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AIPD_nlp_sentence_dataset

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10. An apparatus for automatically managing a dialogue with a user, the apparatus comprising: a processor; and a memory with computer code instructions stored thereon, the processor and the memory, with the computer code instructions, being configured to cause the apparatus to: generate a generic semantic representation based upon received user-input data, the generic semantic representation being independent of a language and an input modality associated with the received user-input data, the generic semantic representation comprising at least one of a list of semantic slots and a sequence of nested semantic slots; determine a user-intention based upon the received user-input data, the generic semantic representation, and at least one of: a maintained state of the dialogue, concept data representing one or more concepts, and history data representing history of the dialogue; perform selection of a list of data items based on any of the concept data representing the one or more concepts and attribute data representing one or more attributes associated with the generic semantic representation; and send output data indicative of one or more actions for a dialogue application to perform, the one or more actions being determined based on a result of said determining the user-intention.

10. An apparatus for automatically managing a dialogue with a user, the apparatus comprising: a processor; and a memory with computer code instructions stored thereon, the processor and the memory, with the computer code instructions, being configured to cause the apparatus to: generate a generic semantic representation based upon received user-input data, the generic semantic representation being independent of a language and an input modality associated with the received user-input data, the generic semantic representation comprising at least one of a list of semantic slots and a sequence of nested semantic slots; determine a user-intention based upon the received user-input data, the generic semantic representation, and at least one of: a maintained state of the dialogue, concept data representing one or more concepts, and history data representing history of the dialogue; perform selection of a list of data items based on any of the concept data representing the one or more concepts and attribute data representing one or more attributes associated with the generic semantic representation; and send output data indicative of one or more actions for a dialogue application to perform, the one or more actions being determined based on a result of said determining the user-intention. 14. The apparatus according to claim 10 , wherein the processor and the memory, with the computer code instructions, are configured to further cause the apparatus to perform selection based upon at least one of a rank and an instance associated with the list of data items.

41. A method, comprising: A) causing a first search query to be executed to generate a first information set comprising a first plurality of items, wherein each item of the first plurality of items is associated with at least one characteristic; B) representing at least some of the first information set in a first presentation; C) in response to a user input, saving at least one item of the first plurality of items in a location specified by the user; D) causing a second search query to be executed to generate a second information set comprising a second plurality of items, wherein each item of the second plurality of items is associated with at least one characteristic; E) representing at least some of the second information set in a second presentation; F) in response to a user input, retrieving at least one saved item of the first plurality of items; and G) generating a third search query based on a combination of the at least one saved item of the first plurality of items and at least one of the second plurality of items; wherein generating a third search query based on the combination of the at least one saved item and the at least one of the second plurality of items comprises applying an evolutionary algorithm to a genetic string comprising a plurality of characteristics associated with the at least one saved item and applying an evolutionary algorithm to a genetic string comprising a plurality of characteristics associated with the at least one of the second plurality of items; wherein at least one characteristic associated with each item is chosen from a group comprising: at least one descriptor made available by a search engine or web directory service; at least one tag; at least one keyword; at least one classification-oriented identifier; at least one categorization-oriented identifier; and at least one semantic web-oriented identifier; and wherein at least one characteristic associated with each item is not a word or phrase selected from text displayed as part of the item.

41. A method, comprising: A) causing a first search query to be executed to generate a first information set comprising a first plurality of items, wherein each item of the first plurality of items is associated with at least one characteristic; B) representing at least some of the first information set in a first presentation; C) in response to a user input, saving at least one item of the first plurality of items in a location specified by the user; D) causing a second search query to be executed to generate a second information set comprising a second plurality of items, wherein each item of the second plurality of items is associated with at least one characteristic; E) representing at least some of the second information set in a second presentation; F) in response to a user input, retrieving at least one saved item of the first plurality of items; and G) generating a third search query based on a combination of the at least one saved item of the first plurality of items and at least one of the second plurality of items; wherein generating a third search query based on the combination of the at least one saved item and the at least one of the second plurality of items comprises applying an evolutionary algorithm to a genetic string comprising a plurality of characteristics associated with the at least one saved item and applying an evolutionary algorithm to a genetic string comprising a plurality of characteristics associated with the at least one of the second plurality of items; wherein at least one characteristic associated with each item is chosen from a group comprising: at least one descriptor made available by a search engine or web directory service; at least one tag; at least one keyword; at least one classification-oriented identifier; at least one categorization-oriented identifier; and at least one semantic web-oriented identifier; and wherein at least one characteristic associated with each item is not a word or phrase selected from text displayed as part of the item. 50. The method of claim 41 , wherein the evolutionary algorithm comprises a crossover operator configured to combine genes of two genetic strings to produce at least one offspring, and a mutation operator configured to delete at least one gene of a genetic string and/or add at least one random gene to a genetic string, and wherein applying the evolutionary algorithm to generate a third search query comprises: applying the crossover operator to genetic strings respectively comprising a plurality of characteristics associated with the at least one saved item and at least one of the second plurality of items to generate at least one offspring; and applying the mutation operator to at least one of the genetic string comprising a plurality of characteristics associated with the at least one saved item, the genetic string comprising a plurality of characteristics associated with at least one of the second plurality of items, and the genetic string comprising a plurality of characteristics associated with the at least one offspring.

2. The method according to claim 1 , wherein the second percentage at (c) is computed by giving weight only to those keywords and their set of neighboring keywords in the first list that match in the second list and a threshold percentage of the keywords in their set of neighboring keywords.

2. The method according to claim 1 , wherein the second percentage at (c) is computed by giving weight only to those keywords and their set of neighboring keywords in the first list that match in the second list and a threshold percentage of the keywords in their set of neighboring keywords. 6. The method according to claim 2 , wherein the first list of rated keywords includes one or more keywords translated from a second language different from a first language that is identified as being a primary language of the first document.

20. The system for utilizing a self-service source of claim 19 , wherein the first user and the second user each belong to a common group of users, wherein the common group of users is associated with a set of access rights.

20. The system for utilizing a self-service source of claim 19 , wherein the first user and the second user each belong to a common group of users, wherein the common group of users is associated with a set of access rights. 21. The system for utilizing a self-service source of claim 20 , wherein the access control list that controls access to the user-subscribed source provides access to users who belong to the common group.

1. A computer implemented method for creating a compact tree node representation of an extensible markup language (XML) document, the method comprising: creating a compact tree node representation of an extensible markup language (XML) document by: allocating a first portion of memory of a main memory of a computer to store a first memory block for storing an in-memory instance of an XML tree index data structure for the XML document, the in-memory instance of the XML tree index data structure comprising an array of rows in which each row holds a node identifier, one or more pointers referencing to one or more children nodes, allocating a second portion of the memory of the main memory of the computer to store one or more separate data structures for storing at least a portion of the node data for the XML document, the one or more separate data structures each storing a different type of node data; traversing the XML document from a first node to a final node and through at least one intermediate node; and processing traversed nodes of the XML document, the processing comprising: in determining a traversed node is an element node, adding the element node to the first portion of the main memory and copying an element name of the element node into the one or more separate data structures for storing at least a portion of the node data; in determining the traversed node is a text node, populating a text node index into the first portion of the main memory and copying text node values into the one or more separate data structures for storing at least a portion of the node data, the text node values copied being accessible via the text node index; in determining the traversed node is an attribute node, populating an attribute node index into the first portion of the main memory and copying an attribute name and attribute value into the one or more separate data structures, the attribute value copied being accessible via the attribute node index.

1. A computer implemented method for creating a compact tree node representation of an extensible markup language (XML) document, the method comprising: creating a compact tree node representation of an extensible markup language (XML) document by: allocating a first portion of memory of a main memory of a computer to store a first memory block for storing an in-memory instance of an XML tree index data structure for the XML document, the in-memory instance of the XML tree index data structure comprising an array of rows in which each row holds a node identifier, one or more pointers referencing to one or more children nodes, allocating a second portion of the memory of the main memory of the computer to store one or more separate data structures for storing at least a portion of the node data for the XML document, the one or more separate data structures each storing a different type of node data; traversing the XML document from a first node to a final node and through at least one intermediate node; and processing traversed nodes of the XML document, the processing comprising: in determining a traversed node is an element node, adding the element node to the first portion of the main memory and copying an element name of the element node into the one or more separate data structures for storing at least a portion of the node data; in determining the traversed node is a text node, populating a text node index into the first portion of the main memory and copying text node values into the one or more separate data structures for storing at least a portion of the node data, the text node values copied being accessible via the text node index; in determining the traversed node is an attribute node, populating an attribute node index into the first portion of the main memory and copying an attribute name and attribute value into the one or more separate data structures, the attribute value copied being accessible via the attribute node index. 3. The method of claim 1 , wherein the different type of node data is selected from a group consisting of an element node, a text node, and an attribute node.

22. A non-transitory computer readable medium for processing a voice input provided in response to a prompt, the computer readable medium storing instructions for: automatically providing a sequence of prompts, wherein each prompt is associated with a respective time period of a plurality of time periods; receiving a voice input while a prompt of the sequence of prompts is being provided; identifying a characteristic time associated with the received voice input; identifying a time period of the plurality of time periods that includes the characteristic time; and applying the received voice input to a respective prompt of the sequence of prompts associated with the identified time period.

22. A non-transitory computer readable medium for processing a voice input provided in response to a prompt, the computer readable medium storing instructions for: automatically providing a sequence of prompts, wherein each prompt is associated with a respective time period of a plurality of time periods; receiving a voice input while a prompt of the sequence of prompts is being provided; identifying a characteristic time associated with the received voice input; identifying a time period of the plurality of time periods that includes the characteristic time; and applying the received voice input to a respective prompt of the sequence of prompts associated with the identified time period. 23. The computer readable medium claim 22 , further storing instructions for: defining, for each prompt, an initial time stamp and a final time stamp, wherein the period between the initial time stamp and the final time stamp constitutes the time period associated with the prompt.

9. A speech processing system comprising: a processor; and a memory coupled to the processor, the memory comprising: a communication module configured to receive speech sample in Oriya English for speech recognition; a data analysis module configured to: determine values of a plurality of speech parameters for the received sample based on Fujisaki model, wherein the values of the plurality of speech parameters comprises base frequencies of the speech samples, phrase components of the speech samples, and associated accent components with the speech samples, wherein each of the speech samples are processed by filtering each of the speech samples using different linear filters, and wherein the linear filters model a baseline component which corresponds to speaker declination and model a micro-prosodic variations which corresponds to the accent components of each of the speech samples; and a data processing module configured to recognize the speech sample based on governing language rules through Hidden Markov Model (HMM).

9. A speech processing system comprising: a processor; and a memory coupled to the processor, the memory comprising: a communication module configured to receive speech sample in Oriya English for speech recognition; a data analysis module configured to: determine values of a plurality of speech parameters for the received sample based on Fujisaki model, wherein the values of the plurality of speech parameters comprises base frequencies of the speech samples, phrase components of the speech samples, and associated accent components with the speech samples, wherein each of the speech samples are processed by filtering each of the speech samples using different linear filters, and wherein the linear filters model a baseline component which corresponds to speaker declination and model a micro-prosodic variations which corresponds to the accent components of each of the speech samples; and a data processing module configured to recognize the speech sample based on governing language rules through Hidden Markov Model (HMM). 11. The speech processing system as claimed in claim 9 , wherein the recognizing of the speech sample is based on one or more of geometrically averaged output probability likelihood method and centralized parametric spaced method of HMM.

18. The method according to claim 12 , wherein the method of forming at least one structure granule for a sub-collection of values for which an ordinal number was assigned to a word in the structure dictionary comprises: for each of symbols that said word is formed from, selecting one or more of the methods of forming symbol granules corresponding to said symbols, said symbol granules representing summarized information about said portions of the values corresponding to said symbols; and forming said at least one structure granule by gathering summarized information represented by said symbol granules.

18. The method according to claim 12 , wherein the method of forming at least one structure granule for a sub-collection of values for which an ordinal number was assigned to a word in the structure dictionary comprises: for each of symbols that said word is formed from, selecting one or more of the methods of forming symbol granules corresponding to said symbols, said symbol granules representing summarized information about said portions of the values corresponding to said symbols; and forming said at least one structure granule by gathering summarized information represented by said symbol granules. 20. The method according to claim 18 , wherein the method of forming the symbol granule corresponding to a symbol from which a word in the structure dictionary is formed comprises: assigning the collection of portions of the values corresponding to said symbol with a sub-structure dictionary containing sub-structures defined based on at least one of: interaction with a user of the system, wherein the algorithms automatically detect sub-structures occurring among said portions of the values corresponding to said symbol, or predefined information about sub-structures relevant to said values' portions corresponding to said symbol; forming a sub-match granule by representing summarized information how the portions of the values corresponding to said symbol match said sub-structure; forming sub-structure granules representing summarized information about the portions of the values corresponding to said symbol that match particular sub-structures; and forming said symbol granule as gathering summarized information represented by said sub-match granule and said sub-structure granules.

8. The method of claim 1 , wherein the received mobile application controls a device separate from the mobile device, the mobile computing device and the controlled device both connected to a local wireless network.

8. The method of claim 1 , wherein the received mobile application controls a device separate from the mobile device, the mobile computing device and the controlled device both connected to a local wireless network. 10. The method of claim 8 , wherein the local wireless network is a Bluetooth network.

17. In an apparatus for displaying numerical value information, comprising: means for receiving in a coded fashion, a signal representative of multi-digit numerical value information, each digit being represented by one of a set of numerals, converting means for converting said coded multi-digit numerical value information signal into a kind of character signal for representing said coded multi-digit numerical value information in a given character form of numeral characters 0, 1, 2, 3, . . . 9, and display means responsive to said kind of character signals for displaying said multi-digit numerical value information by said given kind of character form of numeral characters, 0, 1, 2, 3, . . . 9, wherein said display means comprises multi-digit display positions and each digit numeral is displayed in each corresponding digit display position, the improvement comprising at least one further display means for displaying said numerical value information selectively in a form distinct from said given kind of character form, and wherein said converting means is adapted to convert said multi-digit numerical value information signal into a plurality of different kinds of character signals for representing said coded multi-digit numerical value information in a corresponding plurality of different kinds of character forms of numeral characters 0, 1, 2, 3, . . . 9, and wherein said display means and said at least one further display means are adapted to be responsive, respectively, to said different kinds of character signals to display said multi-digit numerical value information respectively by corresponding said different kinds of character forms of numeral characters 0, 1, 2, 3, . . . 9, which are different in character forms.

17. In an apparatus for displaying numerical value information, comprising: means for receiving in a coded fashion, a signal representative of multi-digit numerical value information, each digit being represented by one of a set of numerals, converting means for converting said coded multi-digit numerical value information signal into a kind of character signal for representing said coded multi-digit numerical value information in a given character form of numeral characters 0, 1, 2, 3, . . . 9, and display means responsive to said kind of character signals for displaying said multi-digit numerical value information by said given kind of character form of numeral characters, 0, 1, 2, 3, . . . 9, wherein said display means comprises multi-digit display positions and each digit numeral is displayed in each corresponding digit display position, the improvement comprising at least one further display means for displaying said numerical value information selectively in a form distinct from said given kind of character form, and wherein said converting means is adapted to convert said multi-digit numerical value information signal into a plurality of different kinds of character signals for representing said coded multi-digit numerical value information in a corresponding plurality of different kinds of character forms of numeral characters 0, 1, 2, 3, . . . 9, and wherein said display means and said at least one further display means are adapted to be responsive, respectively, to said different kinds of character signals to display said multi-digit numerical value information respectively by corresponding said different kinds of character forms of numeral characters 0, 1, 2, 3, . . . 9, which are different in character forms. 18. In an apparatus in accordance with claim 17, which further comprises means for selectively withdrawing one of said different kinds of character signals for representing said coded multi-digit numerical value information by one of said different kinds of character forms of numeral characters, 0, 1, 2, 3, . . . 9, respectively, and wherein said display means is adapted to display said multi-digit numerical value information by said selected one character form of said different kinds of character forms of numeral characters 0, 1, 2, 3, . . . 9.

1. A method for implementing aggregation combination using rollup depth lists for optimizing database query processing comprising: identifying grouping sets and combining multiple grouping sets into multiple single rollups; each grouping set including a collection of elements; assigning rollup depth lists defining the levels of grouping to be performed for each of said multiple single rollups; each said rollup depth list including respective element counts for each element; wherein identifying grouping sets and combining multiple grouping sets into multiple single rollups and assigning rollup depth lists includes: identifying a list of source groups; each source group including a collection of elements; identifying one source group, reading the source group, initially assigning each source group element a count of zero and assigning a last element a count of one; storing the source group within a target group list; identifying one target group, processing the stored source group and the identified target group in the target group list, such that the source group elements are sorted to match element order of target group elements; and combining the processed stored source group with the processed identified target group into a single rollup, wherein the element counts of the rollup depth list for said single rollup are determined based on said combining; generating an access plan utilizing said multiple single rollups; and said corresponding rollup depth lists; providing an execution engine executing a database query with said access plan; and said execution engine using respective element counts of each rollup depth list to identify how many times to perform each level of aggregation of elements within each of said multiple single rollups, optimizing database query processing.

1. A method for implementing aggregation combination using rollup depth lists for optimizing database query processing comprising: identifying grouping sets and combining multiple grouping sets into multiple single rollups; each grouping set including a collection of elements; assigning rollup depth lists defining the levels of grouping to be performed for each of said multiple single rollups; each said rollup depth list including respective element counts for each element; wherein identifying grouping sets and combining multiple grouping sets into multiple single rollups and assigning rollup depth lists includes: identifying a list of source groups; each source group including a collection of elements; identifying one source group, reading the source group, initially assigning each source group element a count of zero and assigning a last element a count of one; storing the source group within a target group list; identifying one target group, processing the stored source group and the identified target group in the target group list, such that the source group elements are sorted to match element order of target group elements; and combining the processed stored source group with the processed identified target group into a single rollup, wherein the element counts of the rollup depth list for said single rollup are determined based on said combining; generating an access plan utilizing said multiple single rollups; and said corresponding rollup depth lists; providing an execution engine executing a database query with said access plan; and said execution engine using respective element counts of each rollup depth list to identify how many times to perform each level of aggregation of elements within each of said multiple single rollups, optimizing database query processing. 4. The method for implementing aggregation combination using rollup depth lists for optimizing database query processing as recited in claim 1 includes responsive to not identifying the source group elements matching the target group elements, positioning to first non-matching element between source and target groups.

1. A barcoded indicator operative to provide a machine-readable indication of exceedance of at least one threshold by at least one product quality affecting parameter, said barcoded indicator comprising: at least a first barcode including at least a first set of initially uncolored, colorable areas including at least one initially uncolored colorable area, positioned at locations between bars of said first barcode when said first barcode is in a first barcode readable state prior to exceedance of said at least one threshold; at least a second barcode including at least a second set of initially uncolored, colorable areas including at least one initially uncolored colorable area, positioned at locations of bars of said second barcode which appear only when said second barcode is in a second barcode readable state following exceedance of said at least one threshold, a coloring agent located at a first location on said indicator; and a coloring agent pathway operative to allow said coloring agent to move from said first location to said first and second sets of colorable areas for coloring thereof, said at least a second barcode being in a second barcode unreadable state prior to exceedance of said at least one threshold wherein as the result of said at least a second set being uncolored, more than a single narrow barcode bar is missing from said at least a second barcode, and upon exceedance of said at least one threshold said at least a first barcode becoming unreadable as the result of coloring of at least a portion of at least one colorable area forming part of said at least a first set of colorable areas and generally simultaneously said at least a second barcode becoming readable as the result of coloring of said at least a second set of colorable areas.

1. A barcoded indicator operative to provide a machine-readable indication of exceedance of at least one threshold by at least one product quality affecting parameter, said barcoded indicator comprising: at least a first barcode including at least a first set of initially uncolored, colorable areas including at least one initially uncolored colorable area, positioned at locations between bars of said first barcode when said first barcode is in a first barcode readable state prior to exceedance of said at least one threshold; at least a second barcode including at least a second set of initially uncolored, colorable areas including at least one initially uncolored colorable area, positioned at locations of bars of said second barcode which appear only when said second barcode is in a second barcode readable state following exceedance of said at least one threshold, a coloring agent located at a first location on said indicator; and a coloring agent pathway operative to allow said coloring agent to move from said first location to said first and second sets of colorable areas for coloring thereof, said at least a second barcode being in a second barcode unreadable state prior to exceedance of said at least one threshold wherein as the result of said at least a second set being uncolored, more than a single narrow barcode bar is missing from said at least a second barcode, and upon exceedance of said at least one threshold said at least a first barcode becoming unreadable as the result of coloring of at least a portion of at least one colorable area forming part of said at least a first set of colorable areas and generally simultaneously said at least a second barcode becoming readable as the result of coloring of said at least a second set of colorable areas. 6. A barcoded indicator according to claim 1 and wherein said at least one threshold includes at least one time and temperature threshold.

4. The method of claim 1 , further comprising: receiving an input to a calendar function associated with the electronic collaboration forum, the input scheduling an event associated with the collaboration team, wherein the scheduled event relates to one or more advisor services to be provided to the client by the collaboration team; and scheduling the event in the calendar function associated with the electronic collaboration forum.

4. The method of claim 1 , further comprising: receiving an input to a calendar function associated with the electronic collaboration forum, the input scheduling an event associated with the collaboration team, wherein the scheduled event relates to one or more advisor services to be provided to the client by the collaboration team; and scheduling the event in the calendar function associated with the electronic collaboration forum. 5. The method of claim 4 , further comprising: receiving additional inputs to the calendar function of the electronic collaboration forum, the additional inputs being related to additional interactions involving the client and/or advisors from the collaboration team, the additional interactions being related to the one or more advisor services to be provided to the client that are related to the scheduled event; and scheduling the additional interactions in the calendar function in response to the additional inputs.

1. A method of unit and regression testing for model aware domain specific languages, the method comprising a processor implemented steps of: modeling an operation present in a repository for a test driven development process, the operation comprising a specification of designing requirements for testing to generate an application library code; generating a blank operation template from the application library code thereof corresponding to the operation, the blank operation template comprising an input XML and an expected output XML thereof, wherein the input XML refers to a set of test cases, and the expected output XML refers to results that are expected post running of the set of the test cases; building an executable unit test load to integrate business logic therein by using predefined unit test stubs and system libraries; executing the unit test load and comparing a resulting actual output XML with the expected output XML to determine states of the set of the test cases, wherein a state of a test case refers to either pass state or fail state; identifying defects unveiled during the comparison; and versioning the test cases for the future reusability by selecting valid test cases with the pass states, wherein the valid test cases are used to build regression test suites, wherein the regression test are used for performing regression testing, wherein regression testing comprises executing an entirety of the regression test suites to uncover unknown defects to all parts of an enterprise computer application due to a change at a unit level of the enterprise computer application.

1. A method of unit and regression testing for model aware domain specific languages, the method comprising a processor implemented steps of: modeling an operation present in a repository for a test driven development process, the operation comprising a specification of designing requirements for testing to generate an application library code; generating a blank operation template from the application library code thereof corresponding to the operation, the blank operation template comprising an input XML and an expected output XML thereof, wherein the input XML refers to a set of test cases, and the expected output XML refers to results that are expected post running of the set of the test cases; building an executable unit test load to integrate business logic therein by using predefined unit test stubs and system libraries; executing the unit test load and comparing a resulting actual output XML with the expected output XML to determine states of the set of the test cases, wherein a state of a test case refers to either pass state or fail state; identifying defects unveiled during the comparison; and versioning the test cases for the future reusability by selecting valid test cases with the pass states, wherein the valid test cases are used to build regression test suites, wherein the regression test are used for performing regression testing, wherein regression testing comprises executing an entirety of the regression test suites to uncover unknown defects to all parts of an enterprise computer application due to a change at a unit level of the enterprise computer application. 3. The method of claim 1 , wherein the repository consists of artifacts but not limited to input XML files, expected output XML files, regression test suite metadata and the like.

1. A computer implemented method for using Superword-Level Parallelism (SLP) in processing a plurality of statements, wherein the statements are associated with an array having a number of array positions, and each statement includes one or more expressions, the computer implemented method comprising the steps of: gathering, by one or more computer processors, isomorphic and non-isomorphic expressions for each of the statements into a merge stream of mixed isomorphic and non-isomorphic expressions, the merge stream being furnished with a location for each gathered isomorphic expression and each gathered non-isomorphic expression, wherein the location for a given expression is associated with one of the positions of the array; selectively identifying, by the one or more computer processors, one or more sets of expressions in the merge stream that are aggregatable by a SLP packing operation, and applying the SLP packing operation to the identified one or more sets of expressions in order to merge the identified one or more sets of expressions into one or more isomorphic sub-streams; selectively combining, by the one or more computer processors, the expressions of the one or more isomorphic sub-streams, and other non-isomorphic expressions of the merge stream, into a number of input vectors that are substantially equal in length to one another; generating, by the one or more computer processors, a location vector containing the respective locations for all of the isomorphic and non-isomorphic expressions in the merge stream; generating, by the one or more computer processors, an output stream comprising the expressions of the number of input vectors, wherein said expressions are arranged in an order in the output stream that is determined by the respective locations contained in the location vector; and preserving, by the one or more computer processors, at least one location vector or a set of location vectors at each step during processing.

1. A computer implemented method for using Superword-Level Parallelism (SLP) in processing a plurality of statements, wherein the statements are associated with an array having a number of array positions, and each statement includes one or more expressions, the computer implemented method comprising the steps of: gathering, by one or more computer processors, isomorphic and non-isomorphic expressions for each of the statements into a merge stream of mixed isomorphic and non-isomorphic expressions, the merge stream being furnished with a location for each gathered isomorphic expression and each gathered non-isomorphic expression, wherein the location for a given expression is associated with one of the positions of the array; selectively identifying, by the one or more computer processors, one or more sets of expressions in the merge stream that are aggregatable by a SLP packing operation, and applying the SLP packing operation to the identified one or more sets of expressions in order to merge the identified one or more sets of expressions into one or more isomorphic sub-streams; selectively combining, by the one or more computer processors, the expressions of the one or more isomorphic sub-streams, and other non-isomorphic expressions of the merge stream, into a number of input vectors that are substantially equal in length to one another; generating, by the one or more computer processors, a location vector containing the respective locations for all of the isomorphic and non-isomorphic expressions in the merge stream; generating, by the one or more computer processors, an output stream comprising the expressions of the number of input vectors, wherein said expressions are arranged in an order in the output stream that is determined by the respective locations contained in the location vector; and preserving, by the one or more computer processors, at least one location vector or a set of location vectors at each step during processing. 9. The computer implemented method of claim 1 , wherein: the number of the input vectors, and a maximum data capacity of respective vectors, are selected to comply with requirements of a specified data processing device, and data found in the output stream is selectively grouped, to further comply with said requirements.

16. The program product of claim 15 , wherein the visited document data further comprises visited document activity data and a plurality of one or more first visited document addresses and first visited document descriptions are displayed prioritized based on the visited document activity data for each of the first visited document addresses.

16. The program product of claim 15 , wherein the visited document data further comprises visited document activity data and a plurality of one or more first visited document addresses and first visited document descriptions are displayed prioritized based on the visited document activity data for each of the first visited document addresses. 17. The program product of claim 16 , wherein the plurality of the one or more of the first visited document addresses and the first visited document descriptions are prioritized by document visit duration.

19. One or more non-transitory storage media storing instructions which, when executed by one or more processors, cause: generating a parent process for a parent document; generating a first child process for a first child document of the parent document; generating a second child process for a second child document of the parent document, wherein the second child document is different than the first child document; generating one or more third child processes, each for a different child document of one or more child documents that do not include the first child document or the second child document; sending target data from the first child process to the second child process; sending the target data from the first child process to each of the one or more third child processes; wherein the target data includes one or more of: a highlight instruction that the second child process is to use to visually distinguish at least a portion of the second child document, or user data that the second child process uses to dynamically display content or to generate content within the second child document, wherein the content is different than the user data, wherein the user data includes profile information that is stored in association with a particular user who is a member of a particular social network that includes the profile information.

19. One or more non-transitory storage media storing instructions which, when executed by one or more processors, cause: generating a parent process for a parent document; generating a first child process for a first child document of the parent document; generating a second child process for a second child document of the parent document, wherein the second child document is different than the first child document; generating one or more third child processes, each for a different child document of one or more child documents that do not include the first child document or the second child document; sending target data from the first child process to the second child process; sending the target data from the first child process to each of the one or more third child processes; wherein the target data includes one or more of: a highlight instruction that the second child process is to use to visually distinguish at least a portion of the second child document, or user data that the second child process uses to dynamically display content or to generate content within the second child document, wherein the content is different than the user data, wherein the user data includes profile information that is stored in association with a particular user who is a member of a particular social network that includes the profile information. 20. The one or more non-transitory storage media of claim 19 , wherein sending target data comprises: creating, by the first child process, a first message; and sending, by the first child process, directly to the second child document, the first message.

1. An image processing apparatus comprising: an image processor configured to process an image signal and display the processed image; an audio input configured to input a user's speech; a storage configured to store a list of simple voice commands and an operation corresponding to each of the simple voice commands; a communication device configured to communicate with a server that analyzes a descriptive voice command and determine an operation corresponding to the descriptive voice command; an audio processor configured to process a voice command corresponding to the user's speech to conduct the operation corresponding to a simple voice command in the list if the first voice command is the simple voice command in the list, and to transmit the voice command to the server through the communication device if the voice command is not the simple voice command in the list; and a controller configured to, if an operation for the voice command determined by the server corresponds to the simple voice command in the list, display a guide image which recommends the corresponding simple voice command in the list, wherein the simple voice command is shorter than the descriptive voice command.

1. An image processing apparatus comprising: an image processor configured to process an image signal and display the processed image; an audio input configured to input a user's speech; a storage configured to store a list of simple voice commands and an operation corresponding to each of the simple voice commands; a communication device configured to communicate with a server that analyzes a descriptive voice command and determine an operation corresponding to the descriptive voice command; an audio processor configured to process a voice command corresponding to the user's speech to conduct the operation corresponding to a simple voice command in the list if the first voice command is the simple voice command in the list, and to transmit the voice command to the server through the communication device if the voice command is not the simple voice command in the list; and a controller configured to, if an operation for the voice command determined by the server corresponds to the simple voice command in the list, display a guide image which recommends the corresponding simple voice command in the list, wherein the simple voice command is shorter than the descriptive voice command. 6. The image processing apparatus of claim 1 , further comprising a display configured to display the image signal processed by the image processor as an image.

1. A method of determining reliability of electronic documents associated with an event occurring in connection with a computing device, comprising, with a processor: with a composer module executed by the processor: obtaining text within an event message, the event message being generated as a result of change in state of a process within the computing device; composing a number of search queries using the text within the event message as input in the search queries; searching for a number of electronic documents via a network, said searching performed based on the composed search queries; and ranking the electronic documents identified by said searching based upon an indication of reliability in addressing the event associated with the event message, in which ranking the electronic documents comprises applying a content source ranking criteria, in which applying a content source ranking criteria to the electronic documents identified by the searching comprises: collecting a number of top search results; and extracting a number of domains from a number of electronic documents within the search results.

1. A method of determining reliability of electronic documents associated with an event occurring in connection with a computing device, comprising, with a processor: with a composer module executed by the processor: obtaining text within an event message, the event message being generated as a result of change in state of a process within the computing device; composing a number of search queries using the text within the event message as input in the search queries; searching for a number of electronic documents via a network, said searching performed based on the composed search queries; and ranking the electronic documents identified by said searching based upon an indication of reliability in addressing the event associated with the event message, in which ranking the electronic documents comprises applying a content source ranking criteria, in which applying a content source ranking criteria to the electronic documents identified by the searching comprises: collecting a number of top search results; and extracting a number of domains from a number of electronic documents within the search results. 3. The method of claim 1 , further comprising adding the result of a monotonically decreasing function to a count if the domain does not appear in a higher ranking search result.

22. The data processing system of claim 21, which further comprises: a second processor means including an accumulating means coupled to said selection means, for accumulating an error count of said first guess character; and second processor means computing a new value for said adaptive probability weighting factor for at least one of said plurality of character recognition means, by modifying said adaptive probability weighting factor with a value derived from said error count.

22. The data processing system of claim 21, which further comprises: a second processor means including an accumulating means coupled to said selection means, for accumulating an error count of said first guess character; and second processor means computing a new value for said adaptive probability weighting factor for at least one of said plurality of character recognition means, by modifying said adaptive probability weighting factor with a value derived from said error count. 23. The data processing system of claim 22, which further comprises: said first processor means coupled to said second processor means, for receiving said new value for said adaptive probability weighting factor and computing a recognition means choice confidence factor in said data processing system, as a product of said new value for said adaptive probability weighting factor, times a difference between said first confidence value and said second confidence value.

1. A computer-implemented method for spell checking, comprising the steps of: providing a user with a user interface adapted for managing files stored on at least one computer, the files being user files comprising words that are characteristic of a user's lexicon usage; receiving via the user interface a user action involving at least one first word; returning via the user interface at least one second word, as a notification that the first word is presumably erroneous and to be corrected by the second word, selected according to: a distance from said at least one second word to said at least one first word; and data of occurrence related to a number of occurrences of said at least one second word in said files, wherein a second word with a higher number of occurrences in said files is more likely to be selected, wherein: at the step of returning said at least one second word, said at least one second word is first selected according to said data of occurrence and then selected according to said distance.

1. A computer-implemented method for spell checking, comprising the steps of: providing a user with a user interface adapted for managing files stored on at least one computer, the files being user files comprising words that are characteristic of a user's lexicon usage; receiving via the user interface a user action involving at least one first word; returning via the user interface at least one second word, as a notification that the first word is presumably erroneous and to be corrected by the second word, selected according to: a distance from said at least one second word to said at least one first word; and data of occurrence related to a number of occurrences of said at least one second word in said files, wherein a second word with a higher number of occurrences in said files is more likely to be selected, wherein: at the step of returning said at least one second word, said at least one second word is first selected according to said data of occurrence and then selected according to said distance. 14. A computer readable medium storing a computer program product comprising instructions to configure a computer system to take the steps of claim 1 .

11. A computer program embodied on a computer-readable medium that creates 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 goal based educational environment, comprising: (a) a code segment that accesses the information in the spreadsheet object component of the rule-based expert system to determine presentation information indicative of a goal; (b) a code segment that utilizes the information in the spreadsheet object component of the rule-based expert system to integrate graphical information in a structured, dynamic business simulation designed by a profiling component to motivate accomplishment of the goal; (c) a code segment that queries a student or answers to one or more questions based on one or more learning objectives of the presentation using a simulated human conversation; and (d) a code segment that monitors the student's answers to the one or more questions to evaluate progress toward the goal and that provides 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.

11. A computer program embodied on a computer-readable medium that creates 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 goal based educational environment, comprising: (a) a code segment that accesses the information in the spreadsheet object component of the rule-based expert system to determine presentation information indicative of a goal; (b) a code segment that utilizes the information in the spreadsheet object component of the rule-based expert system to integrate graphical information in a structured, dynamic business simulation designed by a profiling component to motivate accomplishment of the goal; (c) a code segment that queries a student or answers to one or more questions based on one or more learning objectives of the presentation using a simulated human conversation; and (d) a code segment that monitors the student's answers to the one or more questions to evaluate progress toward the goal and that provides 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. 18. A computer program embodied on a computer-readable medium that creates a business simulation utilizing a rule-based expert system with a spreadsheet object component to provide a goal based educational environment as recited in claim 11, wherein the media information comprises Internet information.

15. A non-transitory computer readable storage medium containing a program for execution by a computer having a display, the program implementing a message notification system to notify a user about incoming messages stored in an inbox, the notification system comprising: a) an input interface to receive size information and originator information about a plurality of messages in the inbox; b) a processing entity coupled to the input interface for generating message visualization structure associated with each of the plurality of messages, for each message: the processing entity determining a first non-textual visual feature of the message visualization structure on the basis of the size information of the respective message and determining a second non-textual visual feature of the message visualization structure on the basis of the originator information of the respective message; c) an output interface for releasing output data to cause the display of the computer to render the visualization structures, the output data defining a graphical environment in which the message visualization structures are rendered on the display simultaneously and convey information about the messages, their respective sizes, and their respective originator information in a non-textual-list manner, wherein at least one of the first non-textual visual feature and the second non-textual visual feature includes an animation of the message visualization structure associated with the corresponding message.

15. A non-transitory computer readable storage medium containing a program for execution by a computer having a display, the program implementing a message notification system to notify a user about incoming messages stored in an inbox, the notification system comprising: a) an input interface to receive size information and originator information about a plurality of messages in the inbox; b) a processing entity coupled to the input interface for generating message visualization structure associated with each of the plurality of messages, for each message: the processing entity determining a first non-textual visual feature of the message visualization structure on the basis of the size information of the respective message and determining a second non-textual visual feature of the message visualization structure on the basis of the originator information of the respective message; c) an output interface for releasing output data to cause the display of the computer to render the visualization structures, the output data defining a graphical environment in which the message visualization structures are rendered on the display simultaneously and convey information about the messages, their respective sizes, and their respective originator information in a non-textual-list manner, wherein at least one of the first non-textual visual feature and the second non-textual visual feature includes an animation of the message visualization structure associated with the corresponding message. 25. A non-transitory computer readable storage medium as defined in claim 15 , wherein at least one of the visualization structures includes a text field for displaying text based information.

1. A computer implemented method of analysing a set of data records, wherein each data record comprises a value for each of a plurality of variables, the method comprising: using one or more computer processors: receiving a selection of a variable of interest from the plurality of variables; analysing values in the data records of at least one variable of the plurality of variables other than the variable of interest by determining whether the at least one variable is correlated with the variable of interest and for each variable determined to be correlated with the variable of interest, determining the strength of the correlation; using the analysis to create a statistical description of said at least one variable; for one or more rules corresponding to the at least one variable, applying the rule to the plurality of variables; for each rule having conditions of the rule met by the plurality of variables, choosing a template corresponding to the rule; using the statistical description to populate the one or more templates corresponding to a rule, the templates populated for one or more variables other than the variable of interest, each template including a natural language description of a relationship between the variable of interest, or a value or range of values of the variable of interest, and the variable to which the statistical description applies; and displaying the one or more populated templates including natural language descriptions for one or more of the variables, together with information relating to the strength of the correlation.

1. A computer implemented method of analysing a set of data records, wherein each data record comprises a value for each of a plurality of variables, the method comprising: using one or more computer processors: receiving a selection of a variable of interest from the plurality of variables; analysing values in the data records of at least one variable of the plurality of variables other than the variable of interest by determining whether the at least one variable is correlated with the variable of interest and for each variable determined to be correlated with the variable of interest, determining the strength of the correlation; using the analysis to create a statistical description of said at least one variable; for one or more rules corresponding to the at least one variable, applying the rule to the plurality of variables; for each rule having conditions of the rule met by the plurality of variables, choosing a template corresponding to the rule; using the statistical description to populate the one or more templates corresponding to a rule, the templates populated for one or more variables other than the variable of interest, each template including a natural language description of a relationship between the variable of interest, or a value or range of values of the variable of interest, and the variable to which the statistical description applies; and displaying the one or more populated templates including natural language descriptions for one or more of the variables, together with information relating to the strength of the correlation. 6. The method of claim 1 comprising allocating the data records to respective groups, each group corresponding to a different value or range of values of the variable of interest, wherein the creation of a statistical description is performed for at least one of the respective groups.

1. A system in the form of a machine, article of manufacture, or composition of matter, comprising: an objective occurrence data solicitation module configured to solicit at least a portion of objective occurrence data including soliciting data indicating incidence of at least one objective occurrence, the solicitation being prompted based at least in part on a hypothesis linking one or more objective occurrences with one or more subjective user states and in response, at least in part, to an occurrence of at least one subjective user state associated with a user; and an objective occurrence data acquisition module configured to acquire the objective occurrence data, the objective occurrence data to be acquired including the data indicating incidence of at least one objective occurrence.

1. A system in the form of a machine, article of manufacture, or composition of matter, comprising: an objective occurrence data solicitation module configured to solicit at least a portion of objective occurrence data including soliciting data indicating incidence of at least one objective occurrence, the solicitation being prompted based at least in part on a hypothesis linking one or more objective occurrences with one or more subjective user states and in response, at least in part, to an occurrence of at least one subjective user state associated with a user; and an objective occurrence data acquisition module configured to acquire the objective occurrence data, the objective occurrence data to be acquired including the data indicating incidence of at least one objective occurrence. 29. The system of claim 1 , wherein said objective occurrence data solicitation module configured to solicit at least a portion of objective occurrence data including soliciting data indicating incidence of at least one objective occurrence, the solicitation being prompted based at least in part on a hypothesis linking one or more objective occurrences with one or more subjective user states and in response, at least in part, to an occurrence of at least one subjective user state associated with a user comprises: an objective occurrence data solicitation module configured to solicit the data indicating incidence of at least one objective occurrence in response, at least in part, to a subjective user state data reception module receiving data indicating incidence of the at least one subjective user state associated with the user.

9. A system for calculating a trust score, the system comprising: a first database storing first data associated with a first entity in a computer network; a second database storing second data associated with the first entity; and processing circuitry configured to: retrieve, from the first database, the first data; calculate a first component score based on the first data; retrieve, from the second database, the second data; calculate a second component score based on the second data; calculate a weighted combination of the first component score and the second component score to produce a trust score for the first entity; receive, from a user device of a second entity in the computer network, data indicating an attribute associated with the first entity; receive, from the user device of the second entity, an indication of an activity to be performed in the future by the first entity and the second entity, wherein the activity is associated with the attribute associated with the first entity; recalculate the first component score based on the first data and the received data indicating the attribute associated with the first entity, wherein recalculating the first component score comprises improving the first component score by a predetermined amount; and update the trust score for the first entity by calculating a weighted combination of the recalculated first component score and the second component score.

9. A system for calculating a trust score, the system comprising: a first database storing first data associated with a first entity in a computer network; a second database storing second data associated with the first entity; and processing circuitry configured to: retrieve, from the first database, the first data; calculate a first component score based on the first data; retrieve, from the second database, the second data; calculate a second component score based on the second data; calculate a weighted combination of the first component score and the second component score to produce a trust score for the first entity; receive, from a user device of a second entity in the computer network, data indicating an attribute associated with the first entity; receive, from the user device of the second entity, an indication of an activity to be performed in the future by the first entity and the second entity, wherein the activity is associated with the attribute associated with the first entity; recalculate the first component score based on the first data and the received data indicating the attribute associated with the first entity, wherein recalculating the first component score comprises improving the first component score by a predetermined amount; and update the trust score for the first entity by calculating a weighted combination of the recalculated first component score and the second component score. 10. The system of claim 9 , wherein the processing circuitry is configured to receive, from the user device of the second entity, the data indicating an attribute associated with the first entity by receiving an indication of a user input from the second entity that validates the first data.

1. A method for determining valid citation patterns in text within an electronic document using a processor, the method comprising: accessing, from a memory, at least one citation pattern, wherein each citation pattern includes a set of citation components which together define a predetermined pattern of citation components, each citation component associated with a set of citation component criteria; comparing text in the electronic document with the predetermined pattern of the citation components corresponding to the at least one citation pattern; and determining valid citation patterns in the text by identifying the predetermined pattern of citation components corresponding to the at least one citation pattern in the text.

1. A method for determining valid citation patterns in text within an electronic document using a processor, the method comprising: accessing, from a memory, at least one citation pattern, wherein each citation pattern includes a set of citation components which together define a predetermined pattern of citation components, each citation component associated with a set of citation component criteria; comparing text in the electronic document with the predetermined pattern of the citation components corresponding to the at least one citation pattern; and determining valid citation patterns in the text by identifying the predetermined pattern of citation components corresponding to the at least one citation pattern in the text. 13. The method according to claim 1 , further comprising: determining a type of citation for each of the valid citation patterns.

12. A system comprising: one or more computers; and a computer-readable medium coupled to the one or more computers having instructions stored thereon which, when executed by the one or more computers, cause the one or more computers to perform operations comprising: accessing audio data; accessing information that indicates a first context, the first context comprising a first physical environment or physical state of a device that records the audio data; accessing at least one term; accessing information that indicates a second context, the second context comprising a second physical environment or physical state associated with the accessed term; determining a similarity score that indicates a degree of similarity between the first physical environment or physical state and the second physical environment or physical state; adjusting a language model based on the accessed term and the determined similarity score to generate an adjusted language model, wherein the adjusted language model includes the accessed term and a weighting value assigned to the accessed term based on the similarity score; and performing speech recognition on the audio data using the adjusted language model to select one or more candidate transcriptions for a portion of the audio data.

12. A system comprising: one or more computers; and a computer-readable medium coupled to the one or more computers having instructions stored thereon which, when executed by the one or more computers, cause the one or more computers to perform operations comprising: accessing audio data; accessing information that indicates a first context, the first context comprising a first physical environment or physical state of a device that records the audio data; accessing at least one term; accessing information that indicates a second context, the second context comprising a second physical environment or physical state associated with the accessed term; determining a similarity score that indicates a degree of similarity between the first physical environment or physical state and the second physical environment or physical state; adjusting a language model based on the accessed term and the determined similarity score to generate an adjusted language model, wherein the adjusted language model includes the accessed term and a weighting value assigned to the accessed term based on the similarity score; and performing speech recognition on the audio data using the adjusted language model to select one or more candidate transcriptions for a portion of the audio data. 13. The system of claim 12 , wherein adjusting a language model comprises accessing a stored language model and adjusting the stored language model based on the similarity score.

2. A computer-implemented method comprising: receiving, at a translation server in communication with a network, a request for a translation of text in a source language to a target language, the request including an identifier associated with a pre-defined term translator specific to a user that identified the text for translation; translating, at the translation server, at least a portion of the text from the source language to the target language to obtain a translated version of the text in the target language, the translating including: identifying the pre-defined term translator based on the identifier; determining one or more terms from the text corresponding to the identified pre-defined term translator; applying the pre-defined term translator to the one or more terms; and translating a set of additional terms from the text from the source language to the target language via a translation model; and providing, via the translation server, the translated version of the text to a web server.

2. A computer-implemented method comprising: receiving, at a translation server in communication with a network, a request for a translation of text in a source language to a target language, the request including an identifier associated with a pre-defined term translator specific to a user that identified the text for translation; translating, at the translation server, at least a portion of the text from the source language to the target language to obtain a translated version of the text in the target language, the translating including: identifying the pre-defined term translator based on the identifier; determining one or more terms from the text corresponding to the identified pre-defined term translator; applying the pre-defined term translator to the one or more terms; and translating a set of additional terms from the text from the source language to the target language via a translation model; and providing, via the translation server, the translated version of the text to a web server. 6. The method of claim 2 , wherein the text includes text associated with a web page.

1. A method comprising: receiving, from a user device corresponding to a user of a social network, a search query; determining, by a processing device, communities associated with the social network that satisfy a content match rule directed to a match between content of the search query and information identifying the communities, the determined communities having scores that are based on results of the content match rule as applied to the communities, wherein each community comprises one or more posts received from one or more members of the community; determining, by the processing device, levels of reputations of the members of the determined communities that satisfy the content match rule; modifying, by the processing device, the scores for the determined communities based on the determined levels of the reputations of the members of the determined communities and based on a number of posts in the determined communities that comprise a determined term identified as relevant to the determined communities; ranking the determined communities based on the modified scores, wherein a higher score correlates to a higher ranking; and providing, in a user interface (UI) of the user device in response to the search query, identification of the determined communities for presentation in a ranked order in accordance with the ranking.

1. A method comprising: receiving, from a user device corresponding to a user of a social network, a search query; determining, by a processing device, communities associated with the social network that satisfy a content match rule directed to a match between content of the search query and information identifying the communities, the determined communities having scores that are based on results of the content match rule as applied to the communities, wherein each community comprises one or more posts received from one or more members of the community; determining, by the processing device, levels of reputations of the members of the determined communities that satisfy the content match rule; modifying, by the processing device, the scores for the determined communities based on the determined levels of the reputations of the members of the determined communities and based on a number of posts in the determined communities that comprise a determined term identified as relevant to the determined communities; ranking the determined communities based on the modified scores, wherein a higher score correlates to a higher ranking; and providing, in a user interface (UI) of the user device in response to the search query, identification of the determined communities for presentation in a ranked order in accordance with the ranking. 2. The method of claim 1 , wherein each community has one or more owners and one or more administrators.

22. A machine method according to claim 20, wherein the areas on the writing surface corresponding to each correlation vector are of a predetermined size and shape.

22. A machine method according to claim 20, wherein the areas on the writing surface corresponding to each correlation vector are of a predetermined size and shape. 24. A machine method according to claim 22, wherein said areas are equivalent to or larger than a predetermined minimum size.

1. A computer implemented method for managing information about a multi-step process in disparate knowledge repositories, the computer implemented method comprising: collecting practice requirements for the multi-step process, wherein the practice requirements comprise procedure information for performing tasks in the multi-step process, and wherein the procedure information specifies a particular execution order of the tasks in the multi-step process; and creating a process metadata data structure in a metadata repository comprising process information that conforms to the practice requirements, wherein the creating step further comprises: creating a template document for each task in the multi-step process; populating the template documents with the procedure information in the practice requirements; creating hierarchical and horizontal associations among the template documents based on the execution order of the tasks in the procedure information; creating a process document for each task in the multi-step process; populating the process documents with information about the tasks; and storing the task information, procedure information, and association information for each task as metadata in the process metadata structure.

1. A computer implemented method for managing information about a multi-step process in disparate knowledge repositories, the computer implemented method comprising: collecting practice requirements for the multi-step process, wherein the practice requirements comprise procedure information for performing tasks in the multi-step process, and wherein the procedure information specifies a particular execution order of the tasks in the multi-step process; and creating a process metadata data structure in a metadata repository comprising process information that conforms to the practice requirements, wherein the creating step further comprises: creating a template document for each task in the multi-step process; populating the template documents with the procedure information in the practice requirements; creating hierarchical and horizontal associations among the template documents based on the execution order of the tasks in the procedure information; creating a process document for each task in the multi-step process; populating the process documents with information about the tasks; and storing the task information, procedure information, and association information for each task as metadata in the process metadata structure. 2. The computer implemented method of claim 1 , wherein the tasks in the multi-step process are components comprising an information technology solution.

9. A system comprising: memory; at least one processor; an instruction storage module storing instructions that when executed by the at least one processor, cause the at least one processor to: capture a user inputted blog post by a blog-publishing application; based upon user input received via the blog-publishing application and from within the blog-publishing application, launch a copyright registration application via an Application Program Interface (API) of the blog-publishing application and an API of the copyright registration application, wherein the API of the copyright registration application supports presentation of an icon within a user interface of the blog-publishing application, and wherein selection of the icon supports launching the copyright registration application; and via the copyright registration application: receive the blog post from the blog-publishing application via the API of the blog-publishing application and the API of the copyright registration application; obtain copyright registration information for the blog post based upon at least one of user input and stored data; access a remote copyright registration data web server via the Internet; retrieve recorded user information for a user from the remote copyright registration data web server; upload the blog post to the remote copyright registration data web server; receive payment information via user input; interface with the remote copyright registration data web server to generate an electronic copyright application using the copyright registration information, the recorded user information, the payment information, and the blog post; interface with the remote copyright registration data web server to electronically submit the electronic copyright application to a copyright registration server; and receive a confirmation of submission of the copyright registration application; automatically publish the blog post by the blog-publishing application after the confirmation is received; and the copyright registration application presents an option to upload the blog post to the remote copyright registration data web server without applying for copyright registration.

9. A system comprising: memory; at least one processor; an instruction storage module storing instructions that when executed by the at least one processor, cause the at least one processor to: capture a user inputted blog post by a blog-publishing application; based upon user input received via the blog-publishing application and from within the blog-publishing application, launch a copyright registration application via an Application Program Interface (API) of the blog-publishing application and an API of the copyright registration application, wherein the API of the copyright registration application supports presentation of an icon within a user interface of the blog-publishing application, and wherein selection of the icon supports launching the copyright registration application; and via the copyright registration application: receive the blog post from the blog-publishing application via the API of the blog-publishing application and the API of the copyright registration application; obtain copyright registration information for the blog post based upon at least one of user input and stored data; access a remote copyright registration data web server via the Internet; retrieve recorded user information for a user from the remote copyright registration data web server; upload the blog post to the remote copyright registration data web server; receive payment information via user input; interface with the remote copyright registration data web server to generate an electronic copyright application using the copyright registration information, the recorded user information, the payment information, and the blog post; interface with the remote copyright registration data web server to electronically submit the electronic copyright application to a copyright registration server; and receive a confirmation of submission of the copyright registration application; automatically publish the blog post by the blog-publishing application after the confirmation is received; and the copyright registration application presents an option to upload the blog post to the remote copyright registration data web server without applying for copyright registration. 10. The system of claim 9 , wherein the blog-publishing application captures one or more of video recordings, photographs, audio recordings, text data, or graphics data.

2. The computer-implemented hypotheses generating device according to claim 1 , the processor further configured to determine, for each of the combinations of the collected noun pairs, based on the polarity of predicate template pair co-occurring with each noun pair and the type of conjunction coupling a phrase pair formed by the noun pair and the predicate templates, the polarity of relation between the nouns forming the combination of the noun pair.

2. The computer-implemented hypotheses generating device according to claim 1 , the processor further configured to determine, for each of the combinations of the collected noun pairs, based on the polarity of predicate template pair co-occurring with each noun pair and the type of conjunction coupling a phrase pair formed by the noun pair and the predicate templates, the polarity of relation between the nouns forming the combination of the noun pair. 3. The computer-implemented hypotheses generating device according to claim 2 , the processor further configured to: determine, for each of the noun pairs collected, based on the polarity of predicate template pair of the predicate templates co-occurring with the noun pair and the type of conjunction coupling a phrase pair formed by the noun pair and the predicate templates, the polarity of relation between the nouns forming each of the noun pairs; and collect, type by type of said noun pairs, polarities determined for each of said noun pairs, and determining polarity for each type of noun pairs, by the majority.

8. A method for a voice-controlled selection of a media file stored on a data storage unit including a plurality of media files, the media files comprising audio files and including media data and associated file identification data, where the file identification data associated with each audio file includes first phonetic information that corresponds to first phonetic rules for pronouncing an artist and a song title associated with the audio file and second phonetic information that corresponds to second phonetic rules for pronouncing the artist and the song title associated with the audio file, where the first phonetic information and the second phonetic information included in the file identification data are part of the audio file, the method comprising: inputting voice data for selecting one of the media files, the voice data including a static vocabulary and a variable vocabulary, the static vocabulary including a user command and the variable vocabulary including the artist and the song title associated with a desired media file; supplying the voice data to a speech recognition unit; providing a static vocabulary list including phonetic transcriptions of corresponding user commands; extracting the first phonetic information and the second phonetic information from the file identification data; supplying the phonetic transcriptions from the static vocabulary list and the first phonetic information and the second phonetic information extracted from the file identification data to the speech recognition unit as recognition vocabulary; generating a control command by comparing the input voice data to the phonetic transcriptions and the extracted phonetic information; using a media file player to select a media file from the data storage unit in accordance with the generated control command; and executing a user command on the media file in accordance with the generated control command.

8. A method for a voice-controlled selection of a media file stored on a data storage unit including a plurality of media files, the media files comprising audio files and including media data and associated file identification data, where the file identification data associated with each audio file includes first phonetic information that corresponds to first phonetic rules for pronouncing an artist and a song title associated with the audio file and second phonetic information that corresponds to second phonetic rules for pronouncing the artist and the song title associated with the audio file, where the first phonetic information and the second phonetic information included in the file identification data are part of the audio file, the method comprising: inputting voice data for selecting one of the media files, the voice data including a static vocabulary and a variable vocabulary, the static vocabulary including a user command and the variable vocabulary including the artist and the song title associated with a desired media file; supplying the voice data to a speech recognition unit; providing a static vocabulary list including phonetic transcriptions of corresponding user commands; extracting the first phonetic information and the second phonetic information from the file identification data; supplying the phonetic transcriptions from the static vocabulary list and the first phonetic information and the second phonetic information extracted from the file identification data to the speech recognition unit as recognition vocabulary; generating a control command by comparing the input voice data to the phonetic transcriptions and the extracted phonetic information; using a media file player to select a media file from the data storage unit in accordance with the generated control command; and executing a user command on the media file in accordance with the generated control command. 10. The method of claim 8 , where the step of generating a control command includes the step of comparing a phoneme sequence in the voice data to determine a candidate list of best matching media files and further determining the most likely entry in the candidate list by matching acoustic representations of the entries in the candidate list to the voice data.

1. A computer-implemented method, comprising: receiving an original query; generating a first feature vector for a first term in the original query; generating a respective feature vector for each of one or more different terms in a collection of terms; associating a respective similarity value with each of the one or more different terms, wherein the similarity value is based at least in part on a similarity measure between the first feature vector for the first term and a respective feature vector for each of the one or more different terms; identifying one or more similar terms from the one or more different terms based on the respective similarity values associated with each of the one or more different terms; generating an alternative query for each of the one or more identified similar terms by substituting the first term in the original query with a respective identified similar term; computing a score for each alternative query based on the similarity value associated with an identified similar term in the respective alternative query; and identifying one or more of the alternative queries as a query suggestion for the original query based at least in part on the computed score for each alternative query.

1. A computer-implemented method, comprising: receiving an original query; generating a first feature vector for a first term in the original query; generating a respective feature vector for each of one or more different terms in a collection of terms; associating a respective similarity value with each of the one or more different terms, wherein the similarity value is based at least in part on a similarity measure between the first feature vector for the first term and a respective feature vector for each of the one or more different terms; identifying one or more similar terms from the one or more different terms based on the respective similarity values associated with each of the one or more different terms; generating an alternative query for each of the one or more identified similar terms by substituting the first term in the original query with a respective identified similar term; computing a score for each alternative query based on the similarity value associated with an identified similar term in the respective alternative query; and identifying one or more of the alternative queries as a query suggestion for the original query based at least in part on the computed score for each alternative query. 2. The computer-implemented method of claim 1 , wherein the original query is a search query.

57. The method of claim 56 , further comprising inputting said mapping information in said medium.

57. The method of claim 56 , further comprising inputting said mapping information in said medium. 60. The method of claim 57 , wherein said inputting comprises selecting an attribute of data from a data source to be associated with a search attribute.

4. A method comprising: providing configuration information to a user application configurable according to the configuration information, the configuration information, after provision, configuring the user application to communicate a plurality of user submissions receiving the plurality of user submissions from the user application configured according to the provided configuration information; and publishing the plurality of user submissions received from the user application configured according to the provided configuration information, the publishing being performed by a processor of a machine.

4. A method comprising: providing configuration information to a user application configurable according to the configuration information, the configuration information, after provision, configuring the user application to communicate a plurality of user submissions receiving the plurality of user submissions from the user application configured according to the provided configuration information; and publishing the plurality of user submissions received from the user application configured according to the provided configuration information, the publishing being performed by a processor of a machine. 5. The method of claim 4 , wherein: the providing of the configuration information to the user application is in response to an indication of a targeted network location; and the publishing of the plurality of submissions includes publishing the plurality of submissions at the targeted network location.

7. A personal search agent system, comprising: a user agent configured to: transform a query from a user into a search task assignment, send the search task assignment to one or more server-based search manager agents, and receive one or more responses to the search task assignment; a user miner agent configured to: observe the user's interaction with the user agent or agents, and learn the behavior and preferences of the user; and a data mining agent configured to: analyze the responses to the user's queries, build a conceptual network of the knowledge contained in the response, and learn associations of natural language artifacts in unstructured data sources, wherein said artifacts include at least one of words, phrases, subjects, predicates, modifiers, and other syntactic forms, and wherein hierarchies of association are constructed across a state space of term usage compatible for interpolation of mapping functions between sets of terms.

7. A personal search agent system, comprising: a user agent configured to: transform a query from a user into a search task assignment, send the search task assignment to one or more server-based search manager agents, and receive one or more responses to the search task assignment; a user miner agent configured to: observe the user's interaction with the user agent or agents, and learn the behavior and preferences of the user; and a data mining agent configured to: analyze the responses to the user's queries, build a conceptual network of the knowledge contained in the response, and learn associations of natural language artifacts in unstructured data sources, wherein said artifacts include at least one of words, phrases, subjects, predicates, modifiers, and other syntactic forms, and wherein hierarchies of association are constructed across a state space of term usage compatible for interpolation of mapping functions between sets of terms. 8. The system of claim 7 , further wherein said mapping functions include one or more of fuzzy-type, weighted-type, or other types of mapping functions.

51. A method of computer analysis of at least one communication originated from a person, comprising: receiving with a computer the at least one communication with each communication being comprised of a group of words originated by the person; processing a text of the received group of words in each of the received at least one communication with a computer to determine risk posed by the person from at least one characteristic represented by the text of the group of words in each of the at least one communication; and in response to the determined risk posed by the person from the at least one characteristic, generating with a computer an output communication pertaining to the risk posed by the person from the at least one characteristic of the at least one communication.

51. A method of computer analysis of at least one communication originated from a person, comprising: receiving with a computer the at least one communication with each communication being comprised of a group of words originated by the person; processing a text of the received group of words in each of the received at least one communication with a computer to determine risk posed by the person from at least one characteristic represented by the text of the group of words in each of the at least one communication; and in response to the determined risk posed by the person from the at least one characteristic, generating with a computer an output communication pertaining to the risk posed by the person from the at least one characteristic of the at least one communication. 60. A method in accordance with claim 51 wherein: the person uses the at least one output communication to alter the at least one communication.

1. A method comprising: automatically extracting, using at least one processor and from first text of a free-form text narrative provided by a provider, a first medical fact, wherein the first medical fact is selected from the group consisting of a problem, a medication, an allergy, and a procedure for a patient, and wherein the first text appears in the free-form text narrative in context of second text of the free-form text narrative and the extracting comprises interpreting the first text in the context of the second text of the free-form text narrative and expressing a meaning of the first text in context as the first medical fact; and outputting at least the first medical fact for a user to validate that the first medical fact was correctly extracted from the free-form text narrative, the outputting comprising: outputting the first medical fact for presentation to the user via a user interface that includes the first medical fact and at least part of the free-form text narrative, the at least part of the free-form text narrative included in the user interface comprising the first text of the free-form text narrative and the second text of the free-form text narrative, wherein the user interface, by which the first medical fact is output, prompts the user to validate that the first medical fact was correctly extracted from the free-form text narrative, and in response to a selection by the user in the user interface of the first medical fact, demonstrates to the user in the user interface that the first medical fact was extracted from the first text in the automatically extracting by visually distinguishing in the user interface the first text of the free-form text narrative from the second text of the free-form text narrative; and in response to receipt of input indicating that the user has validated the first medical fact, storing the first medical fact.

1. A method comprising: automatically extracting, using at least one processor and from first text of a free-form text narrative provided by a provider, a first medical fact, wherein the first medical fact is selected from the group consisting of a problem, a medication, an allergy, and a procedure for a patient, and wherein the first text appears in the free-form text narrative in context of second text of the free-form text narrative and the extracting comprises interpreting the first text in the context of the second text of the free-form text narrative and expressing a meaning of the first text in context as the first medical fact; and outputting at least the first medical fact for a user to validate that the first medical fact was correctly extracted from the free-form text narrative, the outputting comprising: outputting the first medical fact for presentation to the user via a user interface that includes the first medical fact and at least part of the free-form text narrative, the at least part of the free-form text narrative included in the user interface comprising the first text of the free-form text narrative and the second text of the free-form text narrative, wherein the user interface, by which the first medical fact is output, prompts the user to validate that the first medical fact was correctly extracted from the free-form text narrative, and in response to a selection by the user in the user interface of the first medical fact, demonstrates to the user in the user interface that the first medical fact was extracted from the first text in the automatically extracting by visually distinguishing in the user interface the first text of the free-form text narrative from the second text of the free-form text narrative; and in response to receipt of input indicating that the user has validated the first medical fact, storing the first medical fact. 5. The method of claim 1 , wherein the user is the provider.

9. A computer system for identifying obsolete discussion threads in a forum comprising: a processor; a memory; and software instructions stored in the memory for causing the computer system to: extract a plurality of keywords from a discussion thread, wherein the discussion thread comprises a plurality of postings posted by a plurality of users; assign an initial keyword score to each of the plurality of keywords; identify a change event, wherein the change event is a change affecting a topic of the forum; extract a keyword from a recorded medium recording the change event, wherein the recorded medium is separate from the forum; compare the keyword from the recorded medium with the plurality of keywords from the discussion thread to identify a matching keyword in the plurality of keywords; decrease the initial keyword score of the matching keyword to a decreased score for the matching keyword based on the matching keyword matching the keyword from the recorded medium; aggregate the keyword score assigned to each of the plurality of keywords to obtain a total score for the discussion thread, wherein aggregating comprises using the reduced score for the matching keyword; and display a warning on a user interface comprising the discussion thread when the total score is below a first pre-specified threshold.

9. A computer system for identifying obsolete discussion threads in a forum comprising: a processor; a memory; and software instructions stored in the memory for causing the computer system to: extract a plurality of keywords from a discussion thread, wherein the discussion thread comprises a plurality of postings posted by a plurality of users; assign an initial keyword score to each of the plurality of keywords; identify a change event, wherein the change event is a change affecting a topic of the forum; extract a keyword from a recorded medium recording the change event, wherein the recorded medium is separate from the forum; compare the keyword from the recorded medium with the plurality of keywords from the discussion thread to identify a matching keyword in the plurality of keywords; decrease the initial keyword score of the matching keyword to a decreased score for the matching keyword based on the matching keyword matching the keyword from the recorded medium; aggregate the keyword score assigned to each of the plurality of keywords to obtain a total score for the discussion thread, wherein aggregating comprises using the reduced score for the matching keyword; and display a warning on a user interface comprising the discussion thread when the total score is below a first pre-specified threshold. 11. The computer system of claim 9 , wherein the initial keyword score of the matching keyword is decreased by subtracting a constant amount.

6. The defect predicate expression extraction method according to claim 1 , further comprising: storing said predicate expressions representing defects in a defect predicate expression storage unit; detecting, in said text data, expressions matching each of the said stored predicate expressions representing defects and extracting a noun expression occurring in a neighborhood of each of the said detected expressions matching each of the said stored predicate modifying expressions for detecting normality expressions in said text data; and storing, in an analysis object storage unit, as an object to be analyzed, a pair of said detected expressions matching said predicate expression representing said defect and said extracted noun expression, in association with a frequency of extraction of said object to be analyzed.

6. The defect predicate expression extraction method according to claim 1 , further comprising: storing said predicate expressions representing defects in a defect predicate expression storage unit; detecting, in said text data, expressions matching each of the said stored predicate expressions representing defects and extracting a noun expression occurring in a neighborhood of each of the said detected expressions matching each of the said stored predicate modifying expressions for detecting normality expressions in said text data; and storing, in an analysis object storage unit, as an object to be analyzed, a pair of said detected expressions matching said predicate expression representing said defect and said extracted noun expression, in association with a frequency of extraction of said object to be analyzed. 7. The defect predicate expression extraction method according to claim 6 , further comprising: calculating a correlation value of each of the said stored objects to be analyzed; and generating said correlation value of said object to be analyzed as an analysis result.

1. A method for automatically preparing customized communication documents for a plurality of consumer entities, the method comprising the steps of: using data from a first electronic data file containing financial product and/or financial service data for the customized communications, which financial product and/or financial service data includes a plurality of separate descriptions, characteristics and/or identifications for at least a first financial product and/or financial service; using data from a second electronic data file containing customer information for at least certain of the plurality of consumer entities, said customer information including customer related data in addition to, but not excluding, any one or more of customer name, customer address and customer account information obtained for said certain of the plurality of consumer entities; performing an automated composition process using a computing system configured to access said first data file and second data file to compose an electronic document file representing a customized communication document for at least one of said certain of the plurality of the consumer entities, said customized communication document comprising information relating to an offering for one or more financial products or services; wherein at least some content included in said customized communication document is customized content generated by said computing system for said electronic document file which includes variable data specifically for a consumer entity automatically derived and/or calculated from said first electronic data file and/or said second electronic data file during said automated composition process for said electronic document file and said consumer entity; delivering said customized communication documents based on said electronic document file to at least one of said certain of the plurality of consumer entities.

1. A method for automatically preparing customized communication documents for a plurality of consumer entities, the method comprising the steps of: using data from a first electronic data file containing financial product and/or financial service data for the customized communications, which financial product and/or financial service data includes a plurality of separate descriptions, characteristics and/or identifications for at least a first financial product and/or financial service; using data from a second electronic data file containing customer information for at least certain of the plurality of consumer entities, said customer information including customer related data in addition to, but not excluding, any one or more of customer name, customer address and customer account information obtained for said certain of the plurality of consumer entities; performing an automated composition process using a computing system configured to access said first data file and second data file to compose an electronic document file representing a customized communication document for at least one of said certain of the plurality of the consumer entities, said customized communication document comprising information relating to an offering for one or more financial products or services; wherein at least some content included in said customized communication document is customized content generated by said computing system for said electronic document file which includes variable data specifically for a consumer entity automatically derived and/or calculated from said first electronic data file and/or said second electronic data file during said automated composition process for said electronic document file and said consumer entity; delivering said customized communication documents based on said electronic document file to at least one of said certain of the plurality of consumer entities. 33. A method according to claim 1 , wherein customized content specific to each of the consumer entities includes data that is shared by two or more consumer entities.

1. A relational database management system comprising: a relational data store configured to store fact data; a multi-dimensional data store configured to store aggregated fact data in a multi-dimensional data structure and to communicate bi-directionally with the relational data store; a communication interface operably connected between the relational data store and the multi-dimensional data store for communication of fact data from the relational data store to the multi-dimensional data store and for the communication of aggregated fact data from the multi-dimensional data store to the relational data store; and a computer system comprising computer hardware, the computer system programmed to implement: a query servicing mechanism configured to service one or more natural language queries from a user, the query servicing mechanism comprising: a query processing mechanism configured to process a given natural language query using operations to: make a determination of whether servicing the given natural language query needs data stored in the relational data store or multi-dimensional data store; upon a determination that servicing the given natural language query needs data stored in the relational data store, automatically route the given natural language query to the relational data store, so that data is accessed from the relational data store and forwarded to the query processing mechanism for use in servicing the given natural language query, in a manner transparent to the user; and wherein upon a determination that servicing the given natural language query needs data stored in the multi-dimensional data store, automatically route the given natural language query to the multi-dimensional data store, so that aggregated fact data can be accessed and forwarded to the query processing mechanism for use in servicing the given natural language query, in a manner transparent to the user.

1. A relational database management system comprising: a relational data store configured to store fact data; a multi-dimensional data store configured to store aggregated fact data in a multi-dimensional data structure and to communicate bi-directionally with the relational data store; a communication interface operably connected between the relational data store and the multi-dimensional data store for communication of fact data from the relational data store to the multi-dimensional data store and for the communication of aggregated fact data from the multi-dimensional data store to the relational data store; and a computer system comprising computer hardware, the computer system programmed to implement: a query servicing mechanism configured to service one or more natural language queries from a user, the query servicing mechanism comprising: a query processing mechanism configured to process a given natural language query using operations to: make a determination of whether servicing the given natural language query needs data stored in the relational data store or multi-dimensional data store; upon a determination that servicing the given natural language query needs data stored in the relational data store, automatically route the given natural language query to the relational data store, so that data is accessed from the relational data store and forwarded to the query processing mechanism for use in servicing the given natural language query, in a manner transparent to the user; and wherein upon a determination that servicing the given natural language query needs data stored in the multi-dimensional data store, automatically route the given natural language query to the multi-dimensional data store, so that aggregated fact data can be accessed and forwarded to the query processing mechanism for use in servicing the given natural language query, in a manner transparent to the user. 4. The relational database management system of claim 1 , wherein the computer system is configured to generate the multi-dimensional data store by calculating aggregated fact data from the fact data according to a multi-dimensional data aggregation process, and storing the aggregated fact data in the multi-dimensional data store.

1. A method for a database system, comprising: establishing a column-oriented in-memory database structure including a main store and a dictionary compressed delta store, wherein the delta store comprises a value identifier vector that includes each value of a record stored in a same row of a column of the database and a delta dictionary associated with the column of the database; receiving a transaction associated with the column; recording the transaction within the delta store; adding an entry associated with the transaction to a value log of the value identifier vector, the value log comprising a transaction identifier and a row identifier indicating a row in the value identifier vector; and adding an entry associated with the transaction to a dictionary log of the delta dictionary.

1. A method for a database system, comprising: establishing a column-oriented in-memory database structure including a main store and a dictionary compressed delta store, wherein the delta store comprises a value identifier vector that includes each value of a record stored in a same row of a column of the database and a delta dictionary associated with the column of the database; receiving a transaction associated with the column; recording the transaction within the delta store; adding an entry associated with the transaction to a value log of the value identifier vector, the value log comprising a transaction identifier and a row identifier indicating a row in the value identifier vector; and adding an entry associated with the transaction to a dictionary log of the delta dictionary. 10. The method of claim 1 , wherein the delta dictionary comprises an unsorted array.

3. The program media of claim 2 wherein the resolving code further comprises: code for assigning a predetermined priority to each agent; code for comparing the predetermined priority to resolve a conflict between two or more match regions; and code for selecting the agent with the highest predetermined priority to control the overlapping region.

3. The program media of claim 2 wherein the resolving code further comprises: code for assigning a predetermined priority to each agent; code for comparing the predetermined priority to resolve a conflict between two or more match regions; and code for selecting the agent with the highest predetermined priority to control the overlapping region. 6. The program media of claim 3 further comprising code for controlling the agents detecting overlapping match regions to negotiate whether to relinquish control of each agent's overlap region.

13. The method of claim 11 , further comprising the step of: detecting the audio variation throughout the baseball game video to determine the segment length of the semantic event.

13. The method of claim 11 , further comprising the step of: detecting the audio variation throughout the baseball game video to determine the segment length of the semantic event. 14. The method of claim 13 , wherein the analyzing step comprises determining a first segment length of a first semantic event is longer than a second segment length of a second semantic event; wherein a first audio measurement that corresponding to the first semantic event is larger than a second audio measurement that corresponding to the second semantic event.

2. A computer-implemented method comprising: receiving, at a computing device including one or more processors, a phrase in a first language; obtaining, at the computing device, a corpus comprising a plurality of phrases in the first language and word reordering information for the plurality of phrases, the word reordering information indicating a correct word order for each phrase in a second language; identifying, at the computing device, word-to-word correspondences between each of the phrases in the first language and the corresponding correct word order for the phrase in the second language; generating, at the computing device, at least one tree that allows for the identified word-to-word correspondences; based upon the at least one tree, creating, at the computing device, a statistical model for reordering from a word order that is correct for the first language to a word order that is correct for the second language; and based upon the statistical model, generating, at the computing device, a reordered phrase from the received phrase, the reordered phrase having a correct word order for the second language.

2. A computer-implemented method comprising: receiving, at a computing device including one or more processors, a phrase in a first language; obtaining, at the computing device, a corpus comprising a plurality of phrases in the first language and word reordering information for the plurality of phrases, the word reordering information indicating a correct word order for each phrase in a second language; identifying, at the computing device, word-to-word correspondences between each of the phrases in the first language and the corresponding correct word order for the phrase in the second language; generating, at the computing device, at least one tree that allows for the identified word-to-word correspondences; based upon the at least one tree, creating, at the computing device, a statistical model for reordering from a word order that is correct for the first language to a word order that is correct for the second language; and based upon the statistical model, generating, at the computing device, a reordered phrase from the received phrase, the reordered phrase having a correct word order for the second language. 4. The method of claim 2 , further comprising the step of generating a translation of the received phrase in the second language based upon the reordered phrase.

1. A method in a communication network for personalizing a service, comprising the steps of: generating user dependant language models by a speech recognition system, storing said user dependant language models, making said user dependant language models, and/or a user profile derived from said user dependant language models, available to a software application running in a user's device and/or available to external service providers, for personalizing an aspect of a service unrelated to speech processing, using a microphone of a user end device of said user for gathering ambient speech material outside normal use of said user end device for voice or data communications with external devices, and using said ambient speech material for adapting said user dependant language models.

1. A method in a communication network for personalizing a service, comprising the steps of: generating user dependant language models by a speech recognition system, storing said user dependant language models, making said user dependant language models, and/or a user profile derived from said user dependant language models, available to a software application running in a user's device and/or available to external service providers, for personalizing an aspect of a service unrelated to speech processing, using a microphone of a user end device of said user for gathering ambient speech material outside normal use of said user end device for voice or data communications with external devices, and using said ambient speech material for adapting said user dependant language models. 32. The method according to claim 1 , wherein said step of personalizing an aspect of a service comprises filtering out spam messages.

2. The method of claim 1 , wherein solving the GTSP further comprises: transforming the GTSP to an asymmetric traveling salesman problem (ATSP); transforming the ATSP to a standard traveling salesman problem (TSP); and solving the TSP to translate the blocks of the input sentence.

2. The method of claim 1 , wherein solving the GTSP further comprises: transforming the GTSP to an asymmetric traveling salesman problem (ATSP); transforming the ATSP to a standard traveling salesman problem (TSP); and solving the TSP to translate the blocks of the input sentence. 3. The method of claim 2 , further comprising solving the TSP using at least one of a Concorde solver and a Lin-Kernighan-heuristic.

10. The method of claim 1 , wherein the notification comprises a context associated with the candidate term found in the archive.

10. The method of claim 1 , wherein the notification comprises a context associated with the candidate term found in the archive. 11. The method of claim 10 , wherein the context comprises one or more words adjacent to the candidate term in the archive.

18. A system for monitoring participants' level of attentiveness within an interactive online event, the system comprising: a plurality of user devices accessing an interactive online event, wherein each user device corresponds to a participant of the interactive online event; a host device accessing the interactive online event, wherein the host device corresponds to a host of the interactive online event; a server, the server operable to: receive, from each of the plurality of user devices, at least one video; capture, from each received video, at least one facial image; analyze each captured facial image, wherein analyze comprises: compare each captured facial image to a plurality of predefined facial expressions; and match each captured facial image to at least one predefined facial expression, wherein each match assigns a value to the captured facial image; determine a level of attentiveness for the interactive online event by processing each of the assigned values together; and transmit the determined level of attentiveness to the host device.

18. A system for monitoring participants' level of attentiveness within an interactive online event, the system comprising: a plurality of user devices accessing an interactive online event, wherein each user device corresponds to a participant of the interactive online event; a host device accessing the interactive online event, wherein the host device corresponds to a host of the interactive online event; a server, the server operable to: receive, from each of the plurality of user devices, at least one video; capture, from each received video, at least one facial image; analyze each captured facial image, wherein analyze comprises: compare each captured facial image to a plurality of predefined facial expressions; and match each captured facial image to at least one predefined facial expression, wherein each match assigns a value to the captured facial image; determine a level of attentiveness for the interactive online event by processing each of the assigned values together; and transmit the determined level of attentiveness to the host device. 19. The system of claim 18 , the server further operable to: change the color of a user interface of the host device in response to the level of attentiveness.

19. A computer-implemented method executable by at least one processor executing machine readable instructions stored on a non-transitory computer readable medium, the method comprising: generating a procurement request, the procurement request to solicit bids for providing at least one item; determining an item risk score for the procurement request based on a classification performed by at least one machine learning classifier; displaying the item risk score in a graphical user interface; determining whether the at least one item is a high-risk item based on the item risk score; in response to determining the at least one item is high risk, generating a link in the graphical user interface, the link to provide access to at least one metric explaining the high-risk of the at least one item; generating a solicitation from the procurement request; receiving bids to provide the at least one item in response to the solicitation; evaluating the bids based on classifications performed by the at least one machine learning classifier, where to evaluate the bids: classifying, by the at least one machine learning classifier, the bids as being associated with at least one of a high-risk supplier and a high-risk price; and determining a price risk score and a supplier risk score for each of the bids based on the classifications; comparing, for each bid, the price risk score and the supplier risk score to a respective threshold; determining if any of the received bids are associated with a high-risk procurement based on the comparing of the price risk score and the supplier risk score to the respective threshold; in response to determining a bid is associated with a high-risk procurement, generating a bid evaluation link in the graphical user interface, the bid evaluation link providing access to information explaining the high-risk procurement; and selecting one of the bids as a winning bid based on the classifications of the bids determined by the at least one machine learning classifier.

19. A computer-implemented method executable by at least one processor executing machine readable instructions stored on a non-transitory computer readable medium, the method comprising: generating a procurement request, the procurement request to solicit bids for providing at least one item; determining an item risk score for the procurement request based on a classification performed by at least one machine learning classifier; displaying the item risk score in a graphical user interface; determining whether the at least one item is a high-risk item based on the item risk score; in response to determining the at least one item is high risk, generating a link in the graphical user interface, the link to provide access to at least one metric explaining the high-risk of the at least one item; generating a solicitation from the procurement request; receiving bids to provide the at least one item in response to the solicitation; evaluating the bids based on classifications performed by the at least one machine learning classifier, where to evaluate the bids: classifying, by the at least one machine learning classifier, the bids as being associated with at least one of a high-risk supplier and a high-risk price; and determining a price risk score and a supplier risk score for each of the bids based on the classifications; comparing, for each bid, the price risk score and the supplier risk score to a respective threshold; determining if any of the received bids are associated with a high-risk procurement based on the comparing of the price risk score and the supplier risk score to the respective threshold; in response to determining a bid is associated with a high-risk procurement, generating a bid evaluation link in the graphical user interface, the bid evaluation link providing access to information explaining the high-risk procurement; and selecting one of the bids as a winning bid based on the classifications of the bids determined by the at least one machine learning classifier. 20. The method of claim 19 , comprising: prior to generating the procurement request, determining the item risk score and a pre-procurement request, supplier risk score; and displaying the item risk score and the pre-procurement request, supplier risk score in the graphical user interface.

15. A non-transitory computer storage medium storing thereon instructions that, when executed by one or more processors, cause a system to perform operations of geocoding resources based on contained text, the operations comprising: retrieving, by the one or more processors, an electronic resource including text; identifying, by the one or more processors within the electronic resource, a geotoken referring to a geographic location; determining, by the one or more processors, a location where the geotoken is contained within the electronic resource; scoring, by the one or more processors, relevance of the electronic resource to the geographic location as a function of the determined location of the geotoken within the electronic resource, to generate a relevance score, including: determining that the geotoken is one of N different geotokens, each referencing a different respective geographic location, located in a title part of the electronic resource, and assigning a weight to the geotoken inversely proportional to N; and designating, by the one or more processors, the electronic resource as relevant to the geographic location in memory, in accordance with the generated relevance score.

15. A non-transitory computer storage medium storing thereon instructions that, when executed by one or more processors, cause a system to perform operations of geocoding resources based on contained text, the operations comprising: retrieving, by the one or more processors, an electronic resource including text; identifying, by the one or more processors within the electronic resource, a geotoken referring to a geographic location; determining, by the one or more processors, a location where the geotoken is contained within the electronic resource; scoring, by the one or more processors, relevance of the electronic resource to the geographic location as a function of the determined location of the geotoken within the electronic resource, to generate a relevance score, including: determining that the geotoken is one of N different geotokens, each referencing a different respective geographic location, located in a title part of the electronic resource, and assigning a weight to the geotoken inversely proportional to N; and designating, by the one or more processors, the electronic resource as relevant to the geographic location in memory, in accordance with the generated relevance score. 17. The computer storage medium of claim 15 , wherein the scoring the relevance of the electronic resource to the geographic location includes: assigning a first weight to the geotoken when the geotoken is within a threshold number of words from a beginning of a text included in the electronic resource, and assigning a second weight to the geotoken when the geotoken is not within the threshold number of words from the beginning of the text, wherein the first weight is larger than the second weight.

14. A title extracting apparatus for recognizing and extracting a required partial region from a document image of a document that has been converted into image data, comprising: character region generating means for generating character regions, including black pixel connected regions composed of connected black pixels of the document image; character string region generating means for unifying one or more character regions generated by said character region generating means, and for generating character string regions including one or more character regions; title extracting means for extracting a particular character string region of the character string regions, according to attributes of a plurality of character string regions generated by said character string region generating means, as a title region; and segment extracting means for horizontally dividing an inside of the character string region into a plurality of partial regions, extracting a partial segment region with a large black pixel occupying ratio from each of the partial regions, unifying horizontally connected partial segment regions that have heights exceeding a predetermined threshold regardless of the heights, and extracting the unified horizontally connected partial segment regions, wherein the title region is extracted using the unified horizontally connected partial segment region.

14. A title extracting apparatus for recognizing and extracting a required partial region from a document image of a document that has been converted into image data, comprising: character region generating means for generating character regions, including black pixel connected regions composed of connected black pixels of the document image; character string region generating means for unifying one or more character regions generated by said character region generating means, and for generating character string regions including one or more character regions; title extracting means for extracting a particular character string region of the character string regions, according to attributes of a plurality of character string regions generated by said character string region generating means, as a title region; and segment extracting means for horizontally dividing an inside of the character string region into a plurality of partial regions, extracting a partial segment region with a large black pixel occupying ratio from each of the partial regions, unifying horizontally connected partial segment regions that have heights exceeding a predetermined threshold regardless of the heights, and extracting the unified horizontally connected partial segment regions, wherein the title region is extracted using the unified horizontally connected partial segment region. 18. The title extracting apparatus as set forth in claim 14, wherein said segment extracting means is adapted for extracting two segment regions, having left edge coordinates and right edge coordinates, from the character string region, generating a third histogram of black pixels in a vertical direction in the vicinity of the left edge coordinates, generating a fourth histogram of black pixels in the vertical direction in the vicinity of the right edge coordinates, and determining that a frame line is disposed in the character string region when heights of the third and fourth histograms are approximately equal to a distance between the two segment regions.

11. A computer system comprising a processor and a computer readable memory unit coupled to the processor, said memory unit containing instructions that when executed by the processor implement a method for searching and retrieving at least one reusable asset, a first asset of said at least one reusable asset being stored in an index file in a database, a search request comprising at least one search term, the method comprising: receiving a new request comprising a first search term of said at least one search term; searching the index file for the first search term in the new request, said searching comprising selecting, from the index file, all reusable assets having the first search term; building a new search result with all reusable assets having been selected from the index file during said searching; retrieving a past request from a search history stored in the database, the past request comprising a second search term of said at least one search term, the past request being coupled to a past search result comprising a second asset of said at least one reusable asset; correlating the new request with the past request, said correlating comprising: calculating a correlation coefficient R between the new request and the past request; determining the correlation coefficient R being greater than or equal to a first predefined threshold value R t for request correlation in a range of 0.2 to 0.99; adding the past search result to the new search result, wherein the past search result is not present within the new search result; and calculating a position value P of the past search result within the new search result as P=Round(wRS), wherein Round(x) is a mathematical function returning a closest integer to x, w is a predefined weight value chosen from a range of 0.1 to 1, and S is a number of reusable assets in the new search result; adjusting a relevance of each reusable asset within the new search result, the relevance indicating how the past search result for said each reusable asset is correlated with the new search result pursuant to a number of occurrences of said at least one search term in said each reusable asset; and storing the new request and the new search result into the search request history in the database upon determining that the relevance of said each reusable asset in the new search result is greater than a second predefined threshold value for the relevance, wherein said receiving, said searching, said building, said retrieving, said correlating, said adjusting, and said storing are performed by a search server, and wherein the search server is configured to store into and retrieve from the database.

11. A computer system comprising a processor and a computer readable memory unit coupled to the processor, said memory unit containing instructions that when executed by the processor implement a method for searching and retrieving at least one reusable asset, a first asset of said at least one reusable asset being stored in an index file in a database, a search request comprising at least one search term, the method comprising: receiving a new request comprising a first search term of said at least one search term; searching the index file for the first search term in the new request, said searching comprising selecting, from the index file, all reusable assets having the first search term; building a new search result with all reusable assets having been selected from the index file during said searching; retrieving a past request from a search history stored in the database, the past request comprising a second search term of said at least one search term, the past request being coupled to a past search result comprising a second asset of said at least one reusable asset; correlating the new request with the past request, said correlating comprising: calculating a correlation coefficient R between the new request and the past request; determining the correlation coefficient R being greater than or equal to a first predefined threshold value R t for request correlation in a range of 0.2 to 0.99; adding the past search result to the new search result, wherein the past search result is not present within the new search result; and calculating a position value P of the past search result within the new search result as P=Round(wRS), wherein Round(x) is a mathematical function returning a closest integer to x, w is a predefined weight value chosen from a range of 0.1 to 1, and S is a number of reusable assets in the new search result; adjusting a relevance of each reusable asset within the new search result, the relevance indicating how the past search result for said each reusable asset is correlated with the new search result pursuant to a number of occurrences of said at least one search term in said each reusable asset; and storing the new request and the new search result into the search request history in the database upon determining that the relevance of said each reusable asset in the new search result is greater than a second predefined threshold value for the relevance, wherein said receiving, said searching, said building, said retrieving, said correlating, said adjusting, and said storing are performed by a search server, and wherein the search server is configured to store into and retrieve from the database. 15. The computer system of claim 11 , said adjusting comprising: mapping each reusable asset in the new search result to a predefined quality metric to determine reusability for each reusable asset within the new search result, wherein the predefined quality metric is selected from the group consisting of a certification, an error test result, a user feedback, and combinations thereof.

4. The method of claim 1 , wherein the ranking of the first search result is changed based on a combination of a factual accuracy and search relevance of the first search result relative to the second search result.

4. The method of claim 1 , wherein the ranking of the first search result is changed based on a combination of a factual accuracy and search relevance of the first search result relative to the second search result. 5. The method of claim 4 , wherein the combination of the factual accuracy and the search relevance of the first search result is based on a particular weight assigned to each of the factual accuracy and the search relevance.

19. The method of generating the record sentence of claim 18 , wherein the generating of the record sentence by combining the selected unseen unit with the speech synthesis information comprises: generating a first candidate record sentence by combining the selected unseen unit with the speech synthesis information; and generating a second candidate record sentence by performing at least one of word replacement, word addition, content word replacement, content word addition, and/or sentence structure modification.

19. The method of generating the record sentence of claim 18 , wherein the generating of the record sentence by combining the selected unseen unit with the speech synthesis information comprises: generating a first candidate record sentence by combining the selected unseen unit with the speech synthesis information; and generating a second candidate record sentence by performing at least one of word replacement, word addition, content word replacement, content word addition, and/or sentence structure modification. 20. The method of generating the record sentence of claim 19 , wherein the generating of the second candidate record sentence is performed according to at least one of morpheme analysis, syntax analysis, dependent structure analysis, case structure analysis, and/or semantic analysis.

1. A method for representing similar text documents and similar segments of text documents as standardized text templates and deviations from standardized text templates, the method comprising: partitioning documents into segments; generating standardized text segment templates from segments of documents; determining the deviations of individual text segments from the standardized text segment templates so generated; representing the individual text segments as the combination of standardized text segment templates and deviations from standardized text segment templates; generating standardized text templates representing text documents as sequences of standardized text segment templates; determining the deviations of the individual text documents from the standardized text templates so generated; and representing the individual text documents as a combination of the standardized text templates and the deviations from the standardized text templates.

1. A method for representing similar text documents and similar segments of text documents as standardized text templates and deviations from standardized text templates, the method comprising: partitioning documents into segments; generating standardized text segment templates from segments of documents; determining the deviations of individual text segments from the standardized text segment templates so generated; representing the individual text segments as the combination of standardized text segment templates and deviations from standardized text segment templates; generating standardized text templates representing text documents as sequences of standardized text segment templates; determining the deviations of the individual text documents from the standardized text templates so generated; and representing the individual text documents as a combination of the standardized text templates and the deviations from the standardized text templates. 2. The method of claim 1 , where the documents are partitioned into segments corresponding to paragraphs.

12. The system according to claim 9 , further comprising: an individual authentication unit configured to authenticate a user requesting generation of the document; and a charge processing unit to accept a payment from the user requesting generation of the document, wherein the print unit prints the generated document based on a determination that the user is authenticated by the individual authentication unit and the charge processing unit has accepted the payment.

12. The system according to claim 9 , further comprising: an individual authentication unit configured to authenticate a user requesting generation of the document; and a charge processing unit to accept a payment from the user requesting generation of the document, wherein the print unit prints the generated document based on a determination that the user is authenticated by the individual authentication unit and the charge processing unit has accepted the payment. 13. The system according to claim 12 , wherein the individual authentication unit authenticates the user based on personal information input by the user and by comparing a name of the user with a name included in the generated document.

16. The method of claim 5, further comprising: creating histograms of the extracted symbol; determining a total mass of the extracted symbol; summing a number of pixels from selected histogram groups; summing a number of runs used to determine the number of pixels; and determining the stroke width of the extracted symbol based on the number of pixels and the number of runs.

16. The method of claim 5, further comprising: creating histograms of the extracted symbol; determining a total mass of the extracted symbol; summing a number of pixels from selected histogram groups; summing a number of runs used to determine the number of pixels; and determining the stroke width of the extracted symbol based on the number of pixels and the number of runs. 19. The method of claim 16, wherein the stroke width of the extracted symbol equals the number of pixels divided by the number of runs.

19. A system comprising: a processing unit coupled to a memory through a bus; and a process executed from the memory by the processing unit to cause the processing unit to: prune redundancy of instances in a plurality of speech segments, wherein the redundancy criterion is based on a similarity measure between feature vectors derived from a machine perception transformation of time-domain samples corresponding to the instances in the plurality of speech segments, wherein the instances subjected to redundancy pruning are clustered together with feature vectors discernably separated from each other in the machine perception transformation and wherein the machine perception transformation is correlated with human perception by using the time-domain samples retaining both amplitude and phase information of the speech segments, which were provided in sound data for a speech synthesis system.

19. A system comprising: a processing unit coupled to a memory through a bus; and a process executed from the memory by the processing unit to cause the processing unit to: prune redundancy of instances in a plurality of speech segments, wherein the redundancy criterion is based on a similarity measure between feature vectors derived from a machine perception transformation of time-domain samples corresponding to the instances in the plurality of speech segments, wherein the instances subjected to redundancy pruning are clustered together with feature vectors discernably separated from each other in the machine perception transformation and wherein the machine perception transformation is correlated with human perception by using the time-domain samples retaining both amplitude and phase information of the speech segments, which were provided in sound data for a speech synthesis system. 20. The system of claim 19 wherein the instances are the instances of a phoneme, a diphone, a syllable, a word, or a sequence unit and wherein a first set of the instances subjected to redundancy pruning are clustered with a first feature vector and a second set of the instances subjected to redundancy pruning are clustered with a second feature vector that is discernably separated from the first feature vector.

12. The categorization method as set forth in claim 10 , further comprising: thresholding the rescaled input object class probabilities to associate the input object with at least one class of the set of classes.

12. The categorization method as set forth in claim 10 , further comprising: thresholding the rescaled input object class probabilities to associate the input object with at least one class of the set of classes. 13. The categorization method as set forth in claim 12 , further comprising: outputting an identification of the at least one class associated with the input object.

12. The method of claim 1 , applying the first set of rules resulting in assigning respective scores to respective segments, the computer determining whether the on-line bank account is a business account or utilized for business purposes based at least in part upon the respective scores for respective segments.

12. The method of claim 1 , applying the first set of rules resulting in assigning respective scores to respective segments, the computer determining whether the on-line bank account is a business account or utilized for business purposes based at least in part upon the respective scores for respective segments. 13. The method of claim 12 , the first set of rules comprising multiple rules that are applied to at least one segment such that multiple scores are assigned to the at least one segment.

16. A method for transforming media elements into a narrative comprising: receiving, with a clustering module in communication with a processor and a memory, a dataset comprising a plurality of media elements each comprising metadata; organizing, with the clustering module, the plurality of media elements into a plurality of clusters based on the metadata, the plurality of clusters being organized into a clustering tree; and creating, with a narrative module in communication with the processor and the memory, a narrative comprising a plurality of the media elements arranged into a narrative sequence, the narrative sequence being structured according to the clustering tree and for a predetermined duration, the narrative sequence comprising a story introduction for introducing the narrative, a geographic introduction for contextualizing a cluster, a time introduction for identifying a cluster time period, a show of media elements for representing the media elements, and a story end, wherein the narrative module is configured to create the narrative sequence according to an algorithm comprising specific durations for at least the following entries: story introduction (SI), geographic introduction (GI), time introduction (TI), show of photos, with a minimum duration for photo representation (P min ), and story end, given (SE guess ), where T is a total duration of the narrative, N is a total number of media elements in the dataset, and the algorithm produces the following result: T>SI+GI+TI+P min +SE guess .

16. A method for transforming media elements into a narrative comprising: receiving, with a clustering module in communication with a processor and a memory, a dataset comprising a plurality of media elements each comprising metadata; organizing, with the clustering module, the plurality of media elements into a plurality of clusters based on the metadata, the plurality of clusters being organized into a clustering tree; and creating, with a narrative module in communication with the processor and the memory, a narrative comprising a plurality of the media elements arranged into a narrative sequence, the narrative sequence being structured according to the clustering tree and for a predetermined duration, the narrative sequence comprising a story introduction for introducing the narrative, a geographic introduction for contextualizing a cluster, a time introduction for identifying a cluster time period, a show of media elements for representing the media elements, and a story end, wherein the narrative module is configured to create the narrative sequence according to an algorithm comprising specific durations for at least the following entries: story introduction (SI), geographic introduction (GI), time introduction (TI), show of photos, with a minimum duration for photo representation (P min ), and story end, given (SE guess ), where T is a total duration of the narrative, N is a total number of media elements in the dataset, and the algorithm produces the following result: T>SI+GI+TI+P min +SE guess . 17. The method of claim 16 , wherein: the metadata for each media element comprises information about a time at which the media element was created, information about a location at which the media element was created, or a combination thereof; and organizing the plurality of media elements into the plurality of clusters is based on: time, wherein the produced clusters are isolated in time but not in space; space, wherein the produced clusters are isolated in space but not in time; space/time, wherein the produced clusters comprise a sequence of clusters organized so that consecutive clusters are isolated in space and time; or a combination thereof.

19. An apparatus, comprising: a processor to display a user interface on a mobile computing device, wherein the user interface includes a data entry menu having one or more menu selections, wherein the processor to activate one of the one or more menu selections to display a data entry method, wherein the data entry method is context specific to the activated menu selection by displaying only one or more virtual keys that are necessary for a user to enter data required by the activated menu selection, wherein the data entry method is language specific to the activated menu selection by automatically changing a phone language, wherein only a virtual keyboard enabled to receive alphabetic input is displayed in the data entry method on the display device when alphabetic user input is required by the activated user selection, and only a virtual keypad enabled to receive numeric input is displayed in the data entry method on the display device when numeric user input is required by the activated user selection.

19. An apparatus, comprising: a processor to display a user interface on a mobile computing device, wherein the user interface includes a data entry menu having one or more menu selections, wherein the processor to activate one of the one or more menu selections to display a data entry method, wherein the data entry method is context specific to the activated menu selection by displaying only one or more virtual keys that are necessary for a user to enter data required by the activated menu selection, wherein the data entry method is language specific to the activated menu selection by automatically changing a phone language, wherein only a virtual keyboard enabled to receive alphabetic input is displayed in the data entry method on the display device when alphabetic user input is required by the activated user selection, and only a virtual keypad enabled to receive numeric input is displayed in the data entry method on the display device when numeric user input is required by the activated user selection. 21. The apparatus of claim 19 , wherein the one or more menu selections comprise one or more data entry boxes.

28. The system of claim 16 and further comprising: a class library comprising an Entity base class representative of a primitive Entity type for modeling semantics of adjective phrases.

28. The system of claim 16 and further comprising: a class library comprising an Entity base class representative of a primitive Entity type for modeling semantics of adjective phrases. 29. The system of claim 28 wherein pairs of resolvable types are related according to semantic rules, and wherein one resolvable type of the pair of resolvable types may fail resolution semantics without affecting the existence of the other resolvable type.

13. A method for controlling a radio device, comprising: receiving a gesture input through a gesture pad that distinguishes between a plurality of fingers used for a gesture, that recognizes the orientation of the distinguished finger, and that performs a function that is dependent on the distinguished finger and its orientation; and modifying an operation of the radio device based on the received gesture input.

13. A method for controlling a radio device, comprising: receiving a gesture input through a gesture pad that distinguishes between a plurality of fingers used for a gesture, that recognizes the orientation of the distinguished finger, and that performs a function that is dependent on the distinguished finger and its orientation; and modifying an operation of the radio device based on the received gesture input. 15. The method of claim 13 , further comprising: receiving a voice command; filtering the voice command; and modifying another operation of the radio device based on the voice command.

1. A method of creating text using a computer having a memory, at least one display screen and means for selecting positions on said screen, said method comprising the steps of: (a) storing in said memory a dictionary of frequently used linguistic expressions, at least some of said linguistic expressions having a plurality of alphanumeric characters; (b) displaying on a first section of said screen a plurality of said linguistic expressions arranged in a predetermined order for selection by a user; (c) displaying on a second section of said screen at least one line of text, as said text is created by a user; (d) identifying the position of a linguistic expression on said first section of said screen, in response to selection of that position by a user with said position selecting means; and (e) displaying the linguistic expression, whose position was identified in step (d), in said second section of said screen, concatenated to the end of said line of text, thereby adding a linguistic expression to said line of text.

1. A method of creating text using a computer having a memory, at least one display screen and means for selecting positions on said screen, said method comprising the steps of: (a) storing in said memory a dictionary of frequently used linguistic expressions, at least some of said linguistic expressions having a plurality of alphanumeric characters; (b) displaying on a first section of said screen a plurality of said linguistic expressions arranged in a predetermined order for selection by a user; (c) displaying on a second section of said screen at least one line of text, as said text is created by a user; (d) identifying the position of a linguistic expression on said first section of said screen, in response to selection of that position by a user with said position selecting means; and (e) displaying the linguistic expression, whose position was identified in step (d), in said second section of said screen, concatenated to the end of said line of text, thereby adding a linguistic expression to said line of text. 2. The method defined in claim 1, wherein some of said linguistic expressions each comprise a single alphanumeric character.

24. The method defined in claim 22 , wherein identifying one of the snippets as the best matching snippet for the reference video stream comprises identifying the snippet for which the associated segment is the longest.

24. The method defined in claim 22 , wherein identifying one of the snippets as the best matching snippet for the reference video stream comprises identifying the snippet for which the associated segment is the longest. 28. The method defined in claim 24 , further comprising carrying out the providing of a set of reference data elements, the associating and the determining for each of a plurality of reference video streams.

3. The computer-implemented method of claim 1 , further comprising, during the training period: determining that a third word in the music collection data is not represented in the first table; performing grapheme-to-phoneme processing to determine a third FST representing an estimated pronunciation of the third word; and storing an association between the third FST and the second FST, wherein creating the second table further comprises creating a third entry including a reference to the third FST.

3. The computer-implemented method of claim 1 , further comprising, during the training period: determining that a third word in the music collection data is not represented in the first table; performing grapheme-to-phoneme processing to determine a third FST representing an estimated pronunciation of the third word; and storing an association between the third FST and the second FST, wherein creating the second table further comprises creating a third entry including a reference to the third FST. 4. The computer-implemented method of claim 3 , further comprising, during a runtime period: receiving audio data associated with the first user profile; identifying a third table associated with an updated ASR language model FST to be used during the runtime period; identifying a fifth entry in the third table corresponding to the third word; generating a modified second table including an updated third entry including a fifth index value to the fifth entry; generating a modified second FST by substituting the first index value for the third index value, the second index value for the fourth index value, and the fifth index value for the reference; and performing ASR using the updated ASR language model FST and the modified second FST.

9. A computer program product, comprising: a non-transitory computer-readable medium comprising code to perform the steps of: receiving a plurality of field size rules for automatically calculating a field size for a code structure; receiving a plurality of element display rules for categorizing one or more elements in a code structure; receiving a selection of one or more program variables; analyzing the selection for completeness; completing an incomplete selection; and displaying results based on the field size rules and the element display rules.

9. A computer program product, comprising: a non-transitory computer-readable medium comprising code to perform the steps of: receiving a plurality of field size rules for automatically calculating a field size for a code structure; receiving a plurality of element display rules for categorizing one or more elements in a code structure; receiving a selection of one or more program variables; analyzing the selection for completeness; completing an incomplete selection; and displaying results based on the field size rules and the element display rules. 10. The computer program product of claim 9 , the steps further comprising: creating lookups for the one or more program variables; and calculating field size offsets for the one or more program variables.

10. A method for summarizing daily life information, the method comprising: collecting log information comprising the daily life information from at least one electronic device; analyzing the log information collected and deciding at least one topic representing the daily life information; generating at least one sentence representing the daily life information based on at least one of a reference topic representative of the daily life information decided, weather, and an emotion; and displaying the generated at least one sentence.

10. A method for summarizing daily life information, the method comprising: collecting log information comprising the daily life information from at least one electronic device; analyzing the log information collected and deciding at least one topic representing the daily life information; generating at least one sentence representing the daily life information based on at least one of a reference topic representative of the daily life information decided, weather, and an emotion; and displaying the generated at least one sentence. 17. The method of claim 10 , wherein generating the sentence comprises: constructing at least one middle sentence using a sentence constituent element comprised in an episode being set at a reference time; removing a middle sentence of a same meaning; and adding sentence connective verb ending and ending verb information to the middle sentence, and generating a sentence for the episode.

7. A method comprising: implementing on a computing device by a processor configured to execute instructions that, when executed by the processor, direct the computing device to perform steps comprising: providing a plurality of management packs wherein each management pack comprises: a model declaration that represents an abstract model that describes components of the management pack, properties associated with the components, and relationships between the components, the components representing objects in a subsystem of a modeled system; discovery rules that describe how to find entities in a particular environment and relationships between the entities, the entities being instantiations of the objects; a monitoring policy that comprises a state machine that describes states and transitions associated with each entity of the entities and monitors a particular state of the entity; and a causality model associated with the model declaration and used to compute causality that: uses the discovery rules and the monitoring policy to express causality across the subsystem of the modeled system; expresses causality in a manner that does not require specific knowledge of an instance of remaining management packs; and expresses multiple different reasons for an observable fault, with at least one reason being associated with a different entity in a different management pack than an entity for which causality is expressed; expressing, in the management pack, causality in terms of classes, relationships and monitors; and enabling a root cause analysis engine to access and utilize data generated by the causality model to determine a root cause of the observable fault.

7. A method comprising: implementing on a computing device by a processor configured to execute instructions that, when executed by the processor, direct the computing device to perform steps comprising: providing a plurality of management packs wherein each management pack comprises: a model declaration that represents an abstract model that describes components of the management pack, properties associated with the components, and relationships between the components, the components representing objects in a subsystem of a modeled system; discovery rules that describe how to find entities in a particular environment and relationships between the entities, the entities being instantiations of the objects; a monitoring policy that comprises a state machine that describes states and transitions associated with each entity of the entities and monitors a particular state of the entity; and a causality model associated with the model declaration and used to compute causality that: uses the discovery rules and the monitoring policy to express causality across the subsystem of the modeled system; expresses causality in a manner that does not require specific knowledge of an instance of remaining management packs; and expresses multiple different reasons for an observable fault, with at least one reason being associated with a different entity in a different management pack than an entity for which causality is expressed; expressing, in the management pack, causality in terms of classes, relationships and monitors; and enabling a root cause analysis engine to access and utilize data generated by the causality model to determine a root cause of the observable fault. 13. The method of claim 7 , wherein at least some reasons are associated with a different entity from same class as the entity for which the causality is expressed.

10. A system comprising: a set of devices, at least one device of the system being arranged: to receive a real-world description in the form of an instruction set of a markup language, the real-world description including asset terms and effect terms, to request assets associated with the asset terms and effects associated with the effect terms according to the terms in the real-world description, and to modify at least one asset associated with an asset term in the real-world description according to at least one effect identified by associated with an effect term in the real-world description, the devices of the set being operated according to the at least one modified asset.

10. A system comprising: a set of devices, at least one device of the system being arranged: to receive a real-world description in the form of an instruction set of a markup language, the real-world description including asset terms and effect terms, to request assets associated with the asset terms and effects associated with the effect terms according to the terms in the real-world description, and to modify at least one asset associated with an asset term in the real-world description according to at least one effect identified by associated with an effect term in the real-world description, the devices of the set being operated according to the at least one modified asset. 13. A system according to claim 10 , wherein a device of the set is a local server.

2. The computer-implemented method of claim 1 , wherein the preprocessing comprises: matching the prefix and the trailing context in the first document with the matching prefixes in the transformation database.

2. The computer-implemented method of claim 1 , wherein the preprocessing comprises: matching the prefix and the trailing context in the first document with the matching prefixes in the transformation database. 3. The computer-implemented method of claim 2 , wherein the matching further comprises matching a preceding context with a preceding context in the transformation database, wherein the preceding context is a third string of tokens that precede the prefix.

14. A non-transitory computer-readable medium having stored thereon program code, the program code executable by a computer to perform a process comprising: receiving text; extracting a plurality of linguistic entities and associated linguistic entity categories based on the text; determining one or more semantic objects of a semantic layer based on the linguistic entity categories, wherein each of the one or more semantic objects of the semantic layer associates one or more physical entities stored in a data source with user-friendly names; determining an analysis context based on the text, the linguistic entities, and the associated linguistic entity categories; after determination of the analysis context and the one or more semantic objects, generating a query of the semantic layer based on the analysis context and the one or more semantic objects, wherein the generating the query of the semantic layer comprises: determining, based on the linguistic entity categories that were extracted, whether the one or more semantic objects are to be filtered, and in response to determining to filter the one or more semantic objects, determining how to filter the semantic objects, wherein when a value from a first linguistic entity category matches a value for one of the one or more semantic objects, using the value from the first linguistic entity category as a query filter, and when only a single entity is mentioned for the first linguistic entity category, including the entity in the query filter and removing the first linguistic entity category from a result of the query of the semantic layer; and receiving a structured data result in response to the query of the semantic layer.

14. A non-transitory computer-readable medium having stored thereon program code, the program code executable by a computer to perform a process comprising: receiving text; extracting a plurality of linguistic entities and associated linguistic entity categories based on the text; determining one or more semantic objects of a semantic layer based on the linguistic entity categories, wherein each of the one or more semantic objects of the semantic layer associates one or more physical entities stored in a data source with user-friendly names; determining an analysis context based on the text, the linguistic entities, and the associated linguistic entity categories; after determination of the analysis context and the one or more semantic objects, generating a query of the semantic layer based on the analysis context and the one or more semantic objects, wherein the generating the query of the semantic layer comprises: determining, based on the linguistic entity categories that were extracted, whether the one or more semantic objects are to be filtered, and in response to determining to filter the one or more semantic objects, determining how to filter the semantic objects, wherein when a value from a first linguistic entity category matches a value for one of the one or more semantic objects, using the value from the first linguistic entity category as a query filter, and when only a single entity is mentioned for the first linguistic entity category, including the entity in the query filter and removing the first linguistic entity category from a result of the query of the semantic layer; and receiving a structured data result in response to the query of the semantic layer. 16. The medium according to claim 14 , the process further comprising: identifying two or more phrases from the text, wherein determination of an analysis context comprises: for each of the two or more phrases, identification of an associated linguistic entity.

21. The system of claim 13 , wherein receiving the request by the first user to expose the particular resource identifier comprises receiving a request by the first user to share the particular resource identifier with one or more users identified as a social network connection of the first user, and wherein exposing the particular resource identifier to the second user comprises determining that the second user is identified as a social network friend of the first user, then exposing the particular resource identifier to the second user.

21. The system of claim 13 , wherein receiving the request by the first user to expose the particular resource identifier comprises receiving a request by the first user to share the particular resource identifier with one or more users identified as a social network connection of the first user, and wherein exposing the particular resource identifier to the second user comprises determining that the second user is identified as a social network friend of the first user, then exposing the particular resource identifier to the second user. 22. The system of claim 21 , wherein social network connections are social network groups.

9. The method of claim 6 , wherein determining which nodes and contexts of an input document are streamable comprises: initializing all data references as being streamable; matching a root path of the input document into a template; setting the root path of the input document as a current context; determining if the current context is streamable based on a data reference defining the current context; streaming the current context during parsing of the input document in response to the current context being streamable; and buffering the current context in response to the current context being non-streamable.

9. The method of claim 6 , wherein determining which nodes and contexts of an input document are streamable comprises: initializing all data references as being streamable; matching a root path of the input document into a template; setting the root path of the input document as a current context; determining if the current context is streamable based on a data reference defining the current context; streaming the current context during parsing of the input document in response to the current context being streamable; and buffering the current context in response to the current context being non-streamable. 10. The method of claim 9 , wherein determining if the current context is streamability comprises: determining if an output construct requires output generation in a non-document order; marking a data reference as non-streamable in response to the output construct requiring output generation in a non-document order and buffering a re-ordered form of the input document; marking a first input data reference required to produce the output construct as streamable in response to the output construct not requiring generation in a non-document order; and determining if previous input data reference in the current context only references information in an opening element SAX-like event, wherein the current data reference is streamable in response to the previous input data referencing information in the opening element SAX-like event.

1. A method comprising: maintaining, using a database system, at least one database storing a plurality of enterprise work records as data objects; Maintaining timeline data characterizing a timeline of past and future events related to at least one of the enterprise work records; processing a request to provide one or more applications to a user device; causing display, using at least one of the applications, in a user interface at the user device, of a graphical representation of the timeline comprising a chronological arrangement of feed items related to the at least one enterprise work record, the feed items comprising past feed items representing the past events and future feed items representing the future events, each feed item being of one of a plurality of event types; identifying a first one of the feed items, the first feed item being of a first event type and having a first event date; responsive to identifying the first feed item: causing display of a first visual indicator indicating the first event type, and causing display of a second visual indicator indicating the first event date, the second visual indicator being offset from the first visual indicator; processing a selection of the second visual indicator associated with the first feed item; causing updating of the graphical representation of the timeline comprising the selected second visual indicator being substantially aligned with the first visual indicator; processing notification data regarding a first future event related to the at least one enterprise work record, the notification data identifying a future event date, a location, and at least one user; and providing the notification data to the user device, the notification data capable of being processed to update the graphical representation of the timeline to represent the first future event as a designated future feed item according to the future event date, the designated future feed item configured to display the future event date, the location, and/or the at least one user.

1. A method comprising: maintaining, using a database system, at least one database storing a plurality of enterprise work records as data objects; Maintaining timeline data characterizing a timeline of past and future events related to at least one of the enterprise work records; processing a request to provide one or more applications to a user device; causing display, using at least one of the applications, in a user interface at the user device, of a graphical representation of the timeline comprising a chronological arrangement of feed items related to the at least one enterprise work record, the feed items comprising past feed items representing the past events and future feed items representing the future events, each feed item being of one of a plurality of event types; identifying a first one of the feed items, the first feed item being of a first event type and having a first event date; responsive to identifying the first feed item: causing display of a first visual indicator indicating the first event type, and causing display of a second visual indicator indicating the first event date, the second visual indicator being offset from the first visual indicator; processing a selection of the second visual indicator associated with the first feed item; causing updating of the graphical representation of the timeline comprising the selected second visual indicator being substantially aligned with the first visual indicator; processing notification data regarding a first future event related to the at least one enterprise work record, the notification data identifying a future event date, a location, and at least one user; and providing the notification data to the user device, the notification data capable of being processed to update the graphical representation of the timeline to represent the first future event as a designated future feed item according to the future event date, the designated future feed item configured to display the future event date, the location, and/or the at least one user. 5. The method of claim 1 , wherein the notification data is associated with a request to join a secure portal that is automatically created when a user shares a file with a customer.

5. A non-transitory computer readable storage medium storing instructions executable by a data processing apparatus and that upon such execution causes the data processing apparatus to perform operations comprising: providing, by the data processing apparatus to a first plurality of user devices, a first user interface that includes: a first task definition describing a first user task to generate a command sentence for an action; a first set of non-terminal fields, each non-terminal field in the set listing a non-terminal type and a terminal that parses to the non-terminal type; and a command sentence input field in which a user-generated command sentence is input by the user; receiving, by the data processing apparatus and from the plurality of user devices, user-generated command sentences input into the command sentence input field; providing, by the data processing apparatus to a second plurality of user devices, a second user interface that includes: one of the user-generated command sentences selected from the received user-generated command sentences; the first set of non-terminal fields, each non-terminal field in the first set listing the non-terminal type and the terminal that parses to the non-terminal type; a second task definition describing a second user task to classify each of a plurality of n-grams of the command sentence as belonging to one of: the non-terminal types in the set of non-terminal types; or none of the non-terminal types in the set of non-terminal types; receiving, by the data processing apparatus and from the second plurality of user devices, second user task response data classifying the n-grams of the command sentence, wherein for each non-terminal type at least a respective first set of n-grams are classified as belonging to the non-terminal type and at least a second set of n-grams are classified as belonging to none of the non-terminal types; generating, by the data processing apparatus, command grammars for the action from the second user task response data, each of the command grammars defining non-terminals of each of the non-terminal types and at least one terminal defining at least one of the second set of n-grams; and persisting the command grammars to a command model.

5. A non-transitory computer readable storage medium storing instructions executable by a data processing apparatus and that upon such execution causes the data processing apparatus to perform operations comprising: providing, by the data processing apparatus to a first plurality of user devices, a first user interface that includes: a first task definition describing a first user task to generate a command sentence for an action; a first set of non-terminal fields, each non-terminal field in the set listing a non-terminal type and a terminal that parses to the non-terminal type; and a command sentence input field in which a user-generated command sentence is input by the user; receiving, by the data processing apparatus and from the plurality of user devices, user-generated command sentences input into the command sentence input field; providing, by the data processing apparatus to a second plurality of user devices, a second user interface that includes: one of the user-generated command sentences selected from the received user-generated command sentences; the first set of non-terminal fields, each non-terminal field in the first set listing the non-terminal type and the terminal that parses to the non-terminal type; a second task definition describing a second user task to classify each of a plurality of n-grams of the command sentence as belonging to one of: the non-terminal types in the set of non-terminal types; or none of the non-terminal types in the set of non-terminal types; receiving, by the data processing apparatus and from the second plurality of user devices, second user task response data classifying the n-grams of the command sentence, wherein for each non-terminal type at least a respective first set of n-grams are classified as belonging to the non-terminal type and at least a second set of n-grams are classified as belonging to none of the non-terminal types; generating, by the data processing apparatus, command grammars for the action from the second user task response data, each of the command grammars defining non-terminals of each of the non-terminal types and at least one terminal defining at least one of the second set of n-grams; and persisting the command grammars to a command model. 6. The non-transitory computer readable medium of claim 5 , wherein the command model facilitates, for each command grammar, the generation of an action score for the action from a bottom-up parse of an input sentence using the command grammar.

16. The method of claim 1 , further comprising: using the processor executing instructions to operate the EDA software by: determining whether the query is activated for keyword augmentation, wherein the query is activated when query includes a term that matches a term in the first file, when the query is activated: augmenting the query with at least the first search instruction, and performing the augmented query in the at least one field in the augmented CDF information set, and when the first file is not activated: performing the query in the at least one field in the augmented CDF information set, wherein the first file is a keyword search augmentation structure file.

16. The method of claim 1 , further comprising: using the processor executing instructions to operate the EDA software by: determining whether the query is activated for keyword augmentation, wherein the query is activated when query includes a term that matches a term in the first file, when the query is activated: augmenting the query with at least the first search instruction, and performing the augmented query in the at least one field in the augmented CDF information set, and when the first file is not activated: performing the query in the at least one field in the augmented CDF information set, wherein the first file is a keyword search augmentation structure file. 17. The method of claim 16 , further comprising: using the processor executing instructions to operate the EDA software by: when the query is activated: augmenting the CDF information set by the first file so that the augmented CDF file contains the at least one additional field.

22. A computer storage medium encoded with computer program instructions that when executed by one or more computers cause the one or more computers to perform operations including: providing a user interface for creating a table having at least one input column and at least one output column, wherein each input column is associated with an input variable and each output column is associated with an output variable; in at least one row of the table, receiving one or more conditions on input values in respective input columns, the conditions in the at least one row identifying more than one set of potential values of the input variables, and receiving one or more output values in respective output columns, thereby defining a rule case of a rule specification; generating a function for transforming data based on the rule specification; associating the function with the functional component; receiving changes to values in the rule specification, the changes including new potential values of the input variables for a condition; confirming that the changed rule specification is valid; and associating a new function with the functional component.

22. A computer storage medium encoded with computer program instructions that when executed by one or more computers cause the one or more computers to perform operations including: providing a user interface for creating a table having at least one input column and at least one output column, wherein each input column is associated with an input variable and each output column is associated with an output variable; in at least one row of the table, receiving one or more conditions on input values in respective input columns, the conditions in the at least one row identifying more than one set of potential values of the input variables, and receiving one or more output values in respective output columns, thereby defining a rule case of a rule specification; generating a function for transforming data based on the rule specification; associating the function with the functional component; receiving changes to values in the rule specification, the changes including new potential values of the input variables for a condition; confirming that the changed rule specification is valid; and associating a new function with the functional component. 35. The computer storage medium of claim 22 in which receiving the table of test columns includes receiving from a user a set of input values, matching the set of input values to the potential input values of the rule specification, and storing the set of input values to a column of the table.

10. A system comprising: a processor; and computer-readable medium coupled to the processor and having instructions stored thereon, which, when executed by the processor, cause the processor to perform operations comprising: obtaining a plurality of instances and a plurality of attributes, wherein each instance has one or more attributes of the plurality of attributes as attributes of the instance; for each attribute of an instance: identifying a plurality documents from an unstructured document collection that are relevant to the instance, where each of the documents include at least a value for the attribute of the instance; grouping values of the attribute of the instance into two or more groups; and establishing a subset of the one or more values of the attribute as characterizing the instance including selecting one group of values from the two or more groups; and adding each instance, the respective attributes of each instance, and the respective subset of values for the corresponding attributes to a structured data collection.

10. A system comprising: a processor; and computer-readable medium coupled to the processor and having instructions stored thereon, which, when executed by the processor, cause the processor to perform operations comprising: obtaining a plurality of instances and a plurality of attributes, wherein each instance has one or more attributes of the plurality of attributes as attributes of the instance; for each attribute of an instance: identifying a plurality documents from an unstructured document collection that are relevant to the instance, where each of the documents include at least a value for the attribute of the instance; grouping values of the attribute of the instance into two or more groups; and establishing a subset of the one or more values of the attribute as characterizing the instance including selecting one group of values from the two or more groups; and adding each instance, the respective attributes of each instance, and the respective subset of values for the corresponding attributes to a structured data collection. 14. The system of claim 10 , where establishing the subset comprises selecting the group based at least in part on a value in the group being drawn from a document relevant to another instance in the structured data collection.

1. A method comprising: receiving a spoken communication from a first user, the spoken communication comprising an address; extracting, via a processor, the address automatically from the spoken communication, to yield an extracted address; displaying to a second user a selection of addresses, the selection of addresses comprising the extracted address and an alternate address for the first user, the alternate address being stored in a contacts list, the selection of addresses displayed in an order prioritizing a preferred address of the extracted address and the alternate address, where the preferred address is determined based on a user history of the first user by the processor; and receiving from the second user a selection of a selected address from the selection of addresses to initiate communication with the first user.

1. A method comprising: receiving a spoken communication from a first user, the spoken communication comprising an address; extracting, via a processor, the address automatically from the spoken communication, to yield an extracted address; displaying to a second user a selection of addresses, the selection of addresses comprising the extracted address and an alternate address for the first user, the alternate address being stored in a contacts list, the selection of addresses displayed in an order prioritizing a preferred address of the extracted address and the alternate address, where the preferred address is determined based on a user history of the first user by the processor; and receiving from the second user a selection of a selected address from the selection of addresses to initiate communication with the first user. 4. The method of claim 1 , wherein the address comprises one of a phone number, an email address, a website, an internet protocol address, a file transfer protocol server, an instant messaging address, global positioning system coordinates, and a physical address.

3. The method according to claim 2 , after it is detected that the number of people in the user image is a preset value, the method further comprising: extracting, by the speech interaction apparatus, face data from the user image; and performing, by the speech interaction apparatus, face recognition on the face data to obtain a second user attribute recognition result.

3. The method according to claim 2 , after it is detected that the number of people in the user image is a preset value, the method further comprising: extracting, by the speech interaction apparatus, face data from the user image; and performing, by the speech interaction apparatus, face recognition on the face data to obtain a second user attribute recognition result. 4. The method according to claim 3 , wherein the performing a corresponding operation according to at least the first user attribute recognition result and the content recognition result comprises: weighting, by the speech interaction apparatus, the first user attribute recognition result and the second user attribute recognition result to obtain a final user attribute recognition result; and performing, by the speech interaction apparatus, a corresponding operation according to the final user attribute recognition result and the content recognition result.

16. The computer-readable storage device of claim 15 , wherein determining candidate user profiles that are associated with a shared digital assistant device comprises: determining a relationship between each of a plurality of user profiles and the shared digital assistant device; determining, for each user profile, whether the relationship is indicative of an association between the user profile and the shared digital assistant device; and identifying, for each user profile having a relationship indicative of an association with the shared digital assistant device, the user profile as being one of the candidate user profiles associated with the shared digital assistant device.

16. The computer-readable storage device of claim 15 , wherein determining candidate user profiles that are associated with a shared digital assistant device comprises: determining a relationship between each of a plurality of user profiles and the shared digital assistant device; determining, for each user profile, whether the relationship is indicative of an association between the user profile and the shared digital assistant device; and identifying, for each user profile having a relationship indicative of an association with the shared digital assistant device, the user profile as being one of the candidate user profiles associated with the shared digital assistant device. 18. The computer-readable storage device of claim 16 , wherein, for each of the plurality of user profiles, the relationship comprises a geographical proximity of at least one user device associated with the user profile to the shared digital assistant device.

1. A method to enable improved analysis and use of sociological data, the method comprising: identifying causal relationships between a plurality of documents; identifying a plurality of characteristics of a communication, including a modality used, actors involved, proximate events of relevance, and a tone used by an actor in the communication; and enabling a user to query based on available characteristics.

1. A method to enable improved analysis and use of sociological data, the method comprising: identifying causal relationships between a plurality of documents; identifying a plurality of characteristics of a communication, including a modality used, actors involved, proximate events of relevance, and a tone used by an actor in the communication; and enabling a user to query based on available characteristics. 9. The method of claim 1 , further comprising: identifying, for a particular actor, divergence in tones in communications with different actors on a particular topic within a given timeframe.

18. A speech synthesis system comprising: a voice generation device for processing an acoustic phoneme sequence representative of a text; and a duration modeling device coupled to said voice generation device for receiving phonemes from said voice generation device and providing phoneme durations using a phoneme duration model, wherein said phoneme duration model generates model coefficients by developing a non-exponential functional transformation comprising a root sinusoidal transformation that is controlled in response to a minimum phoneme duration and a maximum phoneme duration, wherein said root sinusoidal transformation is expressed by ##EQU8## where x comprises one or more of a plurality of contextual factors influencing the duration of a phoneme, A is the minimum phoneme duration observed in training data, B is the maximum phoneme duration observed in training data, .alpha. controls the amount of shrinking and expansion on either side of a main inflection point, and .beta. controls the position of the main inflection point, and wherein said duration modeling device receives said model coefficients from said phoneme duration model and generates at least one phoneme having a duration using a generalized additive model for each phoneme of the received text.

18. A speech synthesis system comprising: a voice generation device for processing an acoustic phoneme sequence representative of a text; and a duration modeling device coupled to said voice generation device for receiving phonemes from said voice generation device and providing phoneme durations using a phoneme duration model, wherein said phoneme duration model generates model coefficients by developing a non-exponential functional transformation comprising a root sinusoidal transformation that is controlled in response to a minimum phoneme duration and a maximum phoneme duration, wherein said root sinusoidal transformation is expressed by ##EQU8## where x comprises one or more of a plurality of contextual factors influencing the duration of a phoneme, A is the minimum phoneme duration observed in training data, B is the maximum phoneme duration observed in training data, .alpha. controls the amount of shrinking and expansion on either side of a main inflection point, and .beta. controls the position of the main inflection point, and wherein said duration modeling device receives said model coefficients from said phoneme duration model and generates at least one phoneme having a duration using a generalized additive model for each phoneme of the received text. 19. The speech synthesis of claim 18 further comprising: a pitch modeling device coupled to the duration modeling device that receives at least one phoneme having a duration and, using pitch information, provides an acoustic sequence of synthesized speech signals representative of said text.

1. A method for forming and assembling an individual self-contained pre-linked cross-device software application as an application package, each application package or portion of an application package is adapted to execute on an interoperability engine in a device and to distribute the application package or a portion of the application package to other interoperability engines in other devices over a communications link, the other devices being formed into unified teams for cooperative execution over a plurality of the teamed devices, the method comprising: forming the application package from source materials which encapsulate a main application function code portion, a main application data portion, any optional binary encoded parts portion, and a discovery and distribution code portion; and encoding the application package portions into a linear binary data structure of addressable parts wherein the application package includes self-contained and pre-linked elements; the discovery and distribution code portion encoded to be operable during execution to discover capabilities and resources of a device and then to distribute executable sub-units of the application package over one or more communication links to other devices so that the distributed executable sub-units execute on a team of devices that are unified by the distributed executable sub-units to carry out the function of the application where each device in said team of devices is running a unification engine adapted for executing at least the distributed executable sub-units of the application; each said linear binary data structure including: (i) exactly one linear main program code part including coded instructions where the addresses of all the coded instructions are located in one linear consecutive address space, and (ii) exactly one linear main program data part including data elements and where the addresses of all the data elements are located in one linear consecutive address space; the linear binary data structure including two or more executable sub-units made up of exactly one linear contiguous subset of the main program code part and exactly one linear contiguous subset of the main program data part, along with zero or more of said optional binary encoded parts; and wherein all the code that embodies and carries out the function of the software application on all said teamed devices is contained in the original application package data structure before being distributed to the teamed devices.

1. A method for forming and assembling an individual self-contained pre-linked cross-device software application as an application package, each application package or portion of an application package is adapted to execute on an interoperability engine in a device and to distribute the application package or a portion of the application package to other interoperability engines in other devices over a communications link, the other devices being formed into unified teams for cooperative execution over a plurality of the teamed devices, the method comprising: forming the application package from source materials which encapsulate a main application function code portion, a main application data portion, any optional binary encoded parts portion, and a discovery and distribution code portion; and encoding the application package portions into a linear binary data structure of addressable parts wherein the application package includes self-contained and pre-linked elements; the discovery and distribution code portion encoded to be operable during execution to discover capabilities and resources of a device and then to distribute executable sub-units of the application package over one or more communication links to other devices so that the distributed executable sub-units execute on a team of devices that are unified by the distributed executable sub-units to carry out the function of the application where each device in said team of devices is running a unification engine adapted for executing at least the distributed executable sub-units of the application; each said linear binary data structure including: (i) exactly one linear main program code part including coded instructions where the addresses of all the coded instructions are located in one linear consecutive address space, and (ii) exactly one linear main program data part including data elements and where the addresses of all the data elements are located in one linear consecutive address space; the linear binary data structure including two or more executable sub-units made up of exactly one linear contiguous subset of the main program code part and exactly one linear contiguous subset of the main program data part, along with zero or more of said optional binary encoded parts; and wherein all the code that embodies and carries out the function of the software application on all said teamed devices is contained in the original application package data structure before being distributed to the teamed devices. 10. The method of claim 1 , wherein the framework comprises: a master rendition; a rendition different from the master rendition; a gizmo; a pointer to a parent gizmo; at least one pointer to a child gizmo; a setup method; and a process method.

14. A method for creating an accumulation of documents stored as a cluster by utilizing a process to create a hashing vector to determine whether to add a document to a cluster.

14. A method for creating an accumulation of documents stored as a cluster by utilizing a process to create a hashing vector to determine whether to add a document to a cluster. 15. The computer method for creating an accumulation of documents stored as a cluster as in claim 14 , further comprising one or more steps selected from the group consisting of (i) adjusting by an aging function, (ii) utilizing a mask to identify document clusters, (iii) auto-labeling emails according to instantiation of labels on the basis of whether these labels are pre-defined or user-defined, (iv) determining whether text inputs are related or not related, by assigning a score relating to this determination, (v) further comprising forming email clustering and displaying in graphical form long hash and small hash, (vi) forming email clustering utilizing a small hash threshold, (vii) forming email clustering utilizing small hash length, (viii) forming email clustering utilizing small hash average, (ix) ascribing retention rates to clusters of email based upon clustering, (x) creating hash methods to minimize computation time for clustering and maximize accuracy of classification, (xi) using a clustering method for the purpose of identifying emails containing one of a malicious code, a phishing, a virus or a worm, (xii) using a clustering method for the purpose of identifying an email for purposes of delta storage, (xiii) using a mask on a time frame for the purpose of identifying a cluster member, (xiv) using an aging function to identify a cluster member, (xv) using a cluster to populate a central repository of hash data, (xvi) populating a hash table for anonymous sharing of information between organization,. (xvii) selecting a hash function with small collisions, (xviii) sorting by class a indexing technique to route queries, (xix) using classes to route queries as part of a cluster, (xx) choosing a prime number P for the purpose of determining equivalence classes (mod P) in a stackable hash method, and (xxi) using a brightness and an aging function in a hash method for creating a cluster.

1. A machine implemented method comprising: receiving a first user input comprising a search query; displaying, by a data processing system, in a search interface accessible across a plurality of computer application programs, a plurality of results from a search performed across files and programs accessible by the data processing system, wherein the plurality of results matches the search query and are categorized into a plurality of categories, only a first subset of the plurality of results are displayed for each of the plurality of categories, and the results in the first subset are displayed grouped together in proximity to a displayed representation of a corresponding category; receiving a second user input comprising a selection of one of the displayed representations of the plurality of categories; and in response to the second user input, displaying, in the search interface, a second subset of the plurality of results, wherein the second subset matches the search query and is categorized into a plurality of subcategories of the selected category, and the results categorized into each subcategory is displayed grouped together in proximity to a displayed representation of a corresponding subcategory.

1. A machine implemented method comprising: receiving a first user input comprising a search query; displaying, by a data processing system, in a search interface accessible across a plurality of computer application programs, a plurality of results from a search performed across files and programs accessible by the data processing system, wherein the plurality of results matches the search query and are categorized into a plurality of categories, only a first subset of the plurality of results are displayed for each of the plurality of categories, and the results in the first subset are displayed grouped together in proximity to a displayed representation of a corresponding category; receiving a second user input comprising a selection of one of the displayed representations of the plurality of categories; and in response to the second user input, displaying, in the search interface, a second subset of the plurality of results, wherein the second subset matches the search query and is categorized into a plurality of subcategories of the selected category, and the results categorized into each subcategory is displayed grouped together in proximity to a displayed representation of a corresponding subcategory. 4. The method of claim 1 , further comprising: determining whether a hierarchy of categories includes additional subcategories of the selected category.

6. The method of claim 5 , wherein generating a number of language objects in response to detecting the second delimited ambiguous input further comprises: generating a number of prefix objects corresponding with the second delimited ambiguous input; identifying the language objects corresponding with the prefix objects, each of the language objects being associated with a frequency object; associating the frequency objects of the language objects with the corresponding prefix objects; and generating an output set from at least a portion of the prefix objects.

6. The method of claim 5 , wherein generating a number of language objects in response to detecting the second delimited ambiguous input further comprises: generating a number of prefix objects corresponding with the second delimited ambiguous input; identifying the language objects corresponding with the prefix objects, each of the language objects being associated with a frequency object; associating the frequency objects of the language objects with the corresponding prefix objects; and generating an output set from at least a portion of the prefix objects. 8. The method of claim 6 , further comprising: outputting at least a portion of the output set; and sorting the output portion of the output set in a descending order of frequency values of the frequency objects associated with the prefix objects in the output portion of the output set.

1. A system that facilitates generation of a system profile, comprising: a storage component that receives data relating to respective existing profiles of a community of disparate users, the existing profiles are generated by a plurality of client computers and represent application configuration settings used by the respective users of the client computers; an analyzer that processes the existing profile data for the community of users in view of demographic data of a first user and selects a subset of the existing profiles to present to the first user based on similarities between the first user and the respective users in the community; a filter component that applies collaborative filtering in accordance with the analyzer to process previous system settings preferences of existing users in the community to predict likely or possible settings or profiles for new users of a system, the filter component identifies software settings or preferences about context-sensitive computing that are applicable to an application employed by the first user; and a user interface that displays the subset of existing profiles selected by the analyzer, the user interface having at least one input to select from the displayed subset of profiles, the selected profile is used to configure the application settings for the first user; wherein the profiles include user definitions of a cost of interruption associated with each of a set of activities defined in a communications application, and the filter component selects at least one of the profiles for presentation to the first user based on a calculated similarity between the cost of interruption definitions in the existing profiles and the first user's cost of interruption definitions, wherein selection of the at least one profile by the first user applies the configuration settings defined in the selected profile to the communications application employed by the first user.

1. A system that facilitates generation of a system profile, comprising: a storage component that receives data relating to respective existing profiles of a community of disparate users, the existing profiles are generated by a plurality of client computers and represent application configuration settings used by the respective users of the client computers; an analyzer that processes the existing profile data for the community of users in view of demographic data of a first user and selects a subset of the existing profiles to present to the first user based on similarities between the first user and the respective users in the community; a filter component that applies collaborative filtering in accordance with the analyzer to process previous system settings preferences of existing users in the community to predict likely or possible settings or profiles for new users of a system, the filter component identifies software settings or preferences about context-sensitive computing that are applicable to an application employed by the first user; and a user interface that displays the subset of existing profiles selected by the analyzer, the user interface having at least one input to select from the displayed subset of profiles, the selected profile is used to configure the application settings for the first user; wherein the profiles include user definitions of a cost of interruption associated with each of a set of activities defined in a communications application, and the filter component selects at least one of the profiles for presentation to the first user based on a calculated similarity between the cost of interruption definitions in the existing profiles and the first user's cost of interruption definitions, wherein selection of the at least one profile by the first user applies the configuration settings defined in the selected profile to the communications application employed by the first user. 7. The system of claim 1 , the filter component employs explicit or implicit voting to determine the system settings preferences of disparate users in the community in order to predict the likely or possible settings or profiles for new users of the system.

1. A computer implemented method for generating a prediction query, comprising: electronically displaying a first user interface area adapted to display a mining model and receive inputs matching a mining model to an input table; in response to a user input, electronically displaying a second user interface area adapted to display mining models and receive a selection of a mining model; receiving a selection of a mining model in the second user interface area and displaying the selected model in the first user interface area; electronically displaying a third user interface area, the third user interface area adapted to display a database schema and receive an input matching an input table displayed in the third user interface area with a mining model displayed in the first user interface area; receiving in the first user interface area and the third user interface area an input of at least one line segment between at least one column of the mining model and at least one column of the at least one input table; and in response to receiving the input of at least one line segment, automatically generating a prediction query and displaying the generated prediction query in a fourth user interface area.

1. A computer implemented method for generating a prediction query, comprising: electronically displaying a first user interface area adapted to display a mining model and receive inputs matching a mining model to an input table; in response to a user input, electronically displaying a second user interface area adapted to display mining models and receive a selection of a mining model; receiving a selection of a mining model in the second user interface area and displaying the selected model in the first user interface area; electronically displaying a third user interface area, the third user interface area adapted to display a database schema and receive an input matching an input table displayed in the third user interface area with a mining model displayed in the first user interface area; receiving in the first user interface area and the third user interface area an input of at least one line segment between at least one column of the mining model and at least one column of the at least one input table; and in response to receiving the input of at least one line segment, automatically generating a prediction query and displaying the generated prediction query in a fourth user interface area. 7. The computer implemented method according to claim 1 , wherein said selecting at least one mining model includes selecting one of a trained mining model within an opened project and an existing model from a server.

70. A computer-implemented method for the creation, via a computer processor, of at least one flattened readable object that is readable by a search engine from at least one hierarchical structured data object stored in a database, the method comprising: extracting, via the computer processor, the hierarchical structured data object from the database, wherein the hierarchical structured data object includes a hierarchical sequence of nodes related by at least one link and a plurality of content, and wherein at least one content of the plurality of content is nonreadable content that cannot be read by the search engine; mapping, via the computer processor, structure and contents of the structured data object into a generic data model; and creating, via the computer processor, the flattened readable object from the generic data model, wherein creating includes converting the nonreadable content of the hierarchical structured data object into the readable content for the search engine.

70. A computer-implemented method for the creation, via a computer processor, of at least one flattened readable object that is readable by a search engine from at least one hierarchical structured data object stored in a database, the method comprising: extracting, via the computer processor, the hierarchical structured data object from the database, wherein the hierarchical structured data object includes a hierarchical sequence of nodes related by at least one link and a plurality of content, and wherein at least one content of the plurality of content is nonreadable content that cannot be read by the search engine; mapping, via the computer processor, structure and contents of the structured data object into a generic data model; and creating, via the computer processor, the flattened readable object from the generic data model, wherein creating includes converting the nonreadable content of the hierarchical structured data object into the readable content for the search engine. 71. The computer-implemented method of claim 70 wherein the hierarchical structured data object comprises a business object of an enterprise resource planning (ERP) software.

18. A non-transitory computer-readable medium having computer-readable instructions stored thereon, wherein the computer-readable instructions comprise: instructions to generate a plurality of word neurons; instructions to generate a plurality of sentence neurons; instructions to generate at least one document neuron, wherein the plurality of word neurons, the plurality of sentence neurons, and the at least one document neuron form at least a portion of a neural network; instructions to form a plurality of first connections between at least a portion of the plurality of word neurons and the plurality of sentence neurons; instructions to form a plurality of second connections between at least a portion of the plurality of word neurons and the at least one document neuron; instructions to excite a first sentence neuron of the plurality of sentence neurons in response to excitation of the at least one document neuron; and instructions to change a position of the plurality of word neurons on a display based on an input, and wherein the change in the position of one word neuron changes annotation corresponding to at least one of the plurality of sentence neurons.

18. A non-transitory computer-readable medium having computer-readable instructions stored thereon, wherein the computer-readable instructions comprise: instructions to generate a plurality of word neurons; instructions to generate a plurality of sentence neurons; instructions to generate at least one document neuron, wherein the plurality of word neurons, the plurality of sentence neurons, and the at least one document neuron form at least a portion of a neural network; instructions to form a plurality of first connections between at least a portion of the plurality of word neurons and the plurality of sentence neurons; instructions to form a plurality of second connections between at least a portion of the plurality of word neurons and the at least one document neuron; instructions to excite a first sentence neuron of the plurality of sentence neurons in response to excitation of the at least one document neuron; and instructions to change a position of the plurality of word neurons on a display based on an input, and wherein the change in the position of one word neuron changes annotation corresponding to at least one of the plurality of sentence neurons. 19. The non-transitory computer-readable medium of claim 18 , further comprising instructions to organize the plurality of word neurons into an input layer that receives an input query.

1. A non-transitory computer readable medium having stored thereon a sequence of instructions which when executed by a computer, causes the computer to perform a method comprising: generating a first graphical user interface comprising a text to topic button; receiving an input selecting the text to topic button; and generating a second graphical user interface comprising a plurality of percentage scales simultaneously displayed, each having an associated user-adjustable sliding button wherein a first percentage scale of the plurality of percentage scales controls a number of subjects that a text file is partitioned into and a second percentage scale of the plurality of percentage scales controls a number of subtopics of the number of subjects.

1. A non-transitory computer readable medium having stored thereon a sequence of instructions which when executed by a computer, causes the computer to perform a method comprising: generating a first graphical user interface comprising a text to topic button; receiving an input selecting the text to topic button; and generating a second graphical user interface comprising a plurality of percentage scales simultaneously displayed, each having an associated user-adjustable sliding button wherein a first percentage scale of the plurality of percentage scales controls a number of subjects that a text file is partitioned into and a second percentage scale of the plurality of percentage scales controls a number of subtopics of the number of subjects. 4. The non-transitory computer readable medium of claim 1 comprising partitioning the text file into subjects in response to receiving the input.

15. A computer implemented method for identifying word-senses, the method comprising: generating, using a computer, a set of domain tables each comprising one or more arrays of aggregated statistical information corresponding to a plurality of words, one or more word-senses corresponding to the plurality of words, and temporal properties corresponding to the plurality of words, wherein the aggregated statistical information of words comprises temporal frequency of occurrence of words, wherein the temporal frequency of occurrence of words comprises a frequency of usage of words and corresponding word-senses during a specific time period; receiving a word; identifying, using the computer, the temporal frequency of occurrence corresponding to the received word from each domain table in the set of domain tables; associating, using the computer, the received word with a domain table in the set of domain tables, based on the temporal frequency of occurrence of the received word corresponding to the received word in the domain table meeting a threshold value; and identifying, using the computer, one or more word-senses corresponding to the received word based on one or more corresponding word-senses in the associated domain table based on the specific time period and one or more corresponding word-senses in a corresponding domain dictionary.

15. A computer implemented method for identifying word-senses, the method comprising: generating, using a computer, a set of domain tables each comprising one or more arrays of aggregated statistical information corresponding to a plurality of words, one or more word-senses corresponding to the plurality of words, and temporal properties corresponding to the plurality of words, wherein the aggregated statistical information of words comprises temporal frequency of occurrence of words, wherein the temporal frequency of occurrence of words comprises a frequency of usage of words and corresponding word-senses during a specific time period; receiving a word; identifying, using the computer, the temporal frequency of occurrence corresponding to the received word from each domain table in the set of domain tables; associating, using the computer, the received word with a domain table in the set of domain tables, based on the temporal frequency of occurrence of the received word corresponding to the received word in the domain table meeting a threshold value; and identifying, using the computer, one or more word-senses corresponding to the received word based on one or more corresponding word-senses in the associated domain table based on the specific time period and one or more corresponding word-senses in a corresponding domain dictionary. 16. The method of claim 15 , wherein the aggregated statistical information comprises frequency of usage, frequency of co-occurrence with other words, year of usage, or usage within a specific profession or domain, or context of usage.