer-readable storage medium including one or more sequences of one or more instructions which, when executed by one or more processors, cause an apparatus to at least perform: receiving a user input that is drawn over a map user interface to delineate a route, a geographical area, or a combination thereof depicted in the map user interface; identifying contextual information associated with mapping data of the route, the geographical area, or a combination thereof; determining one or more functions of one or more services based on the contextual information; and initiating a presentation of the one or more functions as one or more contextual menu options of a contextual menu, wherein the contextual menu is rendered over the map user interface.
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18. A non-transitory computer-readable storage medium including one or more sequences of one or more instructions which, when executed by one or more processors, cause an apparatus to at least perform: receiving a user input that is drawn over a map user interface to delineate a route, a geographical area, or a combination thereof depicted in the map user interface; identifying contextual information associated with mapping data of the route, the geographical area, or a combination thereof; determining one or more functions of one or more services based on the contextual information; and initiating a presentation of the one or more functions as one or more contextual menu options of a contextual menu, wherein the contextual menu is rendered over the map user interface. 20. The non-transitory computer-readable storage medium of claim 18 , wherein the contextual information includes weather information.
| 0.651795 |
16. A system for identifying an Internet domain as potentially malicious comprising: a processor; and a memory having instructions that, when executed by the processor, cause the processor to perform operations comprising: identifying unique name servers that query the domain during a first day, identifying unique name servers that query the domain during a second day, determining, by a processor, the similarity of the name servers that queried the domain during the first day and the name servers that queried the domain during the second day, determining, by the processor, that the similarity meets a predetermined threshold; identifying the domain as one of malicious and potentially malicious in response to determining that the similarity of the servers meets the predetermined threshold; determining a confidence level based on a difference between the similarity of the servers and the predetermined threshold; and automatically adding the domain to a blacklist when the confidence level is high.
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16. A system for identifying an Internet domain as potentially malicious comprising: a processor; and a memory having instructions that, when executed by the processor, cause the processor to perform operations comprising: identifying unique name servers that query the domain during a first day, identifying unique name servers that query the domain during a second day, determining, by a processor, the similarity of the name servers that queried the domain during the first day and the name servers that queried the domain during the second day, determining, by the processor, that the similarity meets a predetermined threshold; identifying the domain as one of malicious and potentially malicious in response to determining that the similarity of the servers meets the predetermined threshold; determining a confidence level based on a difference between the similarity of the servers and the predetermined threshold; and automatically adding the domain to a blacklist when the confidence level is high. 18. The system of claim 16 , wherein the operations further comprise: determining a confidence level based on a difference between the similarity and the predetermined threshold; and limiting traffic to the domain when the confidence level is not high.
| 0.727554 |
1. A method for providing at least one client access to a network semantic graph distributed among a plurality of semantic servers wherein the network semantic graph comprises concept instances and relations between the concept instances, the method comprising: receiving first data including semantically distributed annotations from distributed data sources in communication with the plurality of semantic servers; based on the first data including the annotations, linking the concept instances using the relations; storing the concept instances and relations as a local semantic graph comprising a part of the network semantic graph; creating at least one subscription of interest over the network semantic graph in response to a request from the at least one client; collecting second data from the distributed data sources based on the at least one subscription; semantically annotating the second data; updating the local semantic graph based on the semantic annotation; sending alerts to the at least one client based on updates to the local semantic graph matching the at least one subscription of the at least one client.
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1. A method for providing at least one client access to a network semantic graph distributed among a plurality of semantic servers wherein the network semantic graph comprises concept instances and relations between the concept instances, the method comprising: receiving first data including semantically distributed annotations from distributed data sources in communication with the plurality of semantic servers; based on the first data including the annotations, linking the concept instances using the relations; storing the concept instances and relations as a local semantic graph comprising a part of the network semantic graph; creating at least one subscription of interest over the network semantic graph in response to a request from the at least one client; collecting second data from the distributed data sources based on the at least one subscription; semantically annotating the second data; updating the local semantic graph based on the semantic annotation; sending alerts to the at least one client based on updates to the local semantic graph matching the at least one subscription of the at least one client. 8. The method of claim 1 , wherein a data source is represented in the semantic graph as a concept instance with at least one source identifier associated therewith that is available when accessing the concept instance in the semantic graph.
| 0.650559 |
15. An apparatus comprising: means for tracking a location of a pen instrument when the pen instrument is used to modify a physical document, wherein sensors within the pen instrument track the location of the pen instrument; means for generating signals describing movement of the pen instrument based on the location of the pen instrument; means for transmitting the signals to a receiving device, wherein the signals are used to modify an electronic document corresponding to the physical document, and further wherein strokes made with the pen instrument are displayed when the electronic document is displayed.
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15. An apparatus comprising: means for tracking a location of a pen instrument when the pen instrument is used to modify a physical document, wherein sensors within the pen instrument track the location of the pen instrument; means for generating signals describing movement of the pen instrument based on the location of the pen instrument; means for transmitting the signals to a receiving device, wherein the signals are used to modify an electronic document corresponding to the physical document, and further wherein strokes made with the pen instrument are displayed when the electronic document is displayed. 21. The apparatus of claim 15 wherein the means for tracking a location of a pen instrument comprises means for receiving an infrared signal from at least two infrared radiating scanning sources located at prescribed positions external to the pen instrument, the scanning sources to illuminate the pen instrument, wherein the pen instrument determines movements based, at least in part, on the infrared signals.
| 0.530462 |
12. A computer-implemented method of providing a personalized electronic magazine to a user, comprising: establishing correspondences between individual ones of a plurality of content items and individual ones of a plurality of available topics; generating a plurality of profile topics responsive to automatically determined interests of the user; automatically selecting magazine items from the content items responsive to correspondence between the available topics and the profile topics; assigning scores to the profile topics, each of the scores assigned to a corresponding topic of the profile topics, each of the scores assigned based on a number of times that the user has consumed content items corresponding to one of the profile topics over a time period; periodically updating the scores by decaying the scores using a pre-defined decay function to produce updated scores; comparing the updated scores to determine a stale topic from the profile topics, the stale topic having a lower score than other topics from the profile topics; and removing the stale topic from the profile topics.
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12. A computer-implemented method of providing a personalized electronic magazine to a user, comprising: establishing correspondences between individual ones of a plurality of content items and individual ones of a plurality of available topics; generating a plurality of profile topics responsive to automatically determined interests of the user; automatically selecting magazine items from the content items responsive to correspondence between the available topics and the profile topics; assigning scores to the profile topics, each of the scores assigned to a corresponding topic of the profile topics, each of the scores assigned based on a number of times that the user has consumed content items corresponding to one of the profile topics over a time period; periodically updating the scores by decaying the scores using a pre-defined decay function to produce updated scores; comparing the updated scores to determine a stale topic from the profile topics, the stale topic having a lower score than other topics from the profile topics; and removing the stale topic from the profile topics. 13. The computer-implemented method of claim 12 , further comprising managing how many of the content items are identified as corresponding to each of the plurality of available topics.
| 0.608406 |
1. A system that converts text to speech, the system comprising: a response engine to generate a response text and a response intent based on user input; a non-lexical cue insertion engine to: receive the response text and the response intent representative of intended meaning to be conveyed by non-lexical cues; determine insertion points of non-lexical cues based on the received response intent; and insert a non-lexical cue at the insertion point within the response text to generate augmented text; and a speech synthesizer to synthesize speech from the augmented text using speech units associated with the response text and the inserted response intent.
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1. A system that converts text to speech, the system comprising: a response engine to generate a response text and a response intent based on user input; a non-lexical cue insertion engine to: receive the response text and the response intent representative of intended meaning to be conveyed by non-lexical cues; determine insertion points of non-lexical cues based on the received response intent; and insert a non-lexical cue at the insertion point within the response text to generate augmented text; and a speech synthesizer to synthesize speech from the augmented text using speech units associated with the response text and the inserted response intent. 2. The system of claim 1 , further comprising a response engine to analyze the user input and the user input intent, and to select the response text and the response intent based on the user input and the user input intent.
| 0.66458 |
16. A computerized system for determining when to transfer a user from an automated service to a live agent comprising: a) an interactive voice response system (IVR); b) a monitoring module; wherein i) said user interacts with said IVR; ii) said monitoring module evaluates, after each turn in said IVR, a probability that said user's interaction with the IVR is good and a probability that said user's interaction with the IVR is bad; iii) said monitoring module signals an alarm to bring in a human agent if a log of the ratio of said good probability over said bad probability is below a predetermined threshold; iv) said monitoring module evaluates the probability that the user's interaction with the IVR is good using P(x|LM good ); v) the monitoring module evaluates the probability that the user's interaction with the IVR is bad using P(x|LM bad ); vi) x is a set of responses made by the user during the interaction; vii) LM good is a first classification model trained using records of one or more previous interactions classified as good; viii) LM bad is a second classification model trained using records of one or more previous interactions classified as bad; and ix) said one or more previous interactions classified as good and said one or more previous interactions classified as bad comprise, at least, prompts provided by the interactive voice response system, transcriptions of statements by a caller derived from an automatic speech recognizer, meanings ascribed to statements made by the caller, and confidence scores for the transcriptions.
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16. A computerized system for determining when to transfer a user from an automated service to a live agent comprising: a) an interactive voice response system (IVR); b) a monitoring module; wherein i) said user interacts with said IVR; ii) said monitoring module evaluates, after each turn in said IVR, a probability that said user's interaction with the IVR is good and a probability that said user's interaction with the IVR is bad; iii) said monitoring module signals an alarm to bring in a human agent if a log of the ratio of said good probability over said bad probability is below a predetermined threshold; iv) said monitoring module evaluates the probability that the user's interaction with the IVR is good using P(x|LM good ); v) the monitoring module evaluates the probability that the user's interaction with the IVR is bad using P(x|LM bad ); vi) x is a set of responses made by the user during the interaction; vii) LM good is a first classification model trained using records of one or more previous interactions classified as good; viii) LM bad is a second classification model trained using records of one or more previous interactions classified as bad; and ix) said one or more previous interactions classified as good and said one or more previous interactions classified as bad comprise, at least, prompts provided by the interactive voice response system, transcriptions of statements by a caller derived from an automatic speech recognizer, meanings ascribed to statements made by the caller, and confidence scores for the transcriptions. 18. A computerized system as claimed in claim 16 wherein said monitoring module evaluates said probabilities based on a boostexter classifier in an iterative algorithm.
| 0.50093 |
1. A machine implemented method for automatically determining an optimal execution strategy for a request comprising query, update or transaction operations on a distributed database system having a plurality of data site, with each site having a computer and data storage facility, and said sites are interconnected by communication lines, said machine implemented method comprising the steps of: A. inputting an ad hoc relational query or update request from a first computer process that formulates the relational query or update request as a compacted tree having lead nodes that are select, project or join operators and non-leaf nodes that are union, intersection, difference, delete, modify or insert operators to a second computer process which thereafter performs step B; B. for each node in the compacted tree perform the following starting at the root node: 1. if the current node is a leaf node perform the following: a. materialization planning to choose the data sites from which data is to be accessed; b. local process planning to determine which operations can be processed locally at each site determined by materialization planning and estimate parameters of resultant data from the local operations; c. non-local process planning to determine strategy for remaining operations which can not be performed locally without transmission of data between sites; d. execution strategy building to build an execution strategy for each data site in the distributed database system at which data is processed by access or manipulation; 2.
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1. A machine implemented method for automatically determining an optimal execution strategy for a request comprising query, update or transaction operations on a distributed database system having a plurality of data site, with each site having a computer and data storage facility, and said sites are interconnected by communication lines, said machine implemented method comprising the steps of: A. inputting an ad hoc relational query or update request from a first computer process that formulates the relational query or update request as a compacted tree having lead nodes that are select, project or join operators and non-leaf nodes that are union, intersection, difference, delete, modify or insert operators to a second computer process which thereafter performs step B; B. for each node in the compacted tree perform the following starting at the root node: 1. if the current node is a leaf node perform the following: a. materialization planning to choose the data sites from which data is to be accessed; b. local process planning to determine which operations can be processed locally at each site determined by materialization planning and estimate parameters of resultant data from the local operations; c. non-local process planning to determine strategy for remaining operations which can not be performed locally without transmission of data between sites; d. execution strategy building to build an execution strategy for each data site in the distributed database system at which data is processed by access or manipulation; 2. 3. The method of claim 1 wherein the step of local process planning for a leaf node comprises the steps of: A. determining the select, project and join operations that can be performed on base relations or temporary relations that reside at the same node; and B. estimating parameters of temporary relations produced by select, project and join operators using the parameter of the base relations.
| 0.693884 |
11. The system of claim 10 wherein the view panel displays a view configuration that provides various options by which to view (or sort) the selected managed objects.
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11. The system of claim 10 wherein the view panel displays a view configuration that provides various options by which to view (or sort) the selected managed objects. 13. The system of claim 11 wherein the display panel displays the selected managed objects in a view configuration format chosen from the view configuration of the view panel.
| 0.937989 |
1. A computer-implemented method, comprising: selecting a regular expression from a set of regular expressions used to identify a text portion of a message; retrieving a set of one or more configuration parameters arranged to limit expansion of features of the regular expression into a set of potential regular expression key terms, at least one configuration parameter operative to determine which feature to expand and which feature not to expand; identifying a set of one or more features within the regular expression enabled by the set of configuration parameters; and generating a set of one or more identified regular expression key terms from the enabled features of the regular expression.
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1. A computer-implemented method, comprising: selecting a regular expression from a set of regular expressions used to identify a text portion of a message; retrieving a set of one or more configuration parameters arranged to limit expansion of features of the regular expression into a set of potential regular expression key terms, at least one configuration parameter operative to determine which feature to expand and which feature not to expand; identifying a set of one or more features within the regular expression enabled by the set of configuration parameters; and generating a set of one or more identified regular expression key terms from the enabled features of the regular expression. 11. The computer-implemented method of claim 1 , comprising extracting groupings with alternations from the regular expression.
| 0.706173 |
3. The method of claim 1 , wherein the attribute identified by the symbol is at least one of FORM, MEANING and specific content of the recognized gesture, the specific content being represented by the SEM placeholder symbol in the sequence of symbols.
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3. The method of claim 1 , wherein the attribute identified by the symbol is at least one of FORM, MEANING and specific content of the recognized gesture, the specific content being represented by the SEM placeholder symbol in the sequence of symbols. 12. The method of claim 3 , wherein when the attribute identified by the symbol is specific content, the recognition result comprises at least one of entity identifiers and points selected by a user.
| 0.864202 |
12. The method of claim 1 , wherein the second type of input is the swipe input at or near the key of the at least one subsequent candidate input character of the virtual keyboard.
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12. The method of claim 1 , wherein the second type of input is the swipe input at or near the key of the at least one subsequent candidate input character of the virtual keyboard. 13. The method of claim 12 , wherein the swipe input towards the displayed input field consists of an “up” swipe.
| 0.961564 |
13. The method of claim 8 , wherein indicating expected behavior further comprises, within a host computing system, receiving operating state information during simulation for at least one component that corresponds to an instrumented HDL model that is instantiated within the programmable logic device.
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13. The method of claim 8 , wherein indicating expected behavior further comprises, within a host computing system, receiving operating state information during simulation for at least one component that corresponds to an instrumented HDL model that is instantiated within the programmable logic device. 14. The method of claim 13 , further comprising storing the operating state information within the host computing system.
| 0.947793 |
1. A computer-implemented method, comprising: receiving, at a computing device, a search query from a first user, the first user associated with a computer-implemented social graph that includes a plurality of members socially connected to the first user through the computer-implemented social graph; receiving, at the computing device, search results responsive to the search query, the search results each associated with a respective electronic document stored in one or more computer-readable storage media; determining that a particular electronic document is associated with a plurality of endorsements; identifying a subset of members of the plurality of members socially connected to the first user through the computer-implemented social graph, each member of the subset of members respectively associated with an endorsement of the plurality of endorsements; identifying an affinity of each member of the subset of the members with respect to the particular electronic document; selecting a subset of the plurality of endorsements based on the affinity of each member of the subset of the members with respect to the particular electronic document; and transmitting instructions to display the search results to the first user, the instructions comprising instructions to display one or more endorsement annotations associated with the subset of the plurality of endorsements proximate to the search result associated with the particular electronic document, the one or more endorsement annotations each including a text snippet describing the endorsement associated with the endorsement annotation, the text snippet including i) an identification of the respective member of the subset of members, ii) text indicating a service used by the respective member of the subset of members to generate the endorsement, and iii) a web-based link associated with the endorsement to direct the first user to the content of the endorsement within the service, wherein the text indicating the service used by the respective member of the subset of members to generate the endorsement is distinct from the web-based link.
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1. A computer-implemented method, comprising: receiving, at a computing device, a search query from a first user, the first user associated with a computer-implemented social graph that includes a plurality of members socially connected to the first user through the computer-implemented social graph; receiving, at the computing device, search results responsive to the search query, the search results each associated with a respective electronic document stored in one or more computer-readable storage media; determining that a particular electronic document is associated with a plurality of endorsements; identifying a subset of members of the plurality of members socially connected to the first user through the computer-implemented social graph, each member of the subset of members respectively associated with an endorsement of the plurality of endorsements; identifying an affinity of each member of the subset of the members with respect to the particular electronic document; selecting a subset of the plurality of endorsements based on the affinity of each member of the subset of the members with respect to the particular electronic document; and transmitting instructions to display the search results to the first user, the instructions comprising instructions to display one or more endorsement annotations associated with the subset of the plurality of endorsements proximate to the search result associated with the particular electronic document, the one or more endorsement annotations each including a text snippet describing the endorsement associated with the endorsement annotation, the text snippet including i) an identification of the respective member of the subset of members, ii) text indicating a service used by the respective member of the subset of members to generate the endorsement, and iii) a web-based link associated with the endorsement to direct the first user to the content of the endorsement within the service, wherein the text indicating the service used by the respective member of the subset of members to generate the endorsement is distinct from the web-based link. 3. The method of claim 1 , further comprising: receiving, at the computing device, a selection of a link associated with the identification of one of the other users; and filtering the search results to include a subset of the search results based on the selected other user.
| 0.81866 |
5. The method according to claim 1 , wherein the at least one hardware processor further performs: creating a largest composite sets of evidences G according to a union of a plurality of composite sets of evidences G 1 v , subject to the relationship constraints κ , which support the composite rule L 0 v ; and computing a composite object ω 2 v indicative according to a deductive reasoning of a second validity value for target rule L.
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5. The method according to claim 1 , wherein the at least one hardware processor further performs: creating a largest composite sets of evidences G according to a union of a plurality of composite sets of evidences G 1 v , subject to the relationship constraints κ , which support the composite rule L 0 v ; and computing a composite object ω 2 v indicative according to a deductive reasoning of a second validity value for target rule L. 6. The method according to claim 5 , wherein the at least one hardware processor further performs: obtaining a set complement of the largest composite sets of evidences G that supports negation of the target rule L; and computing a composite object ω 2 p indicative according to a deductive reasoning of a second plausibility value for the target rule L.
| 0.876423 |
5. The method of claim 1 wherein the learning bracelet attached to the student's arm is shaped like a cartoon character or an animal.
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5. The method of claim 1 wherein the learning bracelet attached to the student's arm is shaped like a cartoon character or an animal. 10. The method of claim 5 wherein the learning bracelet attached to the student is shaped like a cartoon character or an animal.
| 0.946429 |
30. The system of claim 29 , wherein the criteria include an event triggering condition upon which the user-selected conversation is brought back to the first list of conversations.
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30. The system of claim 29 , wherein the criteria include an event triggering condition upon which the user-selected conversation is brought back to the first list of conversations. 32. The system of claim 30 , wherein the event triggering condition is satisfied by an event selected from the list consisting of: a message from a predetermined sender is associated with the user-selected conversation, a message that specifies the user as a primary recipient is associated with the user-selected conversation, and a message that contains a specified word or phrase is associated with the user-selected conversation.
| 0.898036 |
9. A machine learning system that quantifies relevancy of a document to a training corpus, the system comprising: at least one server comprising a processor configured to execute instructions that reside in memory, the instructions comprising: a classifier module that: calculates an internal best match score for a relevant document relative to each training example in the training corpus by: determining cosine distances between the document and training examples in the training corpus relative to term frequency-inverse document frequency weights associated with the training examples; and determines the training example having a closest cosine distance to the relevant document; and a user interface module that outputs the training example having the closest cosine distance to the relevant document; wherein the classifier module determines the training example having the closest cosine distance to the relevant document by ranking the training examples by stretching the internal best match scores for the training examples linearly to cover a complete unit interval.
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9. A machine learning system that quantifies relevancy of a document to a training corpus, the system comprising: at least one server comprising a processor configured to execute instructions that reside in memory, the instructions comprising: a classifier module that: calculates an internal best match score for a relevant document relative to each training example in the training corpus by: determining cosine distances between the document and training examples in the training corpus relative to term frequency-inverse document frequency weights associated with the training examples; and determines the training example having a closest cosine distance to the relevant document; and a user interface module that outputs the training example having the closest cosine distance to the relevant document; wherein the classifier module determines the training example having the closest cosine distance to the relevant document by ranking the training examples by stretching the internal best match scores for the training examples linearly to cover a complete unit interval. 10. The machine learning system according to claim 9 , wherein each of the training examples has been converted into a high-dimensional feature space using term frequencies.
| 0.538261 |
13. A hierarchical role based access control (HRBAC) modeling system, the system comprising: an environment interface module to receive an indication of existing permissions granted to users in an organizational environment; a role mining module in communication with the environment interface module to analyze the permissions to create permission characteristics and to perform cladistics analysis on the permission characteristics to determine role perspective relationships between individual users of the organizational environment; and a role perspective analyzer module in communication with the role mining module to generate a role based access control model based on the determined role perspective relationships.
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13. A hierarchical role based access control (HRBAC) modeling system, the system comprising: an environment interface module to receive an indication of existing permissions granted to users in an organizational environment; a role mining module in communication with the environment interface module to analyze the permissions to create permission characteristics and to perform cladistics analysis on the permission characteristics to determine role perspective relationships between individual users of the organizational environment; and a role perspective analyzer module in communication with the role mining module to generate a role based access control model based on the determined role perspective relationships. 16. The system of claim 13 , wherein the role perspective analyzer module further comprises a HRBAC model builder to receive an indication of one or more modifications to the determined role perspective relationships and to modify the role perspective relationships in response.
| 0.5 |
1. A method, implemented by a computing device comprising a processor and one or more output devices communicatively connected to the processor, the method comprising: extracting, with the processor, one or more semantic structures from a knowledgebase associated with a computer-controlled character; combining, with the processor, the semantic structures from the knowledgebase with one or more structures from a virtual world state into one or more combined semantic structures; generating, with the processor, one or more natural language elements dynamically generated based on both the combined semantic structures and inputs from the user; and providing, via one or more of the output devices, the natural language elements in menus of user-selectable dialog elements and in computer-controlled character dialog elements responsive to the user-selectable dialog elements, the natural language elements being provided to a user via one or more of the one or more output devices.
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1. A method, implemented by a computing device comprising a processor and one or more output devices communicatively connected to the processor, the method comprising: extracting, with the processor, one or more semantic structures from a knowledgebase associated with a computer-controlled character; combining, with the processor, the semantic structures from the knowledgebase with one or more structures from a virtual world state into one or more combined semantic structures; generating, with the processor, one or more natural language elements dynamically generated based on both the combined semantic structures and inputs from the user; and providing, via one or more of the output devices, the natural language elements in menus of user-selectable dialog elements and in computer-controlled character dialog elements responsive to the user-selectable dialog elements, the natural language elements being provided to a user via one or more of the one or more output devices. 9. The method of claim 1 , further comprising providing one or more of the natural language elements in a menu of user-selectable interactions.
| 0.643419 |
5. A computer-implemented data processing system as recited in claim 1 , wherein said application program is implemented in a platform-independent programming language; and said computer-implemented data processing system performs the method further comprising the steps of: determining whether a native top-level container has been generated; generating an instance of an internal frame object that represents a native top-level container when said determining determines that said native top-level container has already been generated.
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5. A computer-implemented data processing system as recited in claim 1 , wherein said application program is implemented in a platform-independent programming language; and said computer-implemented data processing system performs the method further comprising the steps of: determining whether a native top-level container has been generated; generating an instance of an internal frame object that represents a native top-level container when said determining determines that said native top-level container has already been generated. 6. A computer-implemented data processing system as recited in claim 5 , wherein said computer-implemented data processing system performs the method further comprising the step of: generating a JInternalFrame object that represents a native top-level container.
| 0.789274 |
6. A speech recognition method, comprising: generating, by a processor, a word sequence by decoding a phoneme sequence generated from a speech signal; generating, by the processor, a syllable sequence corresponding to a word element, among words included in the word sequence, based on the phoneme sequence, in response to the word element comprising a lower recognition rate than a threshold value; and determining, by the processor, a text corresponding to a recognition result of the speech signal based on the word sequence and the syllable sequence, wherein the generating of the syllable sequence comprises generating the syllable sequence by decoding a portion corresponding to the word element among phonemes included in the phoneme sequence.
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6. A speech recognition method, comprising: generating, by a processor, a word sequence by decoding a phoneme sequence generated from a speech signal; generating, by the processor, a syllable sequence corresponding to a word element, among words included in the word sequence, based on the phoneme sequence, in response to the word element comprising a lower recognition rate than a threshold value; and determining, by the processor, a text corresponding to a recognition result of the speech signal based on the word sequence and the syllable sequence, wherein the generating of the syllable sequence comprises generating the syllable sequence by decoding a portion corresponding to the word element among phonemes included in the phoneme sequence. 8. The method of claim 6 , wherein the threshold value is one of a threshold value from the user and a relative threshold value determined based on recognition rates of the words included in the word sequence.
| 0.816434 |
1. A computer implemented method for inferring a probability of a first inference relating to an accident, the computer implemented method comprising: receiving a query at a database, on a data processing system, regarding a fact relating to the accident, wherein the first inference is absent from the database, wherein the database comprises a plurality of divergent data, wherein the plurality of divergent data includes a plurality of cohort data, wherein each datum of the database is conformed to the dimensions of the database, wherein each datum of the plurality of data has associated metadata and an associated key, wherein the associated metadata comprises data regarding cohorts associated with the corresponding datum, data regarding hierarchies associated with the corresponding datum, data regarding a corresponding source of the datum, and data regarding probabilities associated with integrity, reliability, and importance of each associated datum; establishing the fact as a frame of reference for the query, by a processing unit of the data processing system, wherein the frame of reference is used to determine data to be searched and rules to apply to the query, wherein the fact becomes a compound fact that includes multiple sub-facts on a subsequent iteration of the recursion process; mathematically refocusing the database such that the fact is modeled as a first center of an inverted star schema, and modeling each datum of the plurality of data in the inverted star schema around the fact; applying a first set of rules to the query, by the processing unit, wherein the first set of rules are determined for the query according to a second set of rules, wherein the first set of rules determine how the plurality of data are to be compared to the fact, wherein the first set of rules is prioritized, and wherein the first set of rules determine a first search space of the inverted star schema for the query including the associated metadata and associated key, wherein the second set of rules is a rule set used in a previous iteration of a recursive executing the query, by the processing unit, to create the probability of the first inference, wherein the probability of the first inference is determined from comparing the first search space according to the first set of rules; storing the probability of the first inference by the processing unit in a memory element of the data processing system; establishing the first inference as a second frame of reference, using the first set of rules to determine a third set of rules, wherein the third set of rules is a rule set used in a subsequent iteration of the recursive process; mathematically refocusing the database such that the first inference is modeled as a second center of the inverted star schema, and modeling each datum of the plurality of data in the inverted star schema around the first inference; and applying the third set of rules to create the probability of a second inference, wherein the third set of rules determines a second search space of the inverted star schema for the query including the associated metadata and associated key, wherein the probability of the second inference is determined from comparing the second search space according to the third set of rules; wherein the accident is selected from the group consisting of an airplane accident, a train accident, a maritime accident, a multi-vehicle accident, a single vehicle accident, a nuclear meltdown, a black-out, a building collapse, a failure of a bridge, a failure of a dam, a toxic spill, an explosion, and combinations thereof.
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1. A computer implemented method for inferring a probability of a first inference relating to an accident, the computer implemented method comprising: receiving a query at a database, on a data processing system, regarding a fact relating to the accident, wherein the first inference is absent from the database, wherein the database comprises a plurality of divergent data, wherein the plurality of divergent data includes a plurality of cohort data, wherein each datum of the database is conformed to the dimensions of the database, wherein each datum of the plurality of data has associated metadata and an associated key, wherein the associated metadata comprises data regarding cohorts associated with the corresponding datum, data regarding hierarchies associated with the corresponding datum, data regarding a corresponding source of the datum, and data regarding probabilities associated with integrity, reliability, and importance of each associated datum; establishing the fact as a frame of reference for the query, by a processing unit of the data processing system, wherein the frame of reference is used to determine data to be searched and rules to apply to the query, wherein the fact becomes a compound fact that includes multiple sub-facts on a subsequent iteration of the recursion process; mathematically refocusing the database such that the fact is modeled as a first center of an inverted star schema, and modeling each datum of the plurality of data in the inverted star schema around the fact; applying a first set of rules to the query, by the processing unit, wherein the first set of rules are determined for the query according to a second set of rules, wherein the first set of rules determine how the plurality of data are to be compared to the fact, wherein the first set of rules is prioritized, and wherein the first set of rules determine a first search space of the inverted star schema for the query including the associated metadata and associated key, wherein the second set of rules is a rule set used in a previous iteration of a recursive executing the query, by the processing unit, to create the probability of the first inference, wherein the probability of the first inference is determined from comparing the first search space according to the first set of rules; storing the probability of the first inference by the processing unit in a memory element of the data processing system; establishing the first inference as a second frame of reference, using the first set of rules to determine a third set of rules, wherein the third set of rules is a rule set used in a subsequent iteration of the recursive process; mathematically refocusing the database such that the first inference is modeled as a second center of the inverted star schema, and modeling each datum of the plurality of data in the inverted star schema around the first inference; and applying the third set of rules to create the probability of a second inference, wherein the third set of rules determines a second search space of the inverted star schema for the query including the associated metadata and associated key, wherein the probability of the second inference is determined from comparing the second search space according to the third set of rules; wherein the accident is selected from the group consisting of an airplane accident, a train accident, a maritime accident, a multi-vehicle accident, a single vehicle accident, a nuclear meltdown, a black-out, a building collapse, a failure of a bridge, a failure of a dam, a toxic spill, an explosion, and combinations thereof. 3. The computer implemented method of claim 1 further comprising: using the probability of the first inference to identify one of a cause of the accident, a proximal cause of the accident, and a combination thereof.
| 0.609389 |
5. A system for declarative specification, the system comprising: a memory, the memory stores executable instructions; and a microprocessor, the microprocessor is configured to execute the executable instructions to: a receiving unit configured to receive from a user through a user interface, an adjustment input to move an icon on a screen to a position in which the icon is touching one or more other icons to form a first grouping, the first grouping comprises an icon of a first data indication predicate touching an icon of a first action predicate, and the first data indication predicate is positive or negative based on a position of a game character relative to an object in a computer game; alter a declarative specification for controlling the game character in the computer game as a function of the first grouping, wherein the first grouping is a logical implication in which the game character performs a respective action identified by the icon of the first action predicate when the first data indication predicate is positive and the game character does not perform the respective action identified by the icon of the first action predicate when the first data indication predicate is negative; and and during the computer game, in response to the declarative specification, allow the game character to perform the respective action identified by the icon of the first action predicate when the first data indication is positive and not allow the game character to perform the respective action identified by the icon of the first action predicate when the first data indication is negative; wherein: one section of the screen comprises a declarative specification area, the adjustment input is received via the declarative specification area and displays the icon of the first data indication predicate and the icon of the first action predicate; and another section of the screen provides a view of a real time execution of the declarative specification, showing in real time, an effect of the adjustment input on the computer game as the declarative specification is altered.
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5. A system for declarative specification, the system comprising: a memory, the memory stores executable instructions; and a microprocessor, the microprocessor is configured to execute the executable instructions to: a receiving unit configured to receive from a user through a user interface, an adjustment input to move an icon on a screen to a position in which the icon is touching one or more other icons to form a first grouping, the first grouping comprises an icon of a first data indication predicate touching an icon of a first action predicate, and the first data indication predicate is positive or negative based on a position of a game character relative to an object in a computer game; alter a declarative specification for controlling the game character in the computer game as a function of the first grouping, wherein the first grouping is a logical implication in which the game character performs a respective action identified by the icon of the first action predicate when the first data indication predicate is positive and the game character does not perform the respective action identified by the icon of the first action predicate when the first data indication predicate is negative; and and during the computer game, in response to the declarative specification, allow the game character to perform the respective action identified by the icon of the first action predicate when the first data indication is positive and not allow the game character to perform the respective action identified by the icon of the first action predicate when the first data indication is negative; wherein: one section of the screen comprises a declarative specification area, the adjustment input is received via the declarative specification area and displays the icon of the first data indication predicate and the icon of the first action predicate; and another section of the screen provides a view of a real time execution of the declarative specification, showing in real time, an effect of the adjustment input on the computer game as the declarative specification is altered. 7. The system of claim 5 , wherein the first data indication predicate and/or first action predicate is at least one of a sub-parameterized predicate or a hierarchical predicate.
| 0.656834 |
6. The method of claim 3 , wherein the reference value is determined or retrieved by: determining or retrieving an average accept ratio, the average accept ratio being a ratio of a number of selected topic suggestions from the combined list to a number of total topic suggestions in the combined list; determining or retrieving an accept ratio for each topic source, the accept ratio being a ratio of a number of selected topic suggestions from the topic source to a number of topic suggestions from the topic source; and adjusting one or more of the weights stored on the storage medium according to a comparison of the accept ratio for a corresponding topic source with the average accept ratio.
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6. The method of claim 3 , wherein the reference value is determined or retrieved by: determining or retrieving an average accept ratio, the average accept ratio being a ratio of a number of selected topic suggestions from the combined list to a number of total topic suggestions in the combined list; determining or retrieving an accept ratio for each topic source, the accept ratio being a ratio of a number of selected topic suggestions from the topic source to a number of topic suggestions from the topic source; and adjusting one or more of the weights stored on the storage medium according to a comparison of the accept ratio for a corresponding topic source with the average accept ratio. 11. The method of claim 6 , wherein the average accept ratio and the accept ratio for each topic source are determined, and the one or more weights are adjusted, over a designated interval.
| 0.79543 |
1. A computer-implemented method, comprising: obtaining a first quality model that was trained using a first set of training entities; identifying a set of candidate entities, where each candidate entity is different from each of the training entities; for each candidate entity in the set of candidate entities: obtaining a first quality score for the candidate entity; obtaining one or more neighbor features for neighbor entities of the candidate entity, where each neighbor entity of the candidate entity is an entity that is linked to the candidate entity; obtaining one or more entity specific feature values for the candidate entity, where each entity specific feature value is determined independent of the neighbor entities of the candidate entity; and determining a second quality score for the candidate entity using the first quality model, the second quality score being computed based on the first quality score, the neighbor features, and the entity specific feature values.
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1. A computer-implemented method, comprising: obtaining a first quality model that was trained using a first set of training entities; identifying a set of candidate entities, where each candidate entity is different from each of the training entities; for each candidate entity in the set of candidate entities: obtaining a first quality score for the candidate entity; obtaining one or more neighbor features for neighbor entities of the candidate entity, where each neighbor entity of the candidate entity is an entity that is linked to the candidate entity; obtaining one or more entity specific feature values for the candidate entity, where each entity specific feature value is determined independent of the neighbor entities of the candidate entity; and determining a second quality score for the candidate entity using the first quality model, the second quality score being computed based on the first quality score, the neighbor features, and the entity specific feature values. 2. The method of claim 1 , further comprising: training a second quality model based on training features associated with the training entities, where the training features for each training entity include: a given quality score for the training entity; at least one neighbor quality score for at least one neighbor entity of the training entity; and at least one entity specific feature value of the training entity; wherein at least one neighbor entity of at least one training entity is a candidate entity, and wherein the second quality model provides, as output, a quality score for an entity.
| 0.621843 |
1. A method in a content recommendation computing system, the method comprising: presenting a first user interface control that is configured to create a new entity collection in response to received user selection of indications of named entities and suggest one or more additional entities to add to an existing collection; presenting a second user interface control that displays information about collections created by multiple different users of the content recommendation computing system; defining, responsive to user selections of indications of named entities via the first user interface control, a collection including multiple entities that are each referenced by one or more of multiple content items indexed by the content recommendation computing system and that are each electronically represented in the content recommendation computing system, the collection being separate from the multiple content items indexed by the content recommendation computing system; under control of the content recommendation computing system, automatically recommending one or more additional entities to be added to the collection based on the multiple entities of the collection, by: determining shared characteristics of the multiple entities, wherein each shared characteristic is the same for each of the multiple entities of the collection, the shared characteristics including common facets and common key terms common to the multiple entities of the collection, wherein the common facets are category values, type values, and/or characteristic values of the multiple entities and are sub-type values of a top-level entity type electronically represented by the content recommendation computing system; determining a plurality of entities that includes at least a first entity that appears in a content item in a relationship with one of the common facets, a second entity that appears in a content item in context with one of the common key terms, and a third entity that appears in a content item in a relationship with one of the multiple entities and in context with another of the multiple entities, wherein the first entity appears in a content item in a relationship with one of the common facets when the first entity appears as a first subject or object of a subject-verb-object relation present in the content item in which the one of the common facets appears as the other of the subject or object of the first subject-verb-object relation; and ordering the determined plurality of entities, based on entity frequency counts of frequency each entity appears in the indexed content items and on whether the determined plurality of entities share one or more of the common facets; presenting on the first user interface control the ordered plurality of entities as suggested additions to the collection; modifying the collection by adding one of the ordered plurality of entities to the multiple entities of the collection, the added entity selected by a user via the first user interface control; automatically processing the modified collection to determine one or more of the multiple content items that are related to at least some of the multiple entities of the modified collection, wherein processing the modified collection includes determining whether one of the multiple entities appears in a content item in a relationship with a facet common to entities of the modified collection and in the content item in context with key terms common to the entities of the modified collection, wherein the one of the multiple entities appears in a content item in a relationship with the facet common to entities of the modified collection when the one of the multiple entities appears as a subject or object of a second subject-verb-object relation present in the content item in which the facet common to the entities of the modified collection appears as the other of the subject or object of the second subject-verb-object relation; and recommending the determined one or more of the multiple content items.
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1. A method in a content recommendation computing system, the method comprising: presenting a first user interface control that is configured to create a new entity collection in response to received user selection of indications of named entities and suggest one or more additional entities to add to an existing collection; presenting a second user interface control that displays information about collections created by multiple different users of the content recommendation computing system; defining, responsive to user selections of indications of named entities via the first user interface control, a collection including multiple entities that are each referenced by one or more of multiple content items indexed by the content recommendation computing system and that are each electronically represented in the content recommendation computing system, the collection being separate from the multiple content items indexed by the content recommendation computing system; under control of the content recommendation computing system, automatically recommending one or more additional entities to be added to the collection based on the multiple entities of the collection, by: determining shared characteristics of the multiple entities, wherein each shared characteristic is the same for each of the multiple entities of the collection, the shared characteristics including common facets and common key terms common to the multiple entities of the collection, wherein the common facets are category values, type values, and/or characteristic values of the multiple entities and are sub-type values of a top-level entity type electronically represented by the content recommendation computing system; determining a plurality of entities that includes at least a first entity that appears in a content item in a relationship with one of the common facets, a second entity that appears in a content item in context with one of the common key terms, and a third entity that appears in a content item in a relationship with one of the multiple entities and in context with another of the multiple entities, wherein the first entity appears in a content item in a relationship with one of the common facets when the first entity appears as a first subject or object of a subject-verb-object relation present in the content item in which the one of the common facets appears as the other of the subject or object of the first subject-verb-object relation; and ordering the determined plurality of entities, based on entity frequency counts of frequency each entity appears in the indexed content items and on whether the determined plurality of entities share one or more of the common facets; presenting on the first user interface control the ordered plurality of entities as suggested additions to the collection; modifying the collection by adding one of the ordered plurality of entities to the multiple entities of the collection, the added entity selected by a user via the first user interface control; automatically processing the modified collection to determine one or more of the multiple content items that are related to at least some of the multiple entities of the modified collection, wherein processing the modified collection includes determining whether one of the multiple entities appears in a content item in a relationship with a facet common to entities of the modified collection and in the content item in context with key terms common to the entities of the modified collection, wherein the one of the multiple entities appears in a content item in a relationship with the facet common to entities of the modified collection when the one of the multiple entities appears as a subject or object of a second subject-verb-object relation present in the content item in which the facet common to the entities of the modified collection appears as the other of the subject or object of the second subject-verb-object relation; and recommending the determined one or more of the multiple content items. 4. The method of claim 1 wherein processing the modified collection includes determining whether an entity appears in an article in a relationship with one of the multiple entities and in the article in context with another one of the multiple entities, wherein the entity appears in the article in a relationship with one of the multiple entities when the entity appears as a subject or object of a third subject-verb-object relation present in the article in which the one of the multiple entities appears as the other of the subject or object of the third subject-verb-object relation.
| 0.510896 |
11. A system for identifying phrasal terms, the system comprising: a processor; and a memory, wherein the processor is configured to execute steps comprising: receiving a text having a plurality of words; determining a plurality of contexts, wherein a context comprises one or more words proximate to another word in the text; for each context, determining a first frequency based on a number of occurrences of the context within the text; assigning a first rank to at least one context based on the first frequency for the at least one context; determining multiple word-context pairs; for each word-context pair, determining a second frequency based on a number of occurrences of the word associated with the word-context pair being used in the context; assigning a second rank to at least one word-context pair based on the second frequency for the at least one word-context pair; determining a rank ratio for each word-context pair based on the first rank and the second rank associated with the word-context pair; determining a mutual rank ratio based on multiple rank ratios; and identifying a phrasal term based on the mutual rank ratio.
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11. A system for identifying phrasal terms, the system comprising: a processor; and a memory, wherein the processor is configured to execute steps comprising: receiving a text having a plurality of words; determining a plurality of contexts, wherein a context comprises one or more words proximate to another word in the text; for each context, determining a first frequency based on a number of occurrences of the context within the text; assigning a first rank to at least one context based on the first frequency for the at least one context; determining multiple word-context pairs; for each word-context pair, determining a second frequency based on a number of occurrences of the word associated with the word-context pair being used in the context; assigning a second rank to at least one word-context pair based on the second frequency for the at least one word-context pair; determining a rank ratio for each word-context pair based on the first rank and the second rank associated with the word-context pair; determining a mutual rank ratio based on multiple rank ratios; and identifying a phrasal term based on the mutual rank ratio. 14. The system of claim 11 , wherein determining a mutual rank ratio comprises: computing a product of the one or more rank ratios corresponding to an n-gram; determining an n th root of the product; and assigning the n th root of the product to the mutual rank ratio for the n-gram.
| 0.894502 |
25. A server system for providing remote message server configuration information, comprising: a POP configuration database including one or more configuration patterns associated with a domain; one or more processors; and memory storing one or programs to be executed by the one or more processors, the one or more programs having instructions for: receiving from a client a request for remote message server configuration information, the request including an electronic mail address; identifying the one or more remote message server configuration patterns using at least a portion of the electronic mail address and the POP configuration database; wherein the one or more remote message server configuration patterns are identified using only the received electronic mail address and information from the POP configuration database; and in response to the request, providing to the client a plurality of options to be concurrently presented to a user of the client, the plurality of options comprising options for (A) selecting one of the one or more remote message server configuration patterns, and (B) entering a remote message server configuration field value.
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25. A server system for providing remote message server configuration information, comprising: a POP configuration database including one or more configuration patterns associated with a domain; one or more processors; and memory storing one or programs to be executed by the one or more processors, the one or more programs having instructions for: receiving from a client a request for remote message server configuration information, the request including an electronic mail address; identifying the one or more remote message server configuration patterns using at least a portion of the electronic mail address and the POP configuration database; wherein the one or more remote message server configuration patterns are identified using only the received electronic mail address and information from the POP configuration database; and in response to the request, providing to the client a plurality of options to be concurrently presented to a user of the client, the plurality of options comprising options for (A) selecting one of the one or more remote message server configuration patterns, and (B) entering a remote message server configuration field value. 29. The server system of claim 25 , wherein the instructions for providing include ordering a plurality of field suggestions in accordance with a ranking function.
| 0.532941 |
20. A computer-implemented method of providing a graphical user interface for electronically displaying search results and related information, the method comprising: under the control of one or more computer systems configured with executable instructions, providing for electronic display a presentation surface of an application, the presentation surface comprising at least a first region and a second region, the first region being arranged for display of search results and the second region including a whiteboard; updating for display at least the second region to include information corresponding to one or more first search results of the first region; detecting, in connection with one or more second search results displayed in the first region, a drag and drop operation, between the first region and the second region, for adding information corresponding to the one or more second search results; and responsive to detection of the drag and drop operation, when the drag and drop operation is from the first region to the second region, updating for display at least the whiteboard of the second region to include the information corresponding to the one or more second search results simultaneously with the information corresponding to the one or more first search results of the first region, wherein the information corresponding to the one or more first search results and the information corresponding to the one or more second search results collectively represent two or more information resources represented by corresponding search result of the first region; and when the drag and drop operation is from the second region to the first region, updating at least the first region with search results associated with content of the second region corresponding with the drag and drop operation.
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20. A computer-implemented method of providing a graphical user interface for electronically displaying search results and related information, the method comprising: under the control of one or more computer systems configured with executable instructions, providing for electronic display a presentation surface of an application, the presentation surface comprising at least a first region and a second region, the first region being arranged for display of search results and the second region including a whiteboard; updating for display at least the second region to include information corresponding to one or more first search results of the first region; detecting, in connection with one or more second search results displayed in the first region, a drag and drop operation, between the first region and the second region, for adding information corresponding to the one or more second search results; and responsive to detection of the drag and drop operation, when the drag and drop operation is from the first region to the second region, updating for display at least the whiteboard of the second region to include the information corresponding to the one or more second search results simultaneously with the information corresponding to the one or more first search results of the first region, wherein the information corresponding to the one or more first search results and the information corresponding to the one or more second search results collectively represent two or more information resources represented by corresponding search result of the first region; and when the drag and drop operation is from the second region to the first region, updating at least the first region with search results associated with content of the second region corresponding with the drag and drop operation. 24. The computer-implemented method of claim 20 , further comprising, responsive to user input for annotation, annotating contents of the second region.
| 0.537688 |
14. The system of claim 10 , wherein the contextual pattern decoder engine is further configured to subdivide one of the plurality of knowledge elements using a second pattern matching at least a part of the abstract representation, to identify the subdivided knowledge element with context information in the abstract representation, and wherein the pattern classification engine is further configured to classify the subdivided knowledge element in the abstract representation as a business rule using the context information and a third pattern.
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14. The system of claim 10 , wherein the contextual pattern decoder engine is further configured to subdivide one of the plurality of knowledge elements using a second pattern matching at least a part of the abstract representation, to identify the subdivided knowledge element with context information in the abstract representation, and wherein the pattern classification engine is further configured to classify the subdivided knowledge element in the abstract representation as a business rule using the context information and a third pattern. 15. The system of claim 14 , wherein knowledge in the abstract representation can be presented in one of a plurality of formats, comprising: text, XML, graphic, one or more program language or pseudo program language.
| 0.849409 |
1. A computer-implemented method comprising: determining a genre classification for a document, the genre classification comprising multiple terms and corresponding scores, each score for a term indicating a confidence level for the term with respect to the document; accessing a stored document control policy ontology, the document control policy ontology comprising a hierarchy of nodes that represent document genres and have corresponding document control policies; identifying an entry point node in the hierarchy of nodes of the document control policy ontology by successively comparing the multiple terms in the genre classification with the nodes in the document control policy ontology in order of increasing hierarchical position of the terms until either a matching node in the ontology is found or the term in the highest hierarchical position of the multiple terms is reached and no matching node is found in which case a document control policy corresponding to a root node is used the identifying comprising, when the document control policy ontology has an underlying classification structure different from a classification structure used for determining the genre classification, identifying a correlation between the different classification structures; assessing a confidence level for applicability of the entry point node based at least in part upon at least one of the scores; inferencing within the document control policy ontology to find a document control policy more conservative than the policy corresponding to the entry point node, the inferencing comprising selecting a parent node of the entry point node in the document control policy ontology to stand in for the entry point node if the assessed confidence level for applicability of the entry point node falls below a threshold, the parent node inheriting at least one document control policy derived from at least the entry point node; and outputting, to a hardware device, a recommendation that identifies at least one document control policy to govern access to the document based on the identified entry point node or the selected parent node in the document control policy ontology.
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1. A computer-implemented method comprising: determining a genre classification for a document, the genre classification comprising multiple terms and corresponding scores, each score for a term indicating a confidence level for the term with respect to the document; accessing a stored document control policy ontology, the document control policy ontology comprising a hierarchy of nodes that represent document genres and have corresponding document control policies; identifying an entry point node in the hierarchy of nodes of the document control policy ontology by successively comparing the multiple terms in the genre classification with the nodes in the document control policy ontology in order of increasing hierarchical position of the terms until either a matching node in the ontology is found or the term in the highest hierarchical position of the multiple terms is reached and no matching node is found in which case a document control policy corresponding to a root node is used the identifying comprising, when the document control policy ontology has an underlying classification structure different from a classification structure used for determining the genre classification, identifying a correlation between the different classification structures; assessing a confidence level for applicability of the entry point node based at least in part upon at least one of the scores; inferencing within the document control policy ontology to find a document control policy more conservative than the policy corresponding to the entry point node, the inferencing comprising selecting a parent node of the entry point node in the document control policy ontology to stand in for the entry point node if the assessed confidence level for applicability of the entry point node falls below a threshold, the parent node inheriting at least one document control policy derived from at least the entry point node; and outputting, to a hardware device, a recommendation that identifies at least one document control policy to govern access to the document based on the identified entry point node or the selected parent node in the document control policy ontology. 10. The method of claim 1 , further comprising: generalizing about document genre when the genre classification matches a node in the document control policy ontology that lacks an associated policy, the generalizing including traversing a link between the matching node and successive parent nodes until an associated policy is found.
| 0.564589 |
9. The method of claim 1 , wherein the one or more suggestions are a first set of suggestions, and the method further comprises: obtaining a second set of suggestions from a message compose window; and presenting the first and second sets of suggestions in a window.
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9. The method of claim 1 , wherein the one or more suggestions are a first set of suggestions, and the method further comprises: obtaining a second set of suggestions from a message compose window; and presenting the first and second sets of suggestions in a window. 11. The method of claim 9 , further comprising: preventing the message compose window from presenting the second set of suggestions in a separate window.
| 0.897552 |
1. A method for creating a business simulation utilizing a rule-based expert system with a spreadsheet object component that includes data and calculations required for the simulation and communication of information to provide a dynamic, goal based educational learning experience, comprising the steps of: (a) accessing the information in the spreadsheet object component of the rule-based expert system to determine presentation information indicative of a goal; (b) utilizing the information in the spreadsheet object component of the rule-based expert system to integrate goal-based learning information in a structured, dynamic business simulation designed by a profiling component to motivate accomplishment of the goal; (c) querying a student for answers to one or more questions based on one or more learning objectives of the business simulation using a simulated human conversation; (d) monitoring answers to questions posed to evaluate progress toward the goal utilizing the spreadsheet object component of the rule-based expert system and providing dynamic, goal-based, remediation learning information feedback from a remediation object component including a knowledge system and a software tutor comprising an artificial intelligence engine which generates individualized coaching messages that further motivates accomplishment of the goal; (e) analyzing the answers from the student utilizing system tools to compare the answers with a standard for achieving the goal.
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1. A method for creating a business simulation utilizing a rule-based expert system with a spreadsheet object component that includes data and calculations required for the simulation and communication of information to provide a dynamic, goal based educational learning experience, comprising the steps of: (a) accessing the information in the spreadsheet object component of the rule-based expert system to determine presentation information indicative of a goal; (b) utilizing the information in the spreadsheet object component of the rule-based expert system to integrate goal-based learning information in a structured, dynamic business simulation designed by a profiling component to motivate accomplishment of the goal; (c) querying a student for answers to one or more questions based on one or more learning objectives of the business simulation using a simulated human conversation; (d) monitoring answers to questions posed to evaluate progress toward the goal utilizing the spreadsheet object component of the rule-based expert system and providing dynamic, goal-based, remediation learning information feedback from a remediation object component including a knowledge system and a software tutor comprising an artificial intelligence engine which generates individualized coaching messages that further motivates accomplishment of the goal; (e) analyzing the answers from the student utilizing system tools to compare the answers with a standard for achieving the goal. 4. A method for creating a business simulation utilizing a rule-based expert system with a spreadsheet object component as recited in claim 1, wherein the media information comprises telephony information.
| 0.673684 |
10. The method of claim 1 , further comprising building a parallel resource map relating the identified resource in the first language to the parallel resource in the second language.
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10. The method of claim 1 , further comprising building a parallel resource map relating the identified resource in the first language to the parallel resource in the second language. 11. The method of claim 10 , wherein the parallel resource map is a part of a resource information database.
| 0.928863 |
1. A computer-implemented method for facilitating cross-language communication among users of respective wireless communication devices, the method comprising: receiving, at a first of the wireless communication devices, a first user input indicating willingness to participate in a cross-language communication session with a second of the wireless communication devices; receiving, at the second wireless communication device, a second user input indicating willingness to participate in the cross-language communication session with the first wireless communication device; receiving, at the first wireless communication device, a first signal from a first sensor of the first wireless communication device, other than an antenna; receiving, at the second wireless communication device, a second signal from a second sensor of the second wireless communication device, other than an antenna; automatically comparing the first signal to the second signal to determine whether the first signal and the second signal satisfy a similarity criterion; if the first and second signals satisfy the similarity criterion, automatically establishing the cross-language communication session; and if the cross-language communication session is established: after receiving the first and second user inputs, receiving a first user message entered on the first wireless communication device in a first natural language; automatically generating a translated first user message, including translating the first user message into a second natural language, different than the first natural language; and outputting the translated first user message on the second wireless communication device.
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1. A computer-implemented method for facilitating cross-language communication among users of respective wireless communication devices, the method comprising: receiving, at a first of the wireless communication devices, a first user input indicating willingness to participate in a cross-language communication session with a second of the wireless communication devices; receiving, at the second wireless communication device, a second user input indicating willingness to participate in the cross-language communication session with the first wireless communication device; receiving, at the first wireless communication device, a first signal from a first sensor of the first wireless communication device, other than an antenna; receiving, at the second wireless communication device, a second signal from a second sensor of the second wireless communication device, other than an antenna; automatically comparing the first signal to the second signal to determine whether the first signal and the second signal satisfy a similarity criterion; if the first and second signals satisfy the similarity criterion, automatically establishing the cross-language communication session; and if the cross-language communication session is established: after receiving the first and second user inputs, receiving a first user message entered on the first wireless communication device in a first natural language; automatically generating a translated first user message, including translating the first user message into a second natural language, different than the first natural language; and outputting the translated first user message on the second wireless communication device. 23. A method according to claim 1 , wherein first and second signals indicate the first wireless communication device and the second wireless communication device have collided with each other.
| 0.656642 |
1. A method for identifying one or more of a plurality of enterprise-level business processes of an enterprise and a plurality of monitoring templates, said method comprising: obtaining, using at least one device, at least one of said plurality of enterprise-level business processes and said plurality of monitoring templates, wherein at least one of said obtained enterprise-level business processes and said obtained monitoring template has an associated monitoring intent comprising one or more monitoring keywords, wherein said monitoring intent links one or more business goals of said enterprise to a given enterprise-level business process such that said monitoring intent can be searched to identify at least one existing enterprise-level business process that is related to a new enterprise-level business process being created; obtaining, using at least one processing device, a user-specified monitoring intent from said user, said user-specified monitoring intent comprising one or more search keywords; assigning, using at least one processing device, a score to at least one of said obtained enterprise-level business process and said monitoring template, wherein said score is based on a matching of a semantic context of said associated monitoring intent and one or more of said monitoring keywords from said associated monitoring intent with a semantic context of said user-specified monitoring intent and said search keywords of said user-specified monitoring intent; and identifying, using at least one processing device, said one or more of said plurality of enterprise-level business processes and said plurality of monitoring templates based on said assigned score.
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1. A method for identifying one or more of a plurality of enterprise-level business processes of an enterprise and a plurality of monitoring templates, said method comprising: obtaining, using at least one device, at least one of said plurality of enterprise-level business processes and said plurality of monitoring templates, wherein at least one of said obtained enterprise-level business processes and said obtained monitoring template has an associated monitoring intent comprising one or more monitoring keywords, wherein said monitoring intent links one or more business goals of said enterprise to a given enterprise-level business process such that said monitoring intent can be searched to identify at least one existing enterprise-level business process that is related to a new enterprise-level business process being created; obtaining, using at least one processing device, a user-specified monitoring intent from said user, said user-specified monitoring intent comprising one or more search keywords; assigning, using at least one processing device, a score to at least one of said obtained enterprise-level business process and said monitoring template, wherein said score is based on a matching of a semantic context of said associated monitoring intent and one or more of said monitoring keywords from said associated monitoring intent with a semantic context of said user-specified monitoring intent and said search keywords of said user-specified monitoring intent; and identifying, using at least one processing device, said one or more of said plurality of enterprise-level business processes and said plurality of monitoring templates based on said assigned score. 4. The method of claim 1 , further comprising the step of storing said one or more of said plurality of enterprise-level business processes and said plurality of monitoring templates in a format that can be searched.
| 0.625156 |
12. A system for enabling static analysis of indirectly modeled code by an analyzer lacking capability for direct analysis of any indirectly modeled code, the system comprising: a first processor; and a first memory in electrical communication with the first processor, the first memory comprising instructions which, when executed by a processing unit comprising at least one of the first processor and a second processor, the processing unit being in electrical communication with a memory module comprises at least one of the first memory and a second memory, program the processing unit to: transform a syntax tree of a source code segment written in an indirectly modeled language that is not supported by a static analyzer, by including in each node of the syntax tree a respective location identifier identifying a location of at least one of an operator and an operand corresponding to that node in source code specified in the indirectly modeled language; identify a set of nodes of selected types in the transformed syntax tree, each selected type being associated with taint propagation indicating propagation of an operand comprising or derived from a user input to an operator specified in a code module specified in a directly modeled language, the static analyzer supporting the directly modelled language; and for each node in the identified set, generate a statement in the directly modeled language, based on, at least in part, at least one of: (i) a type of the node, (ii) a type of an input to the node, and (iii) an object corresponding to the node.
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12. A system for enabling static analysis of indirectly modeled code by an analyzer lacking capability for direct analysis of any indirectly modeled code, the system comprising: a first processor; and a first memory in electrical communication with the first processor, the first memory comprising instructions which, when executed by a processing unit comprising at least one of the first processor and a second processor, the processing unit being in electrical communication with a memory module comprises at least one of the first memory and a second memory, program the processing unit to: transform a syntax tree of a source code segment written in an indirectly modeled language that is not supported by a static analyzer, by including in each node of the syntax tree a respective location identifier identifying a location of at least one of an operator and an operand corresponding to that node in source code specified in the indirectly modeled language; identify a set of nodes of selected types in the transformed syntax tree, each selected type being associated with taint propagation indicating propagation of an operand comprising or derived from a user input to an operator specified in a code module specified in a directly modeled language, the static analyzer supporting the directly modelled language; and for each node in the identified set, generate a statement in the directly modeled language, based on, at least in part, at least one of: (i) a type of the node, (ii) a type of an input to the node, and (iii) an object corresponding to the node. 15. The system of claim 12 , wherein: the type of the node comprises at least one of a set directive type and a loop directive type; and the instructions program the processing unit to generate a propagator statement.
| 0.528605 |
1. A method for identifying an expert information source, the method comprising: analyzing first search results obtained by a search system, the first search results including search results for at least one query associated with more than one category included in a query ontology; based on the analysis of the first search results, determining a relative contribution by an information source to the first search results; analyzing second search results, obtained by the search system, for queries associated with a particular category included in the query ontology, the second search results being different but not exclusive of the first search results; based on the analysis of the second search results, determining a relative contribution by the information source to the second search results; comparing the relative contribution by the information source to the first search results to the relative contribution by the information source to the second search results; based on comparison results, determining whether the relative contribution by the information source to the second search results is more than a threshold greater than the relative contribution by the information source to the first search results; and in response to determining that the relative contribution by the information source to the second search results is more than the threshold greater than the relative contribution by the information source to the first search results: identifying the information source as an expert information source for the particular category included in the query ontology; and storing, in electronic storage, data indicating that the information source is an expert information source for the particular category included in the query ontology.
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1. A method for identifying an expert information source, the method comprising: analyzing first search results obtained by a search system, the first search results including search results for at least one query associated with more than one category included in a query ontology; based on the analysis of the first search results, determining a relative contribution by an information source to the first search results; analyzing second search results, obtained by the search system, for queries associated with a particular category included in the query ontology, the second search results being different but not exclusive of the first search results; based on the analysis of the second search results, determining a relative contribution by the information source to the second search results; comparing the relative contribution by the information source to the first search results to the relative contribution by the information source to the second search results; based on comparison results, determining whether the relative contribution by the information source to the second search results is more than a threshold greater than the relative contribution by the information source to the first search results; and in response to determining that the relative contribution by the information source to the second search results is more than the threshold greater than the relative contribution by the information source to the first search results: identifying the information source as an expert information source for the particular category included in the query ontology; and storing, in electronic storage, data indicating that the information source is an expert information source for the particular category included in the query ontology. 7. The method of claim 1 , wherein analyzing second search results, obtained by the search system, for queries associated with the particular category included in the query ontology, the second search results being different but not exclusive of the first search results includes analyzing second search results, obtained by the search system, for queries associated with the particular category included in the query ontology, the second search results being a subset of the first search results.
| 0.632201 |
14. The system of claim 11 , wherein the control circuitry is further configured to: retrieve closed-captioning information corresponding to the range of play positions; identify a subject based on the retrieved closed-captioning information; and associate the attribute with the identified subject.
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14. The system of claim 11 , wherein the control circuitry is further configured to: retrieve closed-captioning information corresponding to the range of play positions; identify a subject based on the retrieved closed-captioning information; and associate the attribute with the identified subject. 15. The system of claim 14 , wherein the subject is an actor, character or person within the media asset.
| 0.925429 |
29. A non-transitory computer-readable medium storing a set of instructions that when executed cause a computer to perform a method comprising: generating a plurality of test scripts, a test script from among the plurality of test scripts generated by performing: initiating a voice call interaction with a speech application, the speech application comprising a network of interaction nodes; and repeatedly performing, until a stopping condition is encountered, the steps of: executing the voice call interaction with the speech application by traversing through one or more interaction nodes from among the network of interaction nodes until an interaction node requiring a response is encountered; selecting an utterance generation mode corresponding to the interaction node; determining a response to be provided corresponding to the interaction node of the speech application based on the utterance generation mode; and providing the response to the speech application, wherein the test script comprises instructions for traversing interaction nodes involved during a course of the voice call interaction, and, instructions for provisioning one or more responses to the speech application during the course of the voice call interaction; identifying one or more test scripts from among the plurality of test scripts based on a pre-determined objective; and providing the one or more test scripts to a user for facilitating testing of the speech application.
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29. A non-transitory computer-readable medium storing a set of instructions that when executed cause a computer to perform a method comprising: generating a plurality of test scripts, a test script from among the plurality of test scripts generated by performing: initiating a voice call interaction with a speech application, the speech application comprising a network of interaction nodes; and repeatedly performing, until a stopping condition is encountered, the steps of: executing the voice call interaction with the speech application by traversing through one or more interaction nodes from among the network of interaction nodes until an interaction node requiring a response is encountered; selecting an utterance generation mode corresponding to the interaction node; determining a response to be provided corresponding to the interaction node of the speech application based on the utterance generation mode; and providing the response to the speech application, wherein the test script comprises instructions for traversing interaction nodes involved during a course of the voice call interaction, and, instructions for provisioning one or more responses to the speech application during the course of the voice call interaction; identifying one or more test scripts from among the plurality of test scripts based on a pre-determined objective; and providing the one or more test scripts to a user for facilitating testing of the speech application. 30. The computer-readable medium of claim 29 , wherein the stopping condition is one of: an error event comprising detection of at least one error during the execution of the voice call interaction; an interaction transfer event comprising detection of a transfer of the voice call interaction to a customer support representative; an interaction termination event comprising detection of a termination of the voice call interaction; and a goal realization event comprising detection of a realization of a pre-determined goal for testing of the speech application.
| 0.51451 |
7. The method of claim 1 , further causing the computing device to output the estimated intent.
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7. The method of claim 1 , further causing the computing device to output the estimated intent. 9. The method of claim 7 , further causing the computing device to perform an action based on the output estimated intent.
| 0.936203 |
17. A method comprising: capturing a stream of operational events in real time; and materializing, by an activity monitoring computer system, a resulting view, wherein the resulting view comprises a dynamically defined view of the stream of operational events, wherein the materializing of the resulting view comprises: forming a context lookup query comprising a join plan, wherein each join node in the join plan joins a stream table and a context table, the stream table being associated with at least one event in the stream of events, and the context table being associated with context relevant to the at least one event.
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17. A method comprising: capturing a stream of operational events in real time; and materializing, by an activity monitoring computer system, a resulting view, wherein the resulting view comprises a dynamically defined view of the stream of operational events, wherein the materializing of the resulting view comprises: forming a context lookup query comprising a join plan, wherein each join node in the join plan joins a stream table and a context table, the stream table being associated with at least one event in the stream of events, and the context table being associated with context relevant to the at least one event. 19. The method as recited in claim 17 , wherein the context lookup query comprises a context query in SQL wherein the context query does not comprise a JDBC query.
| 0.633298 |
1. A system comprising: one or more electronic memories that store instructions for automated optical symbol recognition; and one or more processors to execute the instructions to: apply blocking to an image stored in at least one of the memories to decompose the image into an ordered set of symbol variants, wherein the image depicts a mathematical expression, and wherein, to apply blocking to the image, the processors are further to execute the instructions to: set a blocking-direction indication to indicate one of a horizontal blocking-direction or a vertical blocking-direction; set a current-level indication to indicate a first level; block the image into sub-images at a level according to the current-level indication and in a direction according to the blocking-direction indication; and recursively for each sub-image in the sub-images at the level, apply one or more symbol-recognition methods to the sub-image; select a most probable path from among candidate paths corresponding to the ordered set of symbol variants; use the most probable path and the ordered set of symbol variants to generate an encoded mathematical expression equivalent to the mathematical expression; and store the encoded mathematical expression in one or more of the memories.
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1. A system comprising: one or more electronic memories that store instructions for automated optical symbol recognition; and one or more processors to execute the instructions to: apply blocking to an image stored in at least one of the memories to decompose the image into an ordered set of symbol variants, wherein the image depicts a mathematical expression, and wherein, to apply blocking to the image, the processors are further to execute the instructions to: set a blocking-direction indication to indicate one of a horizontal blocking-direction or a vertical blocking-direction; set a current-level indication to indicate a first level; block the image into sub-images at a level according to the current-level indication and in a direction according to the blocking-direction indication; and recursively for each sub-image in the sub-images at the level, apply one or more symbol-recognition methods to the sub-image; select a most probable path from among candidate paths corresponding to the ordered set of symbol variants; use the most probable path and the ordered set of symbol variants to generate an encoded mathematical expression equivalent to the mathematical expression; and store the encoded mathematical expression in one or more of the memories. 8. The system of claim 1 , wherein, to apply blocking to the image, the processor are further to execute the instructions to: identify white-space stripes with directions equal to, or within a threshold angular displacement from, a direction orthogonal to a blocking direction; coalesce overlapping ones of the white-space stripes to produce non-overlapping white-space stripes; and use the non-overlapping white-space stripes as block borders to partition the image into two or more blocks along the blocking direction.
| 0.576477 |
51. An apparatus for use in a mobile station, the apparatus comprising: means for generating a request for translation information from a translation information service, wherein said translation information is associated with a location and one or more written and/or spoken languages; means for transmitting said request for translation information to said translation information service; means for receiving a response from said translation information service comprising requested translation information, said requested translation information being based, at least in part, on said request for translation information, said location, and predicted information, wherein the predicted information is associated with the request for translation information, the location, and at least one other request for translation information associated with at least one other location and previously transmitted to said translation information service by at least one other mobile station; and means for generating a presentation for a user based, at least in part, on said response.
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51. An apparatus for use in a mobile station, the apparatus comprising: means for generating a request for translation information from a translation information service, wherein said translation information is associated with a location and one or more written and/or spoken languages; means for transmitting said request for translation information to said translation information service; means for receiving a response from said translation information service comprising requested translation information, said requested translation information being based, at least in part, on said request for translation information, said location, and predicted information, wherein the predicted information is associated with the request for translation information, the location, and at least one other request for translation information associated with at least one other location and previously transmitted to said translation information service by at least one other mobile station; and means for generating a presentation for a user based, at least in part, on said response. 59. The apparatus as recited in claim 51 , wherein said location is associated with at least one of: a region, a structure, a point of interest, an estimated position of said mobile station, and/or an estimated orientation of said mobile station.
| 0.590411 |
11. A non-transitory computer-readable storage device storing instructions that, when executed by a computer, cause the computer to perform operations comprising: detecting startup of one or more applications in an analytics engine configured to analyze a set of items based on data models and processing models to dynamically generate graphical user analysis interfaces; populating columns of a plurality of fact tables with first client data, wherein a first portion of the first client data is stored on a first data storage device, and wherein a second portion of the first client data is stored on a second data storage device that is distinct from the first data storage device; populating and storing a table-info table to indicate which columns in each of the plurality of fact tables are populated with the first client data; evaluating a plurality of content measures based on the populated columns of the plurality of fact tables, the table-info table, and at least one of a role or a security access level of a user to determine and store a set of computable content measures that are supported by the first client data; identifying supported items of the set of items based on the stored set of computable content measures; sending data identifying the supported items to the one or more applications, wherein the data excludes unsupported items; receiving an indication that a portion of the first client data has become unavailable; determining that at least one of the supported items relies on the portion of the first client data; sending second data to the one or more applications, wherein display of the second data excludes the at least one of the supported items; receiving, from the one or more applications, a query that requests values spanning both the first portion and the second portion, and responsive to receiving the query, dividing the query into a plurality of data requests, the plurality of data requests including a first request with respect to the first portion and a second request with respect to the second portion.
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11. A non-transitory computer-readable storage device storing instructions that, when executed by a computer, cause the computer to perform operations comprising: detecting startup of one or more applications in an analytics engine configured to analyze a set of items based on data models and processing models to dynamically generate graphical user analysis interfaces; populating columns of a plurality of fact tables with first client data, wherein a first portion of the first client data is stored on a first data storage device, and wherein a second portion of the first client data is stored on a second data storage device that is distinct from the first data storage device; populating and storing a table-info table to indicate which columns in each of the plurality of fact tables are populated with the first client data; evaluating a plurality of content measures based on the populated columns of the plurality of fact tables, the table-info table, and at least one of a role or a security access level of a user to determine and store a set of computable content measures that are supported by the first client data; identifying supported items of the set of items based on the stored set of computable content measures; sending data identifying the supported items to the one or more applications, wherein the data excludes unsupported items; receiving an indication that a portion of the first client data has become unavailable; determining that at least one of the supported items relies on the portion of the first client data; sending second data to the one or more applications, wherein display of the second data excludes the at least one of the supported items; receiving, from the one or more applications, a query that requests values spanning both the first portion and the second portion, and responsive to receiving the query, dividing the query into a plurality of data requests, the plurality of data requests including a first request with respect to the first portion and a second request with respect to the second portion. 13. The non-transitory computer-readable storage device of claim 11 , wherein the stored set of computable content measures is reusable to identify the supported items while the first client data remains available and while the at least one of the role or the security access level of the user is unchanged.
| 0.651197 |
1. An apparatus for digital forensics, comprising: a page file extractor for extracting a page file stored in a target storage medium; a stored-page feature extractor for extracting features of pages stored in the extracted page file; a page classifier for comparing the extracted features of the pages with at least one predetermined classification criterion, and classifying the pages according to the comparison results; and a digital forensics unit for performing digital forensics according to the classified pages, wherein the features of the pages correspond to binary data distribution characteristics according to memory addresses in the pages.
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1. An apparatus for digital forensics, comprising: a page file extractor for extracting a page file stored in a target storage medium; a stored-page feature extractor for extracting features of pages stored in the extracted page file; a page classifier for comparing the extracted features of the pages with at least one predetermined classification criterion, and classifying the pages according to the comparison results; and a digital forensics unit for performing digital forensics according to the classified pages, wherein the features of the pages correspond to binary data distribution characteristics according to memory addresses in the pages. 7. The apparatus of claim 1 , wherein the pages are 4 Kbytes in size.
| 0.602581 |
13. A non-transitory computer readable medium having computer executable program code embodied thereon, the computer executable program code configured to cause a computing device to perform the operations of: partitioning a digital real estate document into a set of token patterns; normalizing the set of token patterns to obtain a normalized set of token patterns; applying a first filter to the normalized set of token patterns to obtain a first indicator of whether the digital real estate document is a member of a first document classification and a first confidence score for the first indicator; applying a second filter to the normalized set of token patterns to obtain a second indicator of whether the digital real estate document is a member of a second document classification and a second confidence score for the second indicator; and determining a document classification of the digital real estate document from a set of indicators associated with the digital real estate document and set of confidence scores associated with the set of indicators, wherein the set of indicators includes the first and second indicators and the set of confidence scores includes the first and second confidence scores; wherein the first filter comprises a set of indicative token patterns and a set of non-indicative token patterns, wherein the indicative token patterns are those associated with the first document classification, wherein the non-indicative token patterns are those not associated with the first document classification; and wherein applying the first filter to the normalized set of token patterns to obtain the first indicator and the first confidence score comprises: performing a determination of whether the digital real estate document is a member of the first document classification based on the set of indicative token patterns, the set of non-indicative token patterns, and the normalized set of token patterns; setting the first indicator according to the determination; and calculating the first confidence score based on a level of confidence in the determination.
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13. A non-transitory computer readable medium having computer executable program code embodied thereon, the computer executable program code configured to cause a computing device to perform the operations of: partitioning a digital real estate document into a set of token patterns; normalizing the set of token patterns to obtain a normalized set of token patterns; applying a first filter to the normalized set of token patterns to obtain a first indicator of whether the digital real estate document is a member of a first document classification and a first confidence score for the first indicator; applying a second filter to the normalized set of token patterns to obtain a second indicator of whether the digital real estate document is a member of a second document classification and a second confidence score for the second indicator; and determining a document classification of the digital real estate document from a set of indicators associated with the digital real estate document and set of confidence scores associated with the set of indicators, wherein the set of indicators includes the first and second indicators and the set of confidence scores includes the first and second confidence scores; wherein the first filter comprises a set of indicative token patterns and a set of non-indicative token patterns, wherein the indicative token patterns are those associated with the first document classification, wherein the non-indicative token patterns are those not associated with the first document classification; and wherein applying the first filter to the normalized set of token patterns to obtain the first indicator and the first confidence score comprises: performing a determination of whether the digital real estate document is a member of the first document classification based on the set of indicative token patterns, the set of non-indicative token patterns, and the normalized set of token patterns; setting the first indicator according to the determination; and calculating the first confidence score based on a level of confidence in the determination. 18. The computer readable medium of claim 13 , wherein the operation of normalizing comprises: locating a candidate token pattern within the set of token patterns; and replacing the candidate token pattern with a normalized token marker.
| 0.574498 |
10. A method for facilitating electronic communication between people who speak different languages, comprising: receiving an SMS message translation session request from a user; sending an SMS translation session grant response to the user; thereafter, receiving an SMS message including content in a first language; translating at least a portion of the content from a first language to a second language; and sending the SMS message with the content translated from the first language to the second language to a recipient directly from a network node at which the step of translating was performed; and re-translating the translated content to the first language and sending the thus re-translated content to an originator of the first SMS message without involvement of the recipient.
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10. A method for facilitating electronic communication between people who speak different languages, comprising: receiving an SMS message translation session request from a user; sending an SMS translation session grant response to the user; thereafter, receiving an SMS message including content in a first language; translating at least a portion of the content from a first language to a second language; and sending the SMS message with the content translated from the first language to the second language to a recipient directly from a network node at which the step of translating was performed; and re-translating the translated content to the first language and sending the thus re-translated content to an originator of the first SMS message without involvement of the recipient. 13. The method of claim 10 , wherein the SMS message translation session is active for a predetermined destination telephone number identified with the SMS message translation session request.
| 0.545121 |
30. A system for improving interaction with a collection of information, the system comprising: at least one processor operatively connected to a memory, wherein the system is configured to execute system components, and the system further comprises: an I/O engine adapted to output at least a portion of an interactive display, wherein the I/O engine is further adapted to output at least one option in response to the comparison made by an analysis engine; a data retrieval engine adapted to generate a set of results, having a plurality of results and a set size, based, at least in part, on a first interpretation of an interaction with the collection of information; an analysis engine adapted to evaluate the set of results using a measure of distinctiveness, wherein the analysis engine is further adapted to compare the measure of distinctiveness for the set of results against a measure of distinctiveness of a candidate set; and a generation engine adapted to generate at least one candidate set, having a plurality of results and a candidate set size, based, at least in part, on a second interpretation of the interaction with the collection of information, wherein the generation engine is further configured to generate a second result for the interaction with the collection of information from at least the set of results and the at least one candidate set based on the compared measures of distinctiveness.
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30. A system for improving interaction with a collection of information, the system comprising: at least one processor operatively connected to a memory, wherein the system is configured to execute system components, and the system further comprises: an I/O engine adapted to output at least a portion of an interactive display, wherein the I/O engine is further adapted to output at least one option in response to the comparison made by an analysis engine; a data retrieval engine adapted to generate a set of results, having a plurality of results and a set size, based, at least in part, on a first interpretation of an interaction with the collection of information; an analysis engine adapted to evaluate the set of results using a measure of distinctiveness, wherein the analysis engine is further adapted to compare the measure of distinctiveness for the set of results against a measure of distinctiveness of a candidate set; and a generation engine adapted to generate at least one candidate set, having a plurality of results and a candidate set size, based, at least in part, on a second interpretation of the interaction with the collection of information, wherein the generation engine is further configured to generate a second result for the interaction with the collection of information from at least the set of results and the at least one candidate set based on the compared measures of distinctiveness. 44. The system according to claim 30 , wherein the analysis engine is further adapted to identify similar candidate sets based on the act of comparing the measure of distinctiveness.
| 0.529468 |
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