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25. The computer process of claim 23 wherein a score derived from an N-Gram analysis forms an element for deciding if a claim ahs merit to warrant claim recovery efforts.

25. The computer process of claim 23 wherein a score derived from an N-Gram analysis forms an element for deciding if a claim ahs merit to warrant claim recovery efforts. 30. The computer process of claim 25 further including the step of incorporating term data in a dictionary of phrases.

3. The media of claim 1 , further comprising: appending a tag to the at least one suggested search term that is not a match; and displaying the least one suggested search term with the tag associated therewith as a highlighted text.

3. The media of claim 1 , further comprising: appending a tag to the at least one suggested search term that is not a match; and displaying the least one suggested search term with the tag associated therewith as a highlighted text. 4. The media of claim 3 , wherein a bold font is imposed on the highlighted text.

14. An article, comprising a non-transitory storage medium having stored instructions that enable a machine to: process a domain model of a data storage system, the domain model including classes having attributes including relationship, external, and computed, wherein vertices for the data storage system correspond to a respective one of the classes; generate a semantic model from an instantiation of the domain model based upon a topology of the data storage system to determine the vertices; load topology information from the instantiation of the domain model and the relationships; process updates of the attributes; send messages to a target vertex based on the processing of the updates of the attributes; determine a semantic model for a class of the target vertex; for each message for the target vertex; determine a semantic model for the target vertex for the message, wherein the semantic model has a first expression; update the first expression; find expressions that depend on the first expression; determine whether the found dependent expressions are in the same class as the first expression; create a new output message for a dependent vertex when the found dependent expressions are not in the same class as the first expression; for the found dependent expressions in the same class as the first expression: re-evaluate the found dependent expressions; add to a list of messages to be processed for a vertex corresponding to where the re-evaluated found dependent expressions changes value; generate a new external message if the found dependent expressions are marked for export; and process further attribute updates, wherein an output message for a dependent vertex is sent as a difference between the old and new values of an attribute.

14. An article, comprising a non-transitory storage medium having stored instructions that enable a machine to: process a domain model of a data storage system, the domain model including classes having attributes including relationship, external, and computed, wherein vertices for the data storage system correspond to a respective one of the classes; generate a semantic model from an instantiation of the domain model based upon a topology of the data storage system to determine the vertices; load topology information from the instantiation of the domain model and the relationships; process updates of the attributes; send messages to a target vertex based on the processing of the updates of the attributes; determine a semantic model for a class of the target vertex; for each message for the target vertex; determine a semantic model for the target vertex for the message, wherein the semantic model has a first expression; update the first expression; find expressions that depend on the first expression; determine whether the found dependent expressions are in the same class as the first expression; create a new output message for a dependent vertex when the found dependent expressions are not in the same class as the first expression; for the found dependent expressions in the same class as the first expression: re-evaluate the found dependent expressions; add to a list of messages to be processed for a vertex corresponding to where the re-evaluated found dependent expressions changes value; generate a new external message if the found dependent expressions are marked for export; and process further attribute updates, wherein an output message for a dependent vertex is sent as a difference between the old and new values of an attribute. 16. The article according to claim 14 , wherein at least one of the relationships includes a hint as to whether first and second ones of the classes on either end of an associate relationship should share a vertex representation.

1. A method comprising: receiving, by one or more computing devices, an ordered result set, the ordered result set including a plurality of individual search results of a search query, the individual search results having a presentation order from lower positions to higher positions, wherein a lower position is a position that is earlier in the result set; for each of the plurality of individual search results, determining, by at least one of the one or more computing devices, an individual result category and a corresponding individual result score based on content in the individual search result, wherein individual result score is a score that corresponds to the individual result category determined for the individual search result; applying, by at least one of the one or more computing devices, a weighting function to the individual result score of each individual search result, the weighting function using a position of the individual search result in the result set as a parameter, wherein a value of the function decreases if the position increases; determining, by at least one of the one or more computing devices, a result category and a result score for the search query using the individual result categories and corresponding individual weighted result scores; and storing, by at least one of the one or more computing devices, the result category and the result score.

1. A method comprising: receiving, by one or more computing devices, an ordered result set, the ordered result set including a plurality of individual search results of a search query, the individual search results having a presentation order from lower positions to higher positions, wherein a lower position is a position that is earlier in the result set; for each of the plurality of individual search results, determining, by at least one of the one or more computing devices, an individual result category and a corresponding individual result score based on content in the individual search result, wherein individual result score is a score that corresponds to the individual result category determined for the individual search result; applying, by at least one of the one or more computing devices, a weighting function to the individual result score of each individual search result, the weighting function using a position of the individual search result in the result set as a parameter, wherein a value of the function decreases if the position increases; determining, by at least one of the one or more computing devices, a result category and a result score for the search query using the individual result categories and corresponding individual weighted result scores; and storing, by at least one of the one or more computing devices, the result category and the result score. 4. The method of claim 1 , wherein: each individual result category comprises one or more components, each component including one or more words; and at least one individual result category is represented in a tree structure having multiple levels, the levels of the tree structure indicating a hierarchical relationship between the one or more components of the individual result category.

1. A method implemented in a computer infrastructure having computer executable code tangibly embodied in a computer readable storage medium having programming instructions configured to: receive a bilingual text which comprises a first set of characters in a Latin-based language and a second set of characters in a non Latin-based language; convert the second set of characters in the non Latin-based language in the bilingual text to a third set of characters in the Latin-based language based on a lookup table; add a prefix character and a postfix character to each converted word in the third set of characters; output an encoded representation of the bilingual text which comprises the converted third set of characters, the added prefix character to each converted word in the third set of characters, the postfix character to each converted word in the third set of characters, and the first set of characters in the Latin-based language; and generate a QR code based on the encoded representation of the bilingual text, wherein the generation of the QR code based on the encoded representation of the bilingual text embeds a higher number of Arabic characters than a generation of the QR code for the same bilingual text based solely on a standard QR encoding scheme.

1. A method implemented in a computer infrastructure having computer executable code tangibly embodied in a computer readable storage medium having programming instructions configured to: receive a bilingual text which comprises a first set of characters in a Latin-based language and a second set of characters in a non Latin-based language; convert the second set of characters in the non Latin-based language in the bilingual text to a third set of characters in the Latin-based language based on a lookup table; add a prefix character and a postfix character to each converted word in the third set of characters; output an encoded representation of the bilingual text which comprises the converted third set of characters, the added prefix character to each converted word in the third set of characters, the postfix character to each converted word in the third set of characters, and the first set of characters in the Latin-based language; and generate a QR code based on the encoded representation of the bilingual text, wherein the generation of the QR code based on the encoded representation of the bilingual text embeds a higher number of Arabic characters than a generation of the QR code for the same bilingual text based solely on a standard QR encoding scheme. 6. The method of claim 1 , wherein the lookup table comprises a mapping of the non Latin-based language to a corresponding Latin-based language.

3. The method of claim 2 , wherein the one or more supporting evidence measure knowledge values comprises a fuzzy logic truth supporting evidence value indicative of an amount of supporting evidence that an atomic logic term of a corresponding node is true/met, and a fuzzy logic falsity supporting evidence value indicative of an amount of supporting evidence that the atomic logic term is false/not met.

3. The method of claim 2 , wherein the one or more supporting evidence measure knowledge values comprises a fuzzy logic truth supporting evidence value indicative of an amount of supporting evidence that an atomic logic term of a corresponding node is true/met, and a fuzzy logic falsity supporting evidence value indicative of an amount of supporting evidence that the atomic logic term is false/not met. 8. The method of claim 3 , wherein propagating the at least one knowledge value of at least a subset of the nodes in the logical parse hierarchical representation to one or more other nodes in the logical parse hierarchical representation based on propagation rules comprises, for a NOT logical operator node in the hierarchical representation: propagating downward and upwards, in the hierarchical representation, a truth supporting evidence value and a falsity supporting evidence value of the at least one knowledge value of nodes associated with the NOT logical operator node by swapping the truth supporting evidence value and falsity supporting evidence value of parent nodes with child nodes; and preventing sideways propagation of knowledge values of nodes associated with the NOT logical operator.

1. A non-transitory article of manufacture comprising computer readable instructions stored thereon which when executed by a processor cause a computing environment to: receive, from a remote computer system, a data stream containing information from at least one instance of a data object with a structure unknown to the computing environment, the data stream comprising: a header, wherein the header of the data stream includes metadata describing one or more structure elements of the data object, and a body, wherein the body of the data stream includes the information from the at least one instance of the data object; extract the information from the at least one instance of the data object from the body of the data stream in accordance with the one or more structure elements described in the metadata; and dynamically create a user interface (UI) based on the one or more structure elements of the data object, wherein the UI includes a first area to show the one or more structure elements based on a description in the metadata, a second area to present information from the at least one instance of the data object corresponding to a selected element from the one or more structure elements, and a UI control mechanism to allow a user to select the element of the one or more structure elements, and to change a structure of the selected element or the information from the at least one instance of the data object corresponding to the selected element.

1. A non-transitory article of manufacture comprising computer readable instructions stored thereon which when executed by a processor cause a computing environment to: receive, from a remote computer system, a data stream containing information from at least one instance of a data object with a structure unknown to the computing environment, the data stream comprising: a header, wherein the header of the data stream includes metadata describing one or more structure elements of the data object, and a body, wherein the body of the data stream includes the information from the at least one instance of the data object; extract the information from the at least one instance of the data object from the body of the data stream in accordance with the one or more structure elements described in the metadata; and dynamically create a user interface (UI) based on the one or more structure elements of the data object, wherein the UI includes a first area to show the one or more structure elements based on a description in the metadata, a second area to present information from the at least one instance of the data object corresponding to a selected element from the one or more structure elements, and a UI control mechanism to allow a user to select the element of the one or more structure elements, and to change a structure of the selected element or the information from the at least one instance of the data object corresponding to the selected element. 5. The article of manufacture of claim 1 , comprising further computer readable instructions stored thereon which when executed by the processor cause the computing environment to: read from the metadata a definition of a hyperlink property of the data object indicative of a nested structure element of the one or more structure elements of the data object, wherein each instance of the data object includes a reference to at least one instance of another data object.

5. The method of claim 1 further comprising: receiving structure description data including tabular data, said tabular data including a representation of said basic hardware structure and said complex hardware structure; and converting said structure description data into said structured data.

5. The method of claim 1 further comprising: receiving structure description data including tabular data, said tabular data including a representation of said basic hardware structure and said complex hardware structure; and converting said structure description data into said structured data. 6. The method of claim 5 further comprising: receiving spreadsheet data including an equation interrelating a value of at least two cells of said spreadsheet data; modifying, in response to a user input, said spreadsheet data by means of a spreadsheet application based on said equation; and outputting said modified spreadsheet data as said structure description data.

15. One or more non-transitory computer-readable storage media encoded with instructions that, when executed by one or more computers, cause the one or more computers to perform operations comprising: receiving a first voice query; generating a first recognition output; receiving a second voice query; determining from a recognition of the second voice query that the second voice query triggers a correction request; using the first recognition output and the second recognition to determine a plurality of candidate corrections including: determining a misrecognition portion of the first recognition output, and substituting the misrecognition portion with one or more candidate n-grams to form a candidate correction; scoring each candidate correction; and generating a corrected recognition output for a particular candidate correction having a score that satisfies a threshold value.

15. One or more non-transitory computer-readable storage media encoded with instructions that, when executed by one or more computers, cause the one or more computers to perform operations comprising: receiving a first voice query; generating a first recognition output; receiving a second voice query; determining from a recognition of the second voice query that the second voice query triggers a correction request; using the first recognition output and the second recognition to determine a plurality of candidate corrections including: determining a misrecognition portion of the first recognition output, and substituting the misrecognition portion with one or more candidate n-grams to form a candidate correction; scoring each candidate correction; and generating a corrected recognition output for a particular candidate correction having a score that satisfies a threshold value. 19. The one or more non-transitory computer-readable storage media of claim 15 , wherein each candidate corrected query is scored based at least in part on a phonetic distance between the candidate correction and the first voice input.

1. A method in a wireless device comprising: upon an event triggering potential user notification: the wireless device processing the event triggering potential user notification according to customizable voice call settings if there is an active voice call; and the wireless device processing the event triggering potential user notification according to other settings if there is no active voice call.

1. A method in a wireless device comprising: upon an event triggering potential user notification: the wireless device processing the event triggering potential user notification according to customizable voice call settings if there is an active voice call; and the wireless device processing the event triggering potential user notification according to other settings if there is no active voice call. 14. The method of claim 1 wherein the event triggering potential user notification is in respect of at least one service of a plurality of concurrent circuit-switched and packet-switched services handled by the wireless device.

20. A non-transitory computer readable medium which stores a text-to-speech component comprising executable code that directs a client computing device to perform a process comprising: receiving text comprising a sequence of words; and assembling an audio presentation corresponding to the text, the audio presentation comprising a sequence of speech segments, wherein the sequence of speech segments is based at least in part on the sequence of words, and wherein assembling the audio presentation comprises: retrieving a first compressed speech segment; applying two decompression techniques to the first compressed speech segment to obtain a first speech segment; retrieving a second compressed speech segment; applying two decompression techniques to the second compressed speech segment to obtain a second speech segment; concatenating the first speech segment and the second speech segment.

20. A non-transitory computer readable medium which stores a text-to-speech component comprising executable code that directs a client computing device to perform a process comprising: receiving text comprising a sequence of words; and assembling an audio presentation corresponding to the text, the audio presentation comprising a sequence of speech segments, wherein the sequence of speech segments is based at least in part on the sequence of words, and wherein assembling the audio presentation comprises: retrieving a first compressed speech segment; applying two decompression techniques to the first compressed speech segment to obtain a first speech segment; retrieving a second compressed speech segment; applying two decompression techniques to the second compressed speech segment to obtain a second speech segment; concatenating the first speech segment and the second speech segment. 23. The non-transitory computer readable medium of claim 20 , wherein applying two decompression techniques to the first compressed speech segment comprises determining a level of time domain compression applied to the first compressed speech segment.

4. The method of claim 3 , further comprising: formatting the set of responses.

4. The method of claim 3 , further comprising: formatting the set of responses. 5. The method of claim 4 , further comprising: submitting the formatted set of responses to the user; and determining if a response of the submitted formatted set of responses is accepted by the user.

11. A computer program product comprising a computer readable storage medium having a computer readable program stored therein, wherein the computer readable program, when executed on a computing device, causes the computing device to: process the natural language content using a natural language processing (NLP) engine of the computing device; identify a segment of content within the natural language content that is not recognized by the NLP engine; analyze the segment to determine whether the segment contains computer code; in response to determining that the segment contains computer code, generating one or more code segment annotations for the computer code, wherein the one or more code segment annotations provide a natural language description of functionality of the computer code in the segment; store the one or more code segment annotations in association with the natural language content; and perform natural language processing, using the NLP engine, on the one or more code segment annotations to further process the natural language content.

11. A computer program product comprising a computer readable storage medium having a computer readable program stored therein, wherein the computer readable program, when executed on a computing device, causes the computing device to: process the natural language content using a natural language processing (NLP) engine of the computing device; identify a segment of content within the natural language content that is not recognized by the NLP engine; analyze the segment to determine whether the segment contains computer code; in response to determining that the segment contains computer code, generating one or more code segment annotations for the computer code, wherein the one or more code segment annotations provide a natural language description of functionality of the computer code in the segment; store the one or more code segment annotations in association with the natural language content; and perform natural language processing, using the NLP engine, on the one or more code segment annotations to further process the natural language content. 13. The computer program product of claim 11 , wherein the one or more code segment annotations further comprise content references that point to relevant portions of the natural language content that explicitly or implicitly refer to the segment or elements of the computer code within the segment, code segment references that point to the segment or elements within the computer code within the segment that are referenced by other portions of the natural language content, and relationships between the content references and code segment references.

5. The medium of claim 1 , wherein the active content is stored in the second domain as a sandbox document for the secure activation via the updated document of the first domain.

5. The medium of claim 1 , wherein the active content is stored in the second domain as a sandbox document for the secure activation via the updated document of the first domain. 6. The medium of claim 5 , wherein the updated document includes a code and wherein the hyperlink having the one time token is generated according to the code.

70. The apparatus of claim 48, wherein said means for selecting a language includes means for determining whether said calling party determined a language or dialect for delivery of messages to a dialed phone number.

70. The apparatus of claim 48, wherein said means for selecting a language includes means for determining whether said calling party determined a language or dialect for delivery of messages to a dialed phone number. 71. The apparatus of claim 70, wherein said means for selecting a language includes means for selecting said language or dialect determined for said dialed phone number.

1. A computer implemented method for interpreting legal regulations, the method comprising: receiving a plurality of legal regulations; deconstructing the plurality of legal regulations to form a computer interpretable regulation repository based on at least one of a regulatory rule model and Minsky's frames, wherein deconstructing the plurality of legal regulations based on the regulatory rule model comprises identifying at least one of rule intent patterns, legal registers, and regulatory adjuvants associated with each of the plurality of legal regulations, wherein the rule intent pattern is a syntactic representation of a legal regulation; and deconstructing the plurality of legal regulations based on Minsky's frames comprises representing the plurality of legal regulations in slots provided by Minsky's frames; identifying plurality of rule intents contained in each of the deconstructed plurality of legal regulations, wherein a rule intent is a constraint in each of the plurality of legal regulations; and classifying the plurality of legal regulations into at least one rule act based on the identified rule intents, wherein a rule act is a cluster of frequently co-occurring rule intents in the plurality of legal regulations.

1. A computer implemented method for interpreting legal regulations, the method comprising: receiving a plurality of legal regulations; deconstructing the plurality of legal regulations to form a computer interpretable regulation repository based on at least one of a regulatory rule model and Minsky's frames, wherein deconstructing the plurality of legal regulations based on the regulatory rule model comprises identifying at least one of rule intent patterns, legal registers, and regulatory adjuvants associated with each of the plurality of legal regulations, wherein the rule intent pattern is a syntactic representation of a legal regulation; and deconstructing the plurality of legal regulations based on Minsky's frames comprises representing the plurality of legal regulations in slots provided by Minsky's frames; identifying plurality of rule intents contained in each of the deconstructed plurality of legal regulations, wherein a rule intent is a constraint in each of the plurality of legal regulations; and classifying the plurality of legal regulations into at least one rule act based on the identified rule intents, wherein a rule act is a cluster of frequently co-occurring rule intents in the plurality of legal regulations. 5. The computer implemented method as claimed in claim 1 , wherein the method further comprises: receiving at least one software requirement; deconstructing the at least one software requirement based on at least one of a regulatory rule model and Minsky's frames; identifying rule intents contained in the at least one software requirement based on rule intent patterns contained in at least one software requirement; comparing the rule intents contained in the at least one software requirement with the rule intents contained in the legal regulations within the computer interpretable regulation repository; and tracing at least one legal regulation from amongst the computer interpretable regulation repository to the at least one software requirement based on the comparing.

10. A computer-readable storage device having embedded therein a set of instructions which, when executed by one or more processors of a computer, causes the computer to execute operations comprising: receiving a query log comprising a number of user sessions on an e-commerce site, the sessions comprising a plurality of sets of queries that have been executed by users of the e-commerce site during the user sessions, some of the sets of queries including query transitions, followed by a purchase related event; cleaning and normalizing the query log; generating from the cleaned and normalized query log stationary scores and transition scores of at least some of the plurality of sets, the transition scores comprising ordered query pairs; building a set of query suggestions from the ordered query pairs; computing similarity scores of at least some of the set of query suggestions to determine whether individual ones of the at least some of the set of query suggestions meet a predetermined assurance level; including as elements of the set query suggestions that meet the predetermined assurance level; and mixing and ranking the set of query suggestions in accordance with a user behavior that is to be optimized, the ranking comprising using a first weighting with a popularity score and a second weighting with a purchase efficiency score.

10. A computer-readable storage device having embedded therein a set of instructions which, when executed by one or more processors of a computer, causes the computer to execute operations comprising: receiving a query log comprising a number of user sessions on an e-commerce site, the sessions comprising a plurality of sets of queries that have been executed by users of the e-commerce site during the user sessions, some of the sets of queries including query transitions, followed by a purchase related event; cleaning and normalizing the query log; generating from the cleaned and normalized query log stationary scores and transition scores of at least some of the plurality of sets, the transition scores comprising ordered query pairs; building a set of query suggestions from the ordered query pairs; computing similarity scores of at least some of the set of query suggestions to determine whether individual ones of the at least some of the set of query suggestions meet a predetermined assurance level; including as elements of the set query suggestions that meet the predetermined assurance level; and mixing and ranking the set of query suggestions in accordance with a user behavior that is to be optimized, the ranking comprising using a first weighting with a popularity score and a second weighting with a purchase efficiency score. 14. The computer readable storage device of claim 10 , the operations further including normalizing the scores for the set of query suggestions.

2. The method of claim 1 , wherein the audio file includes at least one message portion and at least one advertisement portion and wherein an audio volume of the at least one message portion is different than an audio volume of the at least one advertisement portion.

2. The method of claim 1 , wherein the audio file includes at least one message portion and at least one advertisement portion and wherein an audio volume of the at least one message portion is different than an audio volume of the at least one advertisement portion. 3. The method of claim 2 , further comprising sending first text data to the second remote device and sending second text data to the second remote device, wherein the first text data is associated with the at least one message portion and the second text data is associated with the at least one advertisement portion.

24. A system comprising: one or more processors; and non-transitory memory storing executable instructions that, when executed by the one or more processors, cause the system to: determine an original receiver operating characteristic (ROC) curve describing performance of the system with respect to an original rate of false acceptance (FA) of the system versus an original rate of correct acceptance (CA) of the system; change an algorithm used by the system for sentence confidence scores, wherein changing the algorithm results in a new ROC curve with respect to a new rate of FA of the system versus a new rate of CA of the system; receive a user specification of relative importance of the new rate of FA versus the new rate of CA; and based on the relative importance of the new rate of FA versus the new rate of CA, adjust a confidence scoring functionality related to recognition reliability for a given input utterance, wherein at or above a given operating point of the system, the new ROC curve reflects a double gain constraint relative to the original ROC curve, such that the new rate of FA is equal to or less than the original rate of FA, and the new rate of CA is equal to or greater than the original rate of CA.

24. A system comprising: one or more processors; and non-transitory memory storing executable instructions that, when executed by the one or more processors, cause the system to: determine an original receiver operating characteristic (ROC) curve describing performance of the system with respect to an original rate of false acceptance (FA) of the system versus an original rate of correct acceptance (CA) of the system; change an algorithm used by the system for sentence confidence scores, wherein changing the algorithm results in a new ROC curve with respect to a new rate of FA of the system versus a new rate of CA of the system; receive a user specification of relative importance of the new rate of FA versus the new rate of CA; and based on the relative importance of the new rate of FA versus the new rate of CA, adjust a confidence scoring functionality related to recognition reliability for a given input utterance, wherein at or above a given operating point of the system, the new ROC curve reflects a double gain constraint relative to the original ROC curve, such that the new rate of FA is equal to or less than the original rate of FA, and the new rate of CA is equal to or greater than the original rate of CA. 25. The system of claim 24 , wherein below the given operating point of the system, the new ROC curve minimizes worsening of the rate of FA and the rate of CA.

18. A non-transitory computer storage medium including at least one program for providing information about a current hotspot event when implemented by a hardware processor associated with a server computer of a search engine in communication with a client computer, said at least one program instructing the hardware processor to: determine a first keyword that reflects one or more features of the current hotspot event; receive, from the client computer, a search term inputted in an input box on a user interface of the search engine displayed on the client computer; determine that the search term is correlated with the first keyword; determine a second keyword matched with the first keyword in a first database, the second keyword reflecting one or more features of an advertisement, the second and the first keywords being matched by including a same person name, a same geographic location, a same product functionality, or a combination thereof; determine current information materials of the advertisement according to the second keyword; generate hotspot information materials according to the current information materials and the first keyword; acquire at least one of a sensitivity screening threshold and an online mode; make the hotspot information materials available online according to the at least one of the sensitivity screening threshold and the online mode; and send the hotspot information materials to the client computer, wherein the client computer displays search results associated with the search term on the user interface, and wherein the search results include the hotspot information materials.

18. A non-transitory computer storage medium including at least one program for providing information about a current hotspot event when implemented by a hardware processor associated with a server computer of a search engine in communication with a client computer, said at least one program instructing the hardware processor to: determine a first keyword that reflects one or more features of the current hotspot event; receive, from the client computer, a search term inputted in an input box on a user interface of the search engine displayed on the client computer; determine that the search term is correlated with the first keyword; determine a second keyword matched with the first keyword in a first database, the second keyword reflecting one or more features of an advertisement, the second and the first keywords being matched by including a same person name, a same geographic location, a same product functionality, or a combination thereof; determine current information materials of the advertisement according to the second keyword; generate hotspot information materials according to the current information materials and the first keyword; acquire at least one of a sensitivity screening threshold and an online mode; make the hotspot information materials available online according to the at least one of the sensitivity screening threshold and the online mode; and send the hotspot information materials to the client computer, wherein the client computer displays search results associated with the search term on the user interface, and wherein the search results include the hotspot information materials. 20. The computer storage medium of claim 18 , wherein said at least one program instructs the hardware processor to: determine whether a sensitivity of the current hotspot event satisfies the sensitivity screening threshold; and make the hotspot information materials available online based upon determining whether the sensitivity of the current hotspot event satisfies the sensitivity screening threshold.

1. A method comprising: processing a web page to determine a plurality of segments, wherein each segment from the plurality of segments includes one or more HTML elements; each machine-based classifier of a plurality of machine-based classifiers generating, based at least upon metadata associated with two or more segments from the plurality of segments that indicates one or more presentation features in the HTML elements of the two or more segments from the plurality of segments, a probability output for each segment of the two or more segments from the plurality of segments, wherein each functional category from the plurality of functional categories corresponds to a functional role of HTML elements in the web page; wherein each machine-based classifier from the plurality of machine-based classifiers corresponds to a functional category from the plurality of functional categories; assigning, based on the plurality of probability output, one or more functional categories to each segment of the two or more segments; a first application selecting a first set of functional categories from the plurality of functional categories; a second application that is different than the first application selecting a second set of functional categories from the plurality of functional categories, wherein the second set of functional categories does not include functional categories from the first set of functional categories; the first application selecting for processing, based upon the first set of functional categories and the functional categories assigned to the two or more segments, a first set of one or more segments from the two or more segments; the second application selecting for processing, based upon the second set of functional categories and the functional categories assigned to the two or more segments, a second set of one or more segments from the two or more segments, wherein the second set of one or more segments includes at least one segment that is not in the first set of one or more segments and the first set of one or more segments includes at least one segment that is not in the second set of one or more segments; the first application processing content contained in the first set of one or more segments and not processing content contained in the second set of one or more segments; the second application processing content contained in the second set of one or more segments and not processing content contained in the first set of one or more segments; and wherein the method is performed by one or more computing devices.

1. A method comprising: processing a web page to determine a plurality of segments, wherein each segment from the plurality of segments includes one or more HTML elements; each machine-based classifier of a plurality of machine-based classifiers generating, based at least upon metadata associated with two or more segments from the plurality of segments that indicates one or more presentation features in the HTML elements of the two or more segments from the plurality of segments, a probability output for each segment of the two or more segments from the plurality of segments, wherein each functional category from the plurality of functional categories corresponds to a functional role of HTML elements in the web page; wherein each machine-based classifier from the plurality of machine-based classifiers corresponds to a functional category from the plurality of functional categories; assigning, based on the plurality of probability output, one or more functional categories to each segment of the two or more segments; a first application selecting a first set of functional categories from the plurality of functional categories; a second application that is different than the first application selecting a second set of functional categories from the plurality of functional categories, wherein the second set of functional categories does not include functional categories from the first set of functional categories; the first application selecting for processing, based upon the first set of functional categories and the functional categories assigned to the two or more segments, a first set of one or more segments from the two or more segments; the second application selecting for processing, based upon the second set of functional categories and the functional categories assigned to the two or more segments, a second set of one or more segments from the two or more segments, wherein the second set of one or more segments includes at least one segment that is not in the first set of one or more segments and the first set of one or more segments includes at least one segment that is not in the second set of one or more segments; the first application processing content contained in the first set of one or more segments and not processing content contained in the second set of one or more segments; the second application processing content contained in the second set of one or more segments and not processing content contained in the first set of one or more segments; and wherein the method is performed by one or more computing devices. 5. The method of claim 1 , wherein the plurality of functional categories includes at least one of: a) user-generated content; b) site navigation; or c) boiler-plate.

1. A method of communicating with a software application, the method comprising: exposing internal data of the software application responsive to code injection of computer readable program code comprising a scripting engine into a runtime environment associated with the software application, wherein the code injection allows execution of code based on a different programming language than the software application and the scripting engine comprises an interpreter written in a same programming language as the software application; and loading computer readable program code comprising a script in accordance with the internal data that was exposed, wherein the script is based on a different programming language than the software application and is interpretable by the scripting engine for execution with existing code of the software application to alter operation thereof, wherein the exposing and the loading comprise operations performed by at least one processor.

1. A method of communicating with a software application, the method comprising: exposing internal data of the software application responsive to code injection of computer readable program code comprising a scripting engine into a runtime environment associated with the software application, wherein the code injection allows execution of code based on a different programming language than the software application and the scripting engine comprises an interpreter written in a same programming language as the software application; and loading computer readable program code comprising a script in accordance with the internal data that was exposed, wherein the script is based on a different programming language than the software application and is interpretable by the scripting engine for execution with existing code of the software application to alter operation thereof, wherein the exposing and the loading comprise operations performed by at least one processor. 10. The method of claim 1 , wherein the software application is hosted by a virtual machine, and wherein the execution comprises conversion of the scripting engine to machine-readable program code for execution by the virtual machine in a context of the existing code.

3. The system as recited in claim 1 , wherein the natural language processor module is configured to use proximity based statistical analysis in identifying the keyword results to be provided to the context search engine.

3. The system as recited in claim 1 , wherein the natural language processor module is configured to use proximity based statistical analysis in identifying the keyword results to be provided to the context search engine. 4. The system as recited in claim 3 , wherein the natural language processor module is configured to perform the proximity based statistical analysis on a portion of text within the media experience, wherein the portion of text is within a specified proximity of the search term.

3. A system, comprising: at least one computing device; and at least one application executable in the at least one computing device, wherein, when executed, the at least one application causes the at least one computing device to at least: obtain a plurality of customer review search queries from a plurality of users, the plurality of customer review search queries being obtained to search a collection of customer reviews for a specific item; extract a set of relevant topics for the specific item by analyzing the plurality of customer review search queries; and generate a user interface based at least in part on at least some of the set of relevant topics for the specific item.

3. A system, comprising: at least one computing device; and at least one application executable in the at least one computing device, wherein, when executed, the at least one application causes the at least one computing device to at least: obtain a plurality of customer review search queries from a plurality of users, the plurality of customer review search queries being obtained to search a collection of customer reviews for a specific item; extract a set of relevant topics for the specific item by analyzing the plurality of customer review search queries; and generate a user interface based at least in part on at least some of the set of relevant topics for the specific item. 14. The system of claim 3 , wherein extracting the set of relevant topics further comprises selecting the set of relevant topics by analyzing the plurality of customer review search queries and a plurality of additional customer review search queries, the plurality of additional customer review search queries being obtained to search another collection of customer reviews for at least one item that is similar to the specific item.

11. The handheld electronic device of claim 10 , wherein said predetermined characteristic includes said first input being associated with a consonant.

11. The handheld electronic device of claim 10 , wherein said predetermined characteristic includes said first input being associated with a consonant. 12. The handheld electronic device of claim 11 , wherein said instructions, when executed by the processor, further cause the processor to perform operations comprising: determining that said consonant is a consonant from among a predetermined group of consonants.

11. A non-transitory computer readable recording medium, storing a plurality of program codes, wherein when the program codes are loaded into a processor, the processor executes the program codes to accomplish following steps: transforming a first input voice from a voice receiver into an input sentence according to a grammar rule; determining whether the input sentence is the same as a learning sentence displayed on a display; and when the input sentence is different from the learning sentence, generating an ancillary information comprising at least one error word in the input sentence that is different from the learning sentence, wherein the step of transforming the first input voice from the voice receiver into the input sentence according to the grammar rule comprises: obtaining, based on the first input voice, a first phoneme sequence, wherein the first phoneme sequence represents a pronunciation of the entire first input voice; and obtaining, based on the entire first phoneme sequence, the input sentence according to the grammar rule, wherein the step of determining whether the input sentence is the same as the learning sentence displayed on the display comprises: obtaining, based on the input sentence, a second phoneme sequence, wherein the second phoneme sequence represents a pronunciation of the entire input sentence; determining whether the second phoneme sequence is the same as a standard phoneme sequence corresponding to the learning sentence; and determining whether the input sentence is different from the learning sentence when the second phoneme sequence is different from the standard phoneme sequence, wherein the step of generating the ancillary information comprising the at least one error word in the input sentence that is different from the learning sentence comprises: dividing the input sentence into at least one phrase according to a grammar format of the learning sentence; and generating the ancillary information in unit of the at least one phrase, wherein the ancillary information comprises the at least one error word and at least one phoneme of a standard phoneme sequence corresponding to the learning sentence, wherein the at least one phoneme is pronounced incorrectly and corresponded to the at least one error word, wherein the learning sentence comprises a plurality of words, and the standard phoneme sequence compliant with a grammatical structure of the learning sentence is determined according to positions of the plurality of words in the learning sentence, wherein the ancillary information further comprises a suggestion indicating to practice a pronunciation of at least two adjacent words of a specific phrase which conforms to a specific phrase structure recorded in a grammar database, wherein the at least two adjacent words comprises one of the at least one error word.

11. A non-transitory computer readable recording medium, storing a plurality of program codes, wherein when the program codes are loaded into a processor, the processor executes the program codes to accomplish following steps: transforming a first input voice from a voice receiver into an input sentence according to a grammar rule; determining whether the input sentence is the same as a learning sentence displayed on a display; and when the input sentence is different from the learning sentence, generating an ancillary information comprising at least one error word in the input sentence that is different from the learning sentence, wherein the step of transforming the first input voice from the voice receiver into the input sentence according to the grammar rule comprises: obtaining, based on the first input voice, a first phoneme sequence, wherein the first phoneme sequence represents a pronunciation of the entire first input voice; and obtaining, based on the entire first phoneme sequence, the input sentence according to the grammar rule, wherein the step of determining whether the input sentence is the same as the learning sentence displayed on the display comprises: obtaining, based on the input sentence, a second phoneme sequence, wherein the second phoneme sequence represents a pronunciation of the entire input sentence; determining whether the second phoneme sequence is the same as a standard phoneme sequence corresponding to the learning sentence; and determining whether the input sentence is different from the learning sentence when the second phoneme sequence is different from the standard phoneme sequence, wherein the step of generating the ancillary information comprising the at least one error word in the input sentence that is different from the learning sentence comprises: dividing the input sentence into at least one phrase according to a grammar format of the learning sentence; and generating the ancillary information in unit of the at least one phrase, wherein the ancillary information comprises the at least one error word and at least one phoneme of a standard phoneme sequence corresponding to the learning sentence, wherein the at least one phoneme is pronounced incorrectly and corresponded to the at least one error word, wherein the learning sentence comprises a plurality of words, and the standard phoneme sequence compliant with a grammatical structure of the learning sentence is determined according to positions of the plurality of words in the learning sentence, wherein the ancillary information further comprises a suggestion indicating to practice a pronunciation of at least two adjacent words of a specific phrase which conforms to a specific phrase structure recorded in a grammar database, wherein the at least two adjacent words comprises one of the at least one error word. 14. The computer readable recording medium according to claim 11 , wherein the step of determining whether the input sentence is the same as the learning sentence displayed on the display comprises: comparing the input sentence with the learning sentence by using a DTW algorithm; obtaining an identity information between the input sentence and the learning sentence according to a comparison result of the DTW algorithm; aligning at least one correct word in the input sentence which is the same as the learning sentence with at least one standard word in the learning sentence which is corresponding to the at least one correct word according to the identity information; and when the input sentence is not completely aligned with the learning sentence, determining that the input sentence is different from the learning sentence.

20. A non-transitory computer readable storage medium including computer readable program code stored thereon, wherein the computer readable program code, when executed by an electronic device having one or more processors, causes the electronic device to perform operations comprising: receiving, on an electronic device, a selection of a first filter to apply to an image; receiving input from one or more sensors integrated with the electronic device; determining a value of a first input parameter for the first filter from the received input; and applying the first filter to the image to generate a first filtered image, the first input parameter having the determined value.

20. A non-transitory computer readable storage medium including computer readable program code stored thereon, wherein the computer readable program code, when executed by an electronic device having one or more processors, causes the electronic device to perform operations comprising: receiving, on an electronic device, a selection of a first filter to apply to an image; receiving input from one or more sensors integrated with the electronic device; determining a value of a first input parameter for the first filter from the received input; and applying the first filter to the image to generate a first filtered image, the first input parameter having the determined value. 21. The computer readable storage medium of claim 20 , wherein the operations further comprise: storing the image within a machine-readable storage device communicatively coupled to the electronic device: and storing, separate from the image, metadata including the first filter and the first input parameter used to generate the first filtered image.

15. A system comprising: one or more computer memories collectively storing content items and instructions configured to cause one or more processors to control performance of a method comprising: associating subsets of content items with corresponding strings of one or more overloaded keys of a keypad so that the subsets of content items are directly mapped to the corresponding strings of one or more overloaded keys by a direct mapping, wherein at least one overloaded key of the one or more overloaded keys is associated with a plurality of alphabetical and/or numerical symbols; ranking content items within at least one of the subsets of content items according to one or more ordering criteria; subsequent to the associating and ranking, receiving entry of a first overloaded key, selecting and presenting a first of the subsets of content items that is associated with the first overloaded key based on the direct mapping, subsequent to receiving entry of the first overloaded key, receiving entry of a second overloaded key the same as or different than the first overloaded key, the second overloaded key forming a string with the first overloaded key, and selecting and presenting a second of the subsets of content items that is associated with the string of overloaded keys formed by the first overloaded key and the second overloaded key based on the direct mapping.

15. A system comprising: one or more computer memories collectively storing content items and instructions configured to cause one or more processors to control performance of a method comprising: associating subsets of content items with corresponding strings of one or more overloaded keys of a keypad so that the subsets of content items are directly mapped to the corresponding strings of one or more overloaded keys by a direct mapping, wherein at least one overloaded key of the one or more overloaded keys is associated with a plurality of alphabetical and/or numerical symbols; ranking content items within at least one of the subsets of content items according to one or more ordering criteria; subsequent to the associating and ranking, receiving entry of a first overloaded key, selecting and presenting a first of the subsets of content items that is associated with the first overloaded key based on the direct mapping, subsequent to receiving entry of the first overloaded key, receiving entry of a second overloaded key the same as or different than the first overloaded key, the second overloaded key forming a string with the first overloaded key, and selecting and presenting a second of the subsets of content items that is associated with the string of overloaded keys formed by the first overloaded key and the second overloaded key based on the direct mapping. 28. The system of claim 15 , wherein the method further comprises updating the subsets of content items associated with the corresponding strings of one or more unresolved keystrokes based on the one or more ordering criteria.

13. A system comprising: one or more computers and one or more storage devices storing instructions that are operable, when executed by the one or more computers, to cause the one or more computers to perform operations comprising: receiving in a processor in the system, the processor having a first execution mode and a different second execution mode, a program that has an initial machine language instruction for execution by the processor in the first execution mode, wherein the initial machine language instruction, when executed by the processor in the first execution mode, performs a first operation; determining, by an instruction selector of a plugin configured to execute the program on the processor, that a portion of the initial machine language instruction, when the portion is interpreted by the processor as an instruction in the second execution mode, causes the processor to perform a second operation that is different from the first operation and that satisfies one or more risk criteria; in response, generating, by the instruction selector of the plugin, one or more alternative machine language instructions to replace the initial machine language instruction for execution by the processor in the first execution mode, wherein the one or more alternative machine language instructions, when interpreted by the processor as instructions in the first execution mode, cause the processor to perform a third operation that is similar to the first operation, and wherein the one or more alternative machine language instructions, when interpreted by the processor as one or more instructions in the different second execution mode, cause the processor to perform a fourth operation that is different from the second operation and that does not satisfy the one or more risk criteria of the second operation being performed by the processor when the portion of the initial machine language instruction is interpreted as an instruction in the second execution mode; and replacing, by the instruction selector of the plugin, the initial machine language instruction with the one or more alternative machine language instructions in the program.

13. A system comprising: one or more computers and one or more storage devices storing instructions that are operable, when executed by the one or more computers, to cause the one or more computers to perform operations comprising: receiving in a processor in the system, the processor having a first execution mode and a different second execution mode, a program that has an initial machine language instruction for execution by the processor in the first execution mode, wherein the initial machine language instruction, when executed by the processor in the first execution mode, performs a first operation; determining, by an instruction selector of a plugin configured to execute the program on the processor, that a portion of the initial machine language instruction, when the portion is interpreted by the processor as an instruction in the second execution mode, causes the processor to perform a second operation that is different from the first operation and that satisfies one or more risk criteria; in response, generating, by the instruction selector of the plugin, one or more alternative machine language instructions to replace the initial machine language instruction for execution by the processor in the first execution mode, wherein the one or more alternative machine language instructions, when interpreted by the processor as instructions in the first execution mode, cause the processor to perform a third operation that is similar to the first operation, and wherein the one or more alternative machine language instructions, when interpreted by the processor as one or more instructions in the different second execution mode, cause the processor to perform a fourth operation that is different from the second operation and that does not satisfy the one or more risk criteria of the second operation being performed by the processor when the portion of the initial machine language instruction is interpreted as an instruction in the second execution mode; and replacing, by the instruction selector of the plugin, the initial machine language instruction with the one or more alternative machine language instructions in the program. 26. The system of claim 13 , wherein receiving the initial machine language instruction for execution by the processor in the first execution mode comprises receiving the initial machine language instruction by an instruction selector of a web browser plugin having a native code module, and wherein the operations further comprise providing, by the instruction selector to the native code module, the program having the one or more alternative machine language instructions to be executed natively by the native code module.

1. A method for determining a semantic concept classification for a digital video clip including a temporal sequence of video frames and a corresponding audio soundtrack, comprising: determining, by a processing device, reference video codeword similarity scores for each reference video clip in a set of reference video clips, wherein the determining reference video codeword similarity scores comprises: analyzing a temporal sequence of video frames for a particular reference video clip to determine a set of reference video visual features; analyzing an audio soundtrack for the particular reference video clip to determine a set of reference video audio features; comparing the set of reference video visual features to distinct visual background codewords and distinct visual foreground codewords from audio-visual grouplets of an audio-visual dictionary, wherein the audio-visual grouplets include distinct visual background codewords representing visual background content, distinct visual foreground codewords representing visual foreground content, distinct audio background codewords representing audio background content, and distinct audio foreground codewords representing audio foreground content; and comparing the set of reference video audio features to the distinct audio background codewords and the distinct audio foreground codewords from the audio-visual grouplets of the audio-visual dictionary; determining, by the prosessing device, codeword similarity scores for the digital video clip, wherein the determining codeword similarity scores comprises: analyzing the temporal sequence of video frames in the digital video clip to determine a set of visual features; analyzing the audio soundtrack in the digital video clip to determine a set of audio features; comparing the set of visual features to the distinct visual background codewords and the distinct visual foreground codewords; and comparing the set of audio features to the distinct audio background codewords and the distinct audio foreground codewords; determining, by the prosessing device, a reference video similarity score for each reference video clip representing a similarity between the digital video clip and the respective reference video clip responsive to the audio-visual grouplets, the codeword similarity scores, and the reference video codeword similarity scores; determining, by the prosessing device, a concept classification using trained semantic classifiers responsive to the determined reference video similarity scores; storing, by the prosessing device, an indication of the concept classification in a processor-accessible memory; wherein the distinct visual background codewords and the distinct foreground codewords are separate and distinct from each other, wherein the distinct audio background codewords and the distinct audio foreground codewords are separate and distinct from each other, and wherein the distinct visual background codewords and the distinct visual foreground codewords are separate and distinct from the distinct audio background codewords and the distinct audio foreground codewords.

1. A method for determining a semantic concept classification for a digital video clip including a temporal sequence of video frames and a corresponding audio soundtrack, comprising: determining, by a processing device, reference video codeword similarity scores for each reference video clip in a set of reference video clips, wherein the determining reference video codeword similarity scores comprises: analyzing a temporal sequence of video frames for a particular reference video clip to determine a set of reference video visual features; analyzing an audio soundtrack for the particular reference video clip to determine a set of reference video audio features; comparing the set of reference video visual features to distinct visual background codewords and distinct visual foreground codewords from audio-visual grouplets of an audio-visual dictionary, wherein the audio-visual grouplets include distinct visual background codewords representing visual background content, distinct visual foreground codewords representing visual foreground content, distinct audio background codewords representing audio background content, and distinct audio foreground codewords representing audio foreground content; and comparing the set of reference video audio features to the distinct audio background codewords and the distinct audio foreground codewords from the audio-visual grouplets of the audio-visual dictionary; determining, by the prosessing device, codeword similarity scores for the digital video clip, wherein the determining codeword similarity scores comprises: analyzing the temporal sequence of video frames in the digital video clip to determine a set of visual features; analyzing the audio soundtrack in the digital video clip to determine a set of audio features; comparing the set of visual features to the distinct visual background codewords and the distinct visual foreground codewords; and comparing the set of audio features to the distinct audio background codewords and the distinct audio foreground codewords; determining, by the prosessing device, a reference video similarity score for each reference video clip representing a similarity between the digital video clip and the respective reference video clip responsive to the audio-visual grouplets, the codeword similarity scores, and the reference video codeword similarity scores; determining, by the prosessing device, a concept classification using trained semantic classifiers responsive to the determined reference video similarity scores; storing, by the prosessing device, an indication of the concept classification in a processor-accessible memory; wherein the distinct visual background codewords and the distinct foreground codewords are separate and distinct from each other, wherein the distinct audio background codewords and the distinct audio foreground codewords are separate and distinct from each other, and wherein the distinct visual background codewords and the distinct visual foreground codewords are separate and distinct from the distinct audio background codewords and the distinct audio foreground codewords. 5. The method of claim 1 , wherein the set of audio features comprises Mel-Frequency Cepstral Coefficients features and transient features.

2. The method of claim 1 , wherein the background environment classification comprises one of office, airport, street, vehicle, train and home.

2. The method of claim 1 , wherein the background environment classification comprises one of office, airport, street, vehicle, train and home. 3. The method of claim 2 , wherein the background environment is classified based on two levels comprising a first level from the listing of background environments and a second, finer, level based on specific geographic location.

14. The method according to claim 1 , wherein a structure in the structure dictionary takes a form of a word built using an alphabet of symbols taking a form of constants corresponding to at least one of alpha-numeric strings and characters, and variables corresponding to at least one generic data type, the word being built using formation rules of a formal language, the method of building the structure sub-collection of values matching said word comprising: storing portions of values corresponding to the same symbol in the same symbol sub-collection, the values matching the word, the portions matching the symbol while parsing the values along the structure of the word; and storing the structure sub-collection in a form of the symbol sub-collections corresponding to all particular symbols from which the word is formed using the formation rules.

14. The method according to claim 1 , wherein a structure in the structure dictionary takes a form of a word built using an alphabet of symbols taking a form of constants corresponding to at least one of alpha-numeric strings and characters, and variables corresponding to at least one generic data type, the word being built using formation rules of a formal language, the method of building the structure sub-collection of values matching said word comprising: storing portions of values corresponding to the same symbol in the same symbol sub-collection, the values matching the word, the portions matching the symbol while parsing the values along the structure of the word; and storing the structure sub-collection in a form of the symbol sub-collections corresponding to all particular symbols from which the word is formed using the formation rules. 16. The method of claim 14 , wherein the language is a recursively enumerable language.

2. The method of claim 1 , further comprising determining that the cost of creating the hash index and probing the hash index N times is less than fully scanning the base column N times, creating the hash index, and using the hash index during query execution to apply the correlated predicate.

2. The method of claim 1 , further comprising determining that the cost of creating the hash index and probing the hash index N times is less than fully scanning the base column N times, creating the hash index, and using the hash index during query execution to apply the correlated predicate. 5. The method of claim 2 , wherein the second database query is of a same snapshot of the database, the second database query having a correlated predicate with an operator being one of equal and not equal to the base column.

10. A computer-implemented method for generating a self-check test program comprising: selecting a selected instruction from a plurality of instructions in an instruction set for executing on a target device that is an intended device-under-test (DUT); determining when necessary blocks in the target device are available as fuzzy models in a software DUT model that emulates operation of the target device; skipping and not executing the selected instruction on the software DUT model and not adding the selected instruction to the self-check test program when the necessary blocks are determined to be unavailable as fuzzy models in the software DUT model; when the necessary blocks are determined to be available as fuzzy models in the software DUT model, determining when a known state is required for the selected instruction; skipping and not executing the selected instruction on the software DUT model and not adding the selected instruction to the self-check test program when the known state is required but not available on the software DUT model; executing the selected instruction on the software DUT model to generate an updated state and adding the selected instruction to the self-check test program when the known state is required and available on the software DUT model or when the known state is not required; and continuing to select and process other instructions from the plurality of instructions in the instruction set as the selected instruction to generate the self-check test program, whereby the self-check test program is generated by selecting instructions that have fuzzy models available on the software DUT model.

10. A computer-implemented method for generating a self-check test program comprising: selecting a selected instruction from a plurality of instructions in an instruction set for executing on a target device that is an intended device-under-test (DUT); determining when necessary blocks in the target device are available as fuzzy models in a software DUT model that emulates operation of the target device; skipping and not executing the selected instruction on the software DUT model and not adding the selected instruction to the self-check test program when the necessary blocks are determined to be unavailable as fuzzy models in the software DUT model; when the necessary blocks are determined to be available as fuzzy models in the software DUT model, determining when a known state is required for the selected instruction; skipping and not executing the selected instruction on the software DUT model and not adding the selected instruction to the self-check test program when the known state is required but not available on the software DUT model; executing the selected instruction on the software DUT model to generate an updated state and adding the selected instruction to the self-check test program when the known state is required and available on the software DUT model or when the known state is not required; and continuing to select and process other instructions from the plurality of instructions in the instruction set as the selected instruction to generate the self-check test program, whereby the self-check test program is generated by selecting instructions that have fuzzy models available on the software DUT model. 15. The computer-implemented method of claim 10 wherein executing the selected instruction on the software DUT model comprises: reading a current state of state storage in the software DUT model; applying the current state of state storage as model inputs to the fuzzy models; performing logic operations on the model inputs to generate model outputs of the fuzzy models, wherein the logic operations are determined by the fuzzy models; and updating the state storage in the software DUT model in response to the model outputs to generate a next state of the state storage.

1. A method of adapting a speech system, comprising: logging speech data from the speech system; processing the speech data, by a plurality of characteristic detector modules, to detect a plurality of user characteristics from the speech data, the plurality of characteristic detector modules each map the speech data into at least one category associated with at least one user characteristic of the plurality of user characteristics, the user characteristics comprise characteristics that are specific to behavior of a user of a vehicle when saying a command to an automated system; tracking a frequency of each of the plurality of user characteristics; and when the frequency of at least one of the plurality of user characteristics reaches a certain frequency, selecting a language model associated with the user of the vehicle, and updating the language model based on the categories of the plurality of the user characteristics.

1. A method of adapting a speech system, comprising: logging speech data from the speech system; processing the speech data, by a plurality of characteristic detector modules, to detect a plurality of user characteristics from the speech data, the plurality of characteristic detector modules each map the speech data into at least one category associated with at least one user characteristic of the plurality of user characteristics, the user characteristics comprise characteristics that are specific to behavior of a user of a vehicle when saying a command to an automated system; tracking a frequency of each of the plurality of user characteristics; and when the frequency of at least one of the plurality of user characteristics reaches a certain frequency, selecting a language model associated with the user of the vehicle, and updating the language model based on the categories of the plurality of the user characteristics. 5. The method of claim 1 wherein the user characteristics comprise at least one of a verbosity detected by a percentage of decoration or non-functional words, an information distribution detected by a delivery method of information, and a domain distribution detected by a domain historic behavior.

15. Weighing apparatus comprising in combination: (a) first conveying means, (b) first control means for controlling the operation of said first conveying means, (c) second conveying means, (d) weighing means disposed between said first and said second conveying means and operable to receive and sense the weight of articles conveyed by said first conveying means, (e) a microphone accessible to a person controlling a weighing operation by said weighing means, (f) first computing means operable to receive speech signals of selected words of speech spoken into said microphone and to generate first command control signals in accordance with the words of speech spoken into said microphone by electrically processing and analyzing the speech signals output by said microphone, (g) means for selectively applying said control signals to said first control means to selectively control the operation of said first conveying means to permit an operator of said apparatus to properly control the conveyance of articles by said first conveying means to said weighing means and to permit said weighing means to properly weigh articles delivered by said first conveying means, and (h) means for transferring articles after they have been weighed to said second conveying means to permit said second conveying means to convey said articles away from said weighing means.

15. Weighing apparatus comprising in combination: (a) first conveying means, (b) first control means for controlling the operation of said first conveying means, (c) second conveying means, (d) weighing means disposed between said first and said second conveying means and operable to receive and sense the weight of articles conveyed by said first conveying means, (e) a microphone accessible to a person controlling a weighing operation by said weighing means, (f) first computing means operable to receive speech signals of selected words of speech spoken into said microphone and to generate first command control signals in accordance with the words of speech spoken into said microphone by electrically processing and analyzing the speech signals output by said microphone, (g) means for selectively applying said control signals to said first control means to selectively control the operation of said first conveying means to permit an operator of said apparatus to properly control the conveyance of articles by said first conveying means to said weighing means and to permit said weighing means to properly weigh articles delivered by said first conveying means, and (h) means for transferring articles after they have been weighed to said second conveying means to permit said second conveying means to convey said articles away from said weighing means. 18. Weighing apparatus in accordance with claim 15 wherein said first conveying means includes an article manipulator operable to engage, pick up and transfer an article to said weighing means and to transfer articles from said weighing means to said second conveying means.

1. A distributed database system for a communication network having a plurality of nodes, each of which nodes includes a distributed database, said distributed database system comprising: local process means for extracting relations from each database of said communication network by performing local processes at each of said nodes of said communication system when a query including multi-attribute relations is input from one of said nodes; degree setting means for setting a degree number of each of said extracted relations from said local process means based on (A) tuple numbers of single-attribute relations derived from said multi-attribute relations and (B) a tuple number of each of said multi-attribute relations; relation set means for arranging a plurality of relation sets, each of which relation sets contains relations having the same degree number in ascending order by grouping said extracted relations from said local process means according to the degree number set by said degree setting means, wherein said relation sets include a first relation set containing relations with the lowest degree number; extraction means for extracting single-attribute relations from each of said relation sets arranged by said relation set means so that said single-attribute relations are added to said first relation set; semijoin operating means for repeatedly semijoining two relations of a relation set when a quantity of transfer data after said semijoining is detected to be smaller than a quantity of transfer data before said semijoining, and for adding derived relations resulting from said semijoining to a following relation set among the plurality of relation sets arranged by said relation set means; and control means or allowing said semijoin operating means to sequentially perform said semijoining and said adding for all of the plurality of relation sets arranged by said relation set means, starting from said first relation set and ending at a relation set having the highest degree number, so that each derived relation resulting from said semijoining is added to the transfer data.

1. A distributed database system for a communication network having a plurality of nodes, each of which nodes includes a distributed database, said distributed database system comprising: local process means for extracting relations from each database of said communication network by performing local processes at each of said nodes of said communication system when a query including multi-attribute relations is input from one of said nodes; degree setting means for setting a degree number of each of said extracted relations from said local process means based on (A) tuple numbers of single-attribute relations derived from said multi-attribute relations and (B) a tuple number of each of said multi-attribute relations; relation set means for arranging a plurality of relation sets, each of which relation sets contains relations having the same degree number in ascending order by grouping said extracted relations from said local process means according to the degree number set by said degree setting means, wherein said relation sets include a first relation set containing relations with the lowest degree number; extraction means for extracting single-attribute relations from each of said relation sets arranged by said relation set means so that said single-attribute relations are added to said first relation set; semijoin operating means for repeatedly semijoining two relations of a relation set when a quantity of transfer data after said semijoining is detected to be smaller than a quantity of transfer data before said semijoining, and for adding derived relations resulting from said semijoining to a following relation set among the plurality of relation sets arranged by said relation set means; and control means or allowing said semijoin operating means to sequentially perform said semijoining and said adding for all of the plurality of relation sets arranged by said relation set means, starting from said first relation set and ending at a relation set having the highest degree number, so that each derived relation resulting from said semijoining is added to the transfer data. 5. A distributed database system according to claim 1, wherein said semijoin operating means includes: first means for detecting whether or not a derived relation resulting from said semijoining by said semijoin operating means is a high-level relation, the high-level relation having a degree number greater than half of the maximum number of attributes included in the multi-attribute relations of the query.

4. The computer-implemented method of claim 1 , wherein the plurality of keywords are visually distinguished by color, size, highlighting, font, or other visual indication.

4. The computer-implemented method of claim 1 , wherein the plurality of keywords are visually distinguished by color, size, highlighting, font, or other visual indication. 13. One or more non-transitory storage media storing instructions which, when executed by one or more processors cause performance of the method recited in claim 4 .

1. A processor, comprising: one or more processing elements, including at least a first processing element that includes one or more VA-indexed structures indexed by virtual addresses and that is configured to handle invalidation messages, the handling including: in response to determining that a first invalidation message applies to a subset of virtual addresses consisting of fewer than all virtual addresses associated with a first set of one or more translation context values, searching the VA-indexed structures in the first processing element to find any entries that correspond to a virtual address in the subset and invalidating any entries that are found; in response to determining that a second invalidation message applies to all virtual addresses associated with a second set of one or more translation context values and that no entry exists in one or more invalidation-tracking structures corresponding to the second set of one or more translation context values, bypassing searching of any of the VA-indexed structures in the first processing element to find any entries that correspond to a virtual address associated with the second set of one or more translation context values, where the one or more invalidation-tracking structures track invalidation of different sets of one or more translation context values; and in response to determining that a third invalidation message applies to all virtual addresses associated with a third set of one or more translation context values and that at least one entry exists in the one or more invalidation-tracking structures corresponding to the third set of one or more translation context values, storing invalidation information in the one or more invalidation-tracking structures to invalidate the third set of one or more translation context values and delaying searching of any of the VA-indexed structures in the first processing element to find any entries that correspond to a virtual address associated with the third set of one or more translation context values.

1. A processor, comprising: one or more processing elements, including at least a first processing element that includes one or more VA-indexed structures indexed by virtual addresses and that is configured to handle invalidation messages, the handling including: in response to determining that a first invalidation message applies to a subset of virtual addresses consisting of fewer than all virtual addresses associated with a first set of one or more translation context values, searching the VA-indexed structures in the first processing element to find any entries that correspond to a virtual address in the subset and invalidating any entries that are found; in response to determining that a second invalidation message applies to all virtual addresses associated with a second set of one or more translation context values and that no entry exists in one or more invalidation-tracking structures corresponding to the second set of one or more translation context values, bypassing searching of any of the VA-indexed structures in the first processing element to find any entries that correspond to a virtual address associated with the second set of one or more translation context values, where the one or more invalidation-tracking structures track invalidation of different sets of one or more translation context values; and in response to determining that a third invalidation message applies to all virtual addresses associated with a third set of one or more translation context values and that at least one entry exists in the one or more invalidation-tracking structures corresponding to the third set of one or more translation context values, storing invalidation information in the one or more invalidation-tracking structures to invalidate the third set of one or more translation context values and delaying searching of any of the VA-indexed structures in the first processing element to find any entries that correspond to a virtual address associated with the third set of one or more translation context values. 2. The processor of claim 1 , wherein the one or more invalidation-tracking structures includes entries that map a plurality of sets of one or more translation context values to corresponding translation context identifiers, where a total number of bits used to represent all possible translation context identifiers is smaller than a total number of bits used to represent all possible sets of one or more translation context values.

2. The method according to claim 1 , wherein searching in the set of characters one or more characters having highest similarities of shape to the first character comprises: searching in the set of characters one or more characters having highest similarities of shape to each image sample of a plurality of image samples of the first character, as a candidate character set of the each image sample, so as to form a plurality of candidate character sets each of which corresponds to one of the image samples; calculating a frequency of each character occurring in the plurality of candidate character sets; and selecting in the plurality of candidate character sets one or more characters corresponding to highest frequencies, to form the similar character list of the first character.

2. The method according to claim 1 , wherein searching in the set of characters one or more characters having highest similarities of shape to the first character comprises: searching in the set of characters one or more characters having highest similarities of shape to each image sample of a plurality of image samples of the first character, as a candidate character set of the each image sample, so as to form a plurality of candidate character sets each of which corresponds to one of the image samples; calculating a frequency of each character occurring in the plurality of candidate character sets; and selecting in the plurality of candidate character sets one or more characters corresponding to highest frequencies, to form the similar character list of the first character. 4. The method according to claim 2 , wherein selecting in the candidate character sets one or more characters corresponding to highest frequencies comprises: selecting in the plurality of candidate character sets one or more characters corresponding to frequencies larger than a first threshold, to form the similar character list of the first character.

1. A system comprising: one or more input devices; a display; one or more computer processing modules; and one or more non-transitory storage modules storing computing instructions configured to run on the one or more computer processing modules and perform acts of: receiving, from a third-party electronic device at the one or more computer processing modules, a title for a product; dividing, at the one or more computer processing modules, the title into a sequence of tokens; storing, by the one or more computer processing modules onto the one or more non-transitory storage modules, the sequence of tokens; determining, at the one or more computer processing modules, a type of each token of the sequence of tokens using machine learning algorithms comprising a sequence labeling model, wherein the sequence labeling model comprises a set of feature functions, each of the set of feature functions comprising: f ⁡ ( x , y , i ) = { 1 ⁢ ⁢ if ⁢ ⁢ x i = the ⁢ ⁢ and ⁢ ⁢ y i = DT 0 ⁢ ⁢ otherwise wherein each x consists of a different token of the sequence of tokens, each y consists of a first type of label for each token of the sequence of tokens, and DT is a first label assigned to a determiner; storing, by the one or more computer processing modules onto the one or more non-transitory storage modules, the type of each token of the sequence of tokens; encoding, at the one or more computer processing modules, each token of the sequence of tokens to indicate a second type of label for each token of the sequence of tokens, wherein the second type of label for each token of the sequence of tokens is based on the type of each token of the sequence of tokens and is chosen from a BIO encoding scheme, wherein a label B of the BIO encoding scheme indicates a first token of a brand name, a label I of the BIO encoding scheme indicates a subsequent token of the brand name, and a label O of the BIO encoding scheme indicates a token that is not part of the brand name; storing, by the one or more computer processing modules onto the one or more non-transitory storage modules, the second type of label for each token of the sequence of tokens; determining, at the one or more computer processing modules, an attribute for each token of the sequence of tokens, the attribute comprising the brand name from each token of the sequence of tokens using a label for each token of the sequence of tokens; storing, by the one or more computer processing modules onto the one or more non-transitory storage modules, the attribute for each token of the sequence of tokens; normalizing, at the one or more computer processing modules, the attribute for each token of the sequence of tokens to create standardized representations of the attribute for each token of the sequence of tokens; writing, by the one or more computer processing modules onto the one or more non-transitory storage modules, the attribute for each token of the sequence of tokens to empty database entries associated with the product; and facilitating a representation of the attribute for each token of the sequence of tokens on a user display in response to a search request from a user.

1. A system comprising: one or more input devices; a display; one or more computer processing modules; and one or more non-transitory storage modules storing computing instructions configured to run on the one or more computer processing modules and perform acts of: receiving, from a third-party electronic device at the one or more computer processing modules, a title for a product; dividing, at the one or more computer processing modules, the title into a sequence of tokens; storing, by the one or more computer processing modules onto the one or more non-transitory storage modules, the sequence of tokens; determining, at the one or more computer processing modules, a type of each token of the sequence of tokens using machine learning algorithms comprising a sequence labeling model, wherein the sequence labeling model comprises a set of feature functions, each of the set of feature functions comprising: f ⁡ ( x , y , i ) = { 1 ⁢ ⁢ if ⁢ ⁢ x i = the ⁢ ⁢ and ⁢ ⁢ y i = DT 0 ⁢ ⁢ otherwise wherein each x consists of a different token of the sequence of tokens, each y consists of a first type of label for each token of the sequence of tokens, and DT is a first label assigned to a determiner; storing, by the one or more computer processing modules onto the one or more non-transitory storage modules, the type of each token of the sequence of tokens; encoding, at the one or more computer processing modules, each token of the sequence of tokens to indicate a second type of label for each token of the sequence of tokens, wherein the second type of label for each token of the sequence of tokens is based on the type of each token of the sequence of tokens and is chosen from a BIO encoding scheme, wherein a label B of the BIO encoding scheme indicates a first token of a brand name, a label I of the BIO encoding scheme indicates a subsequent token of the brand name, and a label O of the BIO encoding scheme indicates a token that is not part of the brand name; storing, by the one or more computer processing modules onto the one or more non-transitory storage modules, the second type of label for each token of the sequence of tokens; determining, at the one or more computer processing modules, an attribute for each token of the sequence of tokens, the attribute comprising the brand name from each token of the sequence of tokens using a label for each token of the sequence of tokens; storing, by the one or more computer processing modules onto the one or more non-transitory storage modules, the attribute for each token of the sequence of tokens; normalizing, at the one or more computer processing modules, the attribute for each token of the sequence of tokens to create standardized representations of the attribute for each token of the sequence of tokens; writing, by the one or more computer processing modules onto the one or more non-transitory storage modules, the attribute for each token of the sequence of tokens to empty database entries associated with the product; and facilitating a representation of the attribute for each token of the sequence of tokens on a user display in response to a search request from a user. 8. The system of claim 1 , wherein: normalizing the attribute for each token of the sequence of tokens to create the standardized representations of the attribute for each token of the sequence of tokens further comprises: comparing the brand name token with a normalization dictionary to determine a standardized representation of the brand name, wherein the standardized representations comprise the standardized representation; and writing the standardized representation of the brand name to a database entry associated with the product; and the computing instructions are further configured to perform acts of: storing the first token of the sequence of tokens associated with the label B of the BIO encoding scheme as a beginning of a brand name token; concatenating to the brand name token each subsequent token of the sequence of tokens that is associated with the label I of the BIO encoding scheme; gathering each unique standardized representation of the brand name; determining a correctness of each unique standardized representation of the brand name; and when the standardized representation of the brand name is not correct, creating an entry in the normalization dictionary to reflect a correct standardized representation of the brand name.

3. The method of claim 1 , wherein vectorizing tokenized sentences of the reference document and of the archived document is performed using a k-skip-n-gram vectorization, wherein skip-grams of a length n with a maximum number of skipped tokens k are generated; and wherein each skip-gram is separately vectorized.

3. The method of claim 1 , wherein vectorizing tokenized sentences of the reference document and of the archived document is performed using a k-skip-n-gram vectorization, wherein skip-grams of a length n with a maximum number of skipped tokens k are generated; and wherein each skip-gram is separately vectorized. 4. The method of claim 3 , wherein calculating the plurality of vector similarity values further comprises multiplying each of the vector similarity values of the plurality of vector similarity values with a corresponding weight, wherein the weight is determined based on the length of the skip-gram, and the number of skipped tokens.

16. The method of claim 15, further comprising the step of generating a utility function using the cognitive profile and wherein step (b3) further comprises updating the coefficients using both the utility function and an approximation of the utility function.

16. The method of claim 15, further comprising the step of generating a utility function using the cognitive profile and wherein step (b3) further comprises updating the coefficients using both the utility function and an approximation of the utility function. 17. The method of claim 16, wherein step (b2) further comprises determining one or more preference relations and thereafter determining the approximation of the utility function using the preference relation(s).

1. A method for web application navigation control, comprising: updating navigation data models used in navigation constraints with received data from an end-user or system, the data models being stored on a computer storage medium; without needing a centralized application-specific controller, automatically selecting from a collection of extensible navigation rules associated with each page of a plurality of pages the extensible navigation rules; evaluating the navigation constraints associated only with the pages potentially changing their ready state to execute from among the plurality of pages in an entire application to determine which pages are ready to run, wherein determining which pages are ready to run is based on updated data from the navigation data models; and selecting a preferred page to be actually navigated to next from among a set of all available and ready pages.

1. A method for web application navigation control, comprising: updating navigation data models used in navigation constraints with received data from an end-user or system, the data models being stored on a computer storage medium; without needing a centralized application-specific controller, automatically selecting from a collection of extensible navigation rules associated with each page of a plurality of pages the extensible navigation rules; evaluating the navigation constraints associated only with the pages potentially changing their ready state to execute from among the plurality of pages in an entire application to determine which pages are ready to run, wherein determining which pages are ready to run is based on updated data from the navigation data models; and selecting a preferred page to be actually navigated to next from among a set of all available and ready pages. 8. The method as recited in claim 1 , wherein with received data from an end-user or system includes a presence of data provided by one of a user and memory storage independent of the user.

11. A method comprising: obtaining a first plurality of records, wherein each record of the first plurality of records is associated with a respective entity and comprises a first one or more fields; obtaining a second plurality of records, wherein each record of the second plurality of records is associated with a respective entity and comprises a second one or more fields, and wherein each record of the second plurality of records is associated with a different entity; identifying, based at least in part on a first field of the first one or more fields, a first subset of the first plurality of records; determining that a distribution of sizes of subsets of the first plurality of records satisfies a distribution rule, the subsets of the first plurality of records including the first subset; identifying, based at least in part on a second field of the second one or more fields, a second subset of the second plurality of records; generating a plurality of record pairs, wherein each record pair in the plurality of record pairs comprises a respective first record from the first subset and a respective second record from the second subset, and wherein at least one field of the first record differs from a corresponding field in the second record; determining a respective match score for each of the plurality of record pairs, the respective match scores comprising probabilities that the respective first record and second record of the respective record pairs are associated with a respective same entity; identifying, for each record in the first subset, a respective cluster of record pairs, wherein each record pair in the cluster includes the record; determining, for each cluster of record pairs, that a diameter of the cluster satisfies a diameter criterion; determining, for each cluster of record pairs, that an entropy of the cluster satisfies an entropy criterion; determining, based at least in part on the distribution of sizes, the respective match scores, the diameter criterion, and the entropy criterion, that each cluster of record pairs corresponds to a respective entity; identifying, for each cluster of record pairs, a respective matching record pair based at least in part on the match scores of the record pairs in the cluster; generating, based at least in part on a geographical location associated with each cluster and a number of record pairs in each cluster, a heat map for display on a client computing device, wherein the heat map enables identification of suitable locations for providing coverage of the geographical location associated with the clusters, wherein the heat map overlays information regarding the number of record pairs in each cluster on the geographic location associated with the cluster, and wherein the heat map displays information regarding the at least one field of individual records in each cluster as a color, symbol, shading, or other representation; and causing the client computing device to display the heat map.

11. A method comprising: obtaining a first plurality of records, wherein each record of the first plurality of records is associated with a respective entity and comprises a first one or more fields; obtaining a second plurality of records, wherein each record of the second plurality of records is associated with a respective entity and comprises a second one or more fields, and wherein each record of the second plurality of records is associated with a different entity; identifying, based at least in part on a first field of the first one or more fields, a first subset of the first plurality of records; determining that a distribution of sizes of subsets of the first plurality of records satisfies a distribution rule, the subsets of the first plurality of records including the first subset; identifying, based at least in part on a second field of the second one or more fields, a second subset of the second plurality of records; generating a plurality of record pairs, wherein each record pair in the plurality of record pairs comprises a respective first record from the first subset and a respective second record from the second subset, and wherein at least one field of the first record differs from a corresponding field in the second record; determining a respective match score for each of the plurality of record pairs, the respective match scores comprising probabilities that the respective first record and second record of the respective record pairs are associated with a respective same entity; identifying, for each record in the first subset, a respective cluster of record pairs, wherein each record pair in the cluster includes the record; determining, for each cluster of record pairs, that a diameter of the cluster satisfies a diameter criterion; determining, for each cluster of record pairs, that an entropy of the cluster satisfies an entropy criterion; determining, based at least in part on the distribution of sizes, the respective match scores, the diameter criterion, and the entropy criterion, that each cluster of record pairs corresponds to a respective entity; identifying, for each cluster of record pairs, a respective matching record pair based at least in part on the match scores of the record pairs in the cluster; generating, based at least in part on a geographical location associated with each cluster and a number of record pairs in each cluster, a heat map for display on a client computing device, wherein the heat map enables identification of suitable locations for providing coverage of the geographical location associated with the clusters, wherein the heat map overlays information regarding the number of record pairs in each cluster on the geographic location associated with the cluster, and wherein the heat map displays information regarding the at least one field of individual records in each cluster as a color, symbol, shading, or other representation; and causing the client computing device to display the heat map. 16. The method of claim 11 further comprising: identifying an indeterminate record pair of the plurality of record pairs, the indeterminate record pair having a match score indicating a least certainty of whether the first record and second record of the indeterminate record pair are associated with the same entity; outputting the indeterminate record pair to a user; receiving, from the user, an indication that the first record and the second record of the indeterminate record pair are associated with the same entity; calculating, for each of the plurality of record pairs, a respective revised match score based at least in part on the indication; wherein identifying the respective matching record pair for each cluster of record pairs is further based at least in part on the revised match scores of the record pairs in the cluster.

6. A method comprising: receiving inputs relating to a plurality of entities in a set, each input indicating approval of an entity in the set for another entity in the set; maintaining a database storing indications of approval associated with each of the plurality of entities in the set; processing the database to determine one or more clusters in the set, the clusters each comprising entities for which a metric of approval of members within the cluster exceeds a metric of approval from entities in the set that are not in the cluster, the processing the database to determine one or more clusters in the set includes: selecting an entity as a seed for the cluster; adding entities to the cluster, the adding comprising iteratively: for a candidate entity determining a fraction of indications of approval for the candidate entity received from entities within the cluster; and selectively adding the candidate entity based, at least in part, on the fraction being above a threshold; and after selectively adding the candidate item, selectively removing items from the cluster that do not meet at least one relatedness criteria; and presenting a suggestion, the suggestion relating to an action involving one or more entities and the suggestion being developed based on the one or more clusters such that the one or more entities are within at least one of the one or more clusters.

6. A method comprising: receiving inputs relating to a plurality of entities in a set, each input indicating approval of an entity in the set for another entity in the set; maintaining a database storing indications of approval associated with each of the plurality of entities in the set; processing the database to determine one or more clusters in the set, the clusters each comprising entities for which a metric of approval of members within the cluster exceeds a metric of approval from entities in the set that are not in the cluster, the processing the database to determine one or more clusters in the set includes: selecting an entity as a seed for the cluster; adding entities to the cluster, the adding comprising iteratively: for a candidate entity determining a fraction of indications of approval for the candidate entity received from entities within the cluster; and selectively adding the candidate entity based, at least in part, on the fraction being above a threshold; and after selectively adding the candidate item, selectively removing items from the cluster that do not meet at least one relatedness criteria; and presenting a suggestion, the suggestion relating to an action involving one or more entities and the suggestion being developed based on the one or more clusters such that the one or more entities are within at least one of the one or more clusters. 10. The method of claim 6 , wherein the entity selected as the seed is an item in a dataset about which information has been requested by a user, and wherein the candidate entity has a node in a graph that is close to a node representing the seed.

12. A computer-readable medium having stored thereon instructions for causing at least one processor to perform a method of searching for non-text binary files on the internet using an index graph database, the index graph database including a plurality of File Uniform Resource Identifiers (URIs) uniquely identifying non-text binary files, and a plurality of distinct content signatures linked to non-text binary files having content signatures identical to the respective distinct content signatures, the method comprising: receiving a search query for a target non-text binary file from an initiating source; identifying a plurality of distinct content signatures in the index graph database corresponding to the search query, the identified content signature being linked to the search query; identifying a plurality of first File URIs in the index graph database for each content signature, the identified plurality of first File URIs being linked to the respective content signature; identifying a plurality of Page Uniform Resource Identifiers (URIs) linked to the first set of File URIs in the index graph database; identifying a plurality of second File URIs in the index graph database, the plurality of second File URIs being linked to the identified Page URIs; providing the initiating source with the first File URIs and second File URIs; determining a file weighted average for each identified File URI of the first File URIs and the second File URIs based on at least one of an average downloading speed or a downloading count of the non-text binary file identified by the corresponding File URI; ranking the first File URIs and second URIs according to the file weighted averages; and providing the initiating source with the ranked File URIs.

12. A computer-readable medium having stored thereon instructions for causing at least one processor to perform a method of searching for non-text binary files on the internet using an index graph database, the index graph database including a plurality of File Uniform Resource Identifiers (URIs) uniquely identifying non-text binary files, and a plurality of distinct content signatures linked to non-text binary files having content signatures identical to the respective distinct content signatures, the method comprising: receiving a search query for a target non-text binary file from an initiating source; identifying a plurality of distinct content signatures in the index graph database corresponding to the search query, the identified content signature being linked to the search query; identifying a plurality of first File URIs in the index graph database for each content signature, the identified plurality of first File URIs being linked to the respective content signature; identifying a plurality of Page Uniform Resource Identifiers (URIs) linked to the first set of File URIs in the index graph database; identifying a plurality of second File URIs in the index graph database, the plurality of second File URIs being linked to the identified Page URIs; providing the initiating source with the first File URIs and second File URIs; determining a file weighted average for each identified File URI of the first File URIs and the second File URIs based on at least one of an average downloading speed or a downloading count of the non-text binary file identified by the corresponding File URI; ranking the first File URIs and second URIs according to the file weighted averages; and providing the initiating source with the ranked File URIs. 13. The computer-readable medium of claim 12 having stored thereon additional instructions for causing at least one processor to perform the following: repeating the step of identifying a plurality of Page URIs and the step of identifying a plurality of second File URIs until a break condition is met.

14. A computing device including a speech-enabled application installed thereon, the computing device comprising: at least one storage device configured to store at least one data structure including information describing a plurality of natural language understanding (NLU) results and corresponding ASR output used to generate the plurality of NW results; an input interface configured to receive first audio comprising speech from a user of the computing device; an automatic speech recognition (ASR) engine configured to: detect based, at least in part, on a threshold time for endpointing, an end of speech in the first audio; and generate a first ASR result based, at least in part, on a portion of the first audio prior to the detected end of speech; and at least one processor programmed to: determine whether a valid action can be performed by the speech-enabled application using the first ASR result; instruct the ASR engine to process second audio when it is determined that a valid action cannot be performed by the speech-enabled application using the first ASR result; determine whether to add the first ASR result and a corresponding NLU result generated using the first ASK result to the at least one data structure stored on the at least one storage device; and add the first ASR result and the corresponding NLU result generated using the first ASR result to the at least one data structure stored on the at least one storage device in response to determining that the first ASK result and the corresponding NLU result should be added.

14. A computing device including a speech-enabled application installed thereon, the computing device comprising: at least one storage device configured to store at least one data structure including information describing a plurality of natural language understanding (NLU) results and corresponding ASR output used to generate the plurality of NW results; an input interface configured to receive first audio comprising speech from a user of the computing device; an automatic speech recognition (ASR) engine configured to: detect based, at least in part, on a threshold time for endpointing, an end of speech in the first audio; and generate a first ASR result based, at least in part, on a portion of the first audio prior to the detected end of speech; and at least one processor programmed to: determine whether a valid action can be performed by the speech-enabled application using the first ASR result; instruct the ASR engine to process second audio when it is determined that a valid action cannot be performed by the speech-enabled application using the first ASR result; determine whether to add the first ASR result and a corresponding NLU result generated using the first ASK result to the at least one data structure stored on the at least one storage device; and add the first ASR result and the corresponding NLU result generated using the first ASR result to the at least one data structure stored on the at least one storage device in response to determining that the first ASK result and the corresponding NLU result should be added. 15. The computing device of claim 14 , wherein determining whether to add the first ASR result and the corresponding NLU result generated using the first ASR result to the at least one data structure comprises: determining a number of times the corresponding NLU result has been received by the computing device from an NLU engine remotely located from the computing device; and determining that the first ASR result and the corresponding NLU result should be added to the at least one data structure when the number of times the corresponding NLU result has been received by the computing device exceeds a threshold value.

43. The method of claim 39 , where at least one request action is selected from a group including: completed, third party, saved, preprogrammed, and new engagements.

43. The method of claim 39 , where at least one request action is selected from a group including: completed, third party, saved, preprogrammed, and new engagements. 45. The method of claim 43 , where a requester may save completed engagements on the mobile phone for use in the future.

8. A computer implemented method comprising: accessing a digital document, wherein the digital document relates to one or more topics; generating a list of expert students, wherein the expert students have authored one or more annotations relating to a topic similar to the one or more topics in the digital document; computing a confidence score for each annotation imported by a user accessing the digital document, wherein the confidence score is based on a test score of one or more users who imported the annotation, the test score being an indication of a quality of the annotation; computing a rank based on the confidence score for each annotation by the one or more users relating to the similar topic; and ordering the list of expert students according to the rank, wherein the rank identifies a level of expertise of the expert students; and presenting the ordered list of expert students, where the ordered list comprises a pre-defined number of expert students with a level of expertise meeting a predefined threshold.

8. A computer implemented method comprising: accessing a digital document, wherein the digital document relates to one or more topics; generating a list of expert students, wherein the expert students have authored one or more annotations relating to a topic similar to the one or more topics in the digital document; computing a confidence score for each annotation imported by a user accessing the digital document, wherein the confidence score is based on a test score of one or more users who imported the annotation, the test score being an indication of a quality of the annotation; computing a rank based on the confidence score for each annotation by the one or more users relating to the similar topic; and ordering the list of expert students according to the rank, wherein the rank identifies a level of expertise of the expert students; and presenting the ordered list of expert students, where the ordered list comprises a pre-defined number of expert students with a level of expertise meeting a predefined threshold. 11. The method of claim 8 , wherein ordering the list of expert students according to the rank comprises: determining a first digital document read by a first expert student and a second digital document read by a second expert student; determining a first relevance of the first digital document relative to the digital document and a second relevance of the second digital document relative to the digital document; and ordering the list of expert students based on the first relevance of the first digital document and the second relevance of the second digital document.

5. The device of claim 1 , wherein the language recognition unit is configured to select the first human language among a plurality of available languages as a default source language for mapping the received signal according to a first geographic location associated with the first communication terminal, and wherein the language translation unit is configured to select the second human language among the plurality of available languages as a default target language for generating the translated signal according to a second geographic location associated with the second communication terminal.

5. The device of claim 1 , wherein the language recognition unit is configured to select the first human language among a plurality of available languages as a default source language for mapping the received signal according to a first geographic location associated with the first communication terminal, and wherein the language translation unit is configured to select the second human language among the plurality of available languages as a default target language for generating the translated signal according to a second geographic location associated with the second communication terminal. 6. The device of claim 5 , further comprising: a controller configured to determine the first and/or second geographic locations according to a respective country code, positioning signal, and/or geographic location of network infrastructure associated with the first and/or second communication terminals.

3. The system as in claim 1 , wherein the data set reflects a result of a search term co-occurrence analysis in which a weight accorded to a search query submission from a user is dependent upon actions performed by the user with respect to associated query result items.

3. The system as in claim 1 , wherein the data set reflects a result of a search term co-occurrence analysis in which a weight accorded to a search query submission from a user is dependent upon actions performed by the user with respect to associated query result items. 4. The system of claim 3 , wherein the weight accorded to the search query submission is dependent upon whether the user viewed a resulting query result item.

23. A method for automatically expanding existing data content that is included in a corpus comprising: automatically identifying a topic from existing data in said corpus; automatically generating search queries to search for content related to said topic identified from said existing data, the queries being generated based on said identified topic; using said generated search queries for automatically conducting a search in and retrieving content from one or more other data repositories not including said corpus; automatically extracting units of text from the retrieved content; automatically determining a relevance of the extracted units of text and their relatedness to the topic identified from said existing data; and automatically selecting new sources of content and including them in the corpus based on the determined relevance to said identified topic including compiling a new document from the most relevant extracted text units, said new document being searchable with said existing data content, wherein the existing data content includes one or more seed documents, said automatically identifying a topic comprising: generating from said one or more documents, a topic name and a topic descriptor corresponding to units extracted from said one or more documents, said generated search queries including: said topic name or words and phrases extracted from said topic descriptor, and wherein said retrieving content includes: running, using one or more search engines, said search queries against the one or more external data repositories, said content retrieved including one or more text passages or documents; said extracting units of text comprising: splitting the retrieved text passages or documents into smaller text units, said splitting using structural markup for demarcating text unit boundaries; and said determining the relevance of the text units from said retrieved passages or documents including: scoring each said text unit using a statistical model based on a lexico-syntactic feature, said lexico-syntactic feature includes a topicality feature, a search feature and a surface feature; wherein said automatically determining a relevance of the extracted units includes fitting a logistic regression (LR) model using said topicality, search and surface features and a generation level to estimate a relevance score of each independent text unit based on their relevance to said topic of the seed document; and said scoring is based further on a topicality feature including: computing a likelihood ratio of a text unit estimated with a topic model and a background language model, said topic model being estimated from text units retrieved for a given topic, and said background language model being estimated from a sample of text units retrieved for different topics identified in documents of said corpus, wherein one or more processor units in communication with a memory storage device performs said generating, retrieving, extracting, relevance determining and selecting.

23. A method for automatically expanding existing data content that is included in a corpus comprising: automatically identifying a topic from existing data in said corpus; automatically generating search queries to search for content related to said topic identified from said existing data, the queries being generated based on said identified topic; using said generated search queries for automatically conducting a search in and retrieving content from one or more other data repositories not including said corpus; automatically extracting units of text from the retrieved content; automatically determining a relevance of the extracted units of text and their relatedness to the topic identified from said existing data; and automatically selecting new sources of content and including them in the corpus based on the determined relevance to said identified topic including compiling a new document from the most relevant extracted text units, said new document being searchable with said existing data content, wherein the existing data content includes one or more seed documents, said automatically identifying a topic comprising: generating from said one or more documents, a topic name and a topic descriptor corresponding to units extracted from said one or more documents, said generated search queries including: said topic name or words and phrases extracted from said topic descriptor, and wherein said retrieving content includes: running, using one or more search engines, said search queries against the one or more external data repositories, said content retrieved including one or more text passages or documents; said extracting units of text comprising: splitting the retrieved text passages or documents into smaller text units, said splitting using structural markup for demarcating text unit boundaries; and said determining the relevance of the text units from said retrieved passages or documents including: scoring each said text unit using a statistical model based on a lexico-syntactic feature, said lexico-syntactic feature includes a topicality feature, a search feature and a surface feature; wherein said automatically determining a relevance of the extracted units includes fitting a logistic regression (LR) model using said topicality, search and surface features and a generation level to estimate a relevance score of each independent text unit based on their relevance to said topic of the seed document; and said scoring is based further on a topicality feature including: computing a likelihood ratio of a text unit estimated with a topic model and a background language model, said topic model being estimated from text units retrieved for a given topic, and said background language model being estimated from a sample of text units retrieved for different topics identified in documents of said corpus, wherein one or more processor units in communication with a memory storage device performs said generating, retrieving, extracting, relevance determining and selecting. 27. The method as claimed in claim 23 , further comprising: automatically extending content of said corpus to include content of text units having scores above a pre-determined threshold, or include content of text units using an automatic summarization method.

15. The system of claim 14 , wherein the instructions cause the one or more data processing apparatus to perform operations comprising: receiving a second content item request that specifies a second phrase of one or more words; determining that two keywords in a same content distribution campaign are both eligible to be selected as a controlling keyword for the second content item request; identifying a tiebreaker rule based on data associated with the two keywords; identifying, from the two keywords, a second controlling keyword based on the tiebreaker rule; and providing, in response to the second request, data associated with the second controlling keyword.

15. The system of claim 14 , wherein the instructions cause the one or more data processing apparatus to perform operations comprising: receiving a second content item request that specifies a second phrase of one or more words; determining that two keywords in a same content distribution campaign are both eligible to be selected as a controlling keyword for the second content item request; identifying a tiebreaker rule based on data associated with the two keywords; identifying, from the two keywords, a second controlling keyword based on the tiebreaker rule; and providing, in response to the second request, data associated with the second controlling keyword. 18. The computer readable medium of claim 15 , wherein identifying a second controlling keyword based on the tiebreaker rule comprises: determining that a first keyword from the two keywords has a less specific match with the phrase than a second keyword from the two keywords; determining that a first quality score for the first keyword exceeds a second quality score for the second keyword; and selecting, in response to the determination, the first keyword as the second controlling keyword.

1. A method comprising: determining a first grouping of entities of a plurality of entities based at least in part on one or more first dominance relationships between pairs of entities associated with a plurality of query-independent attribute values; in response to processing a search query, identifying two or more entities from the first grouping that are comparable to the search query, the two or more entities being identified as being comparable at least partially in response to determining that comparability values between pairs of the two or more entities have at least a threshold value of comparability; determining one or more second dominance relationships between the pairs of the two or more entities based at least in part on at least one query-dependent value of the two or more entities; and determining a suggestion set comprising a subset of the two or more entities, the suggestion set comprising one or more identified entities not dominated by another entity of the two or more entities based at least in part on the one or more second dominance relationships and based at least in part on one or more inference rules indicative of an existence of one or more static domination relationships between attributes of the plurality of entities, wherein a first entity of the two or more entities is capable of being determined not to dominate a second entity of the two or more entities at least partially in response to a determination that individual first attribute values of at least two of the attributes of the first entity are not indicative of a greater scope of interest than corresponding individual second attribute values of the at least two of the attributes of the second entity.

1. A method comprising: determining a first grouping of entities of a plurality of entities based at least in part on one or more first dominance relationships between pairs of entities associated with a plurality of query-independent attribute values; in response to processing a search query, identifying two or more entities from the first grouping that are comparable to the search query, the two or more entities being identified as being comparable at least partially in response to determining that comparability values between pairs of the two or more entities have at least a threshold value of comparability; determining one or more second dominance relationships between the pairs of the two or more entities based at least in part on at least one query-dependent value of the two or more entities; and determining a suggestion set comprising a subset of the two or more entities, the suggestion set comprising one or more identified entities not dominated by another entity of the two or more entities based at least in part on the one or more second dominance relationships and based at least in part on one or more inference rules indicative of an existence of one or more static domination relationships between attributes of the plurality of entities, wherein a first entity of the two or more entities is capable of being determined not to dominate a second entity of the two or more entities at least partially in response to a determination that individual first attribute values of at least two of the attributes of the first entity are not indicative of a greater scope of interest than corresponding individual second attribute values of the at least two of the attributes of the second entity. 6. The method of claim 1 , further comprising determining at least one of the two or more attributes from a semi-structured or structured database.

3. The method of claim 2 wherein the formal domain assumptions comprise domain constraints.

3. The method of claim 2 wherein the formal domain assumptions comprise domain constraints. 4. The method of claim 3 wherein the domain constraints comprise cardinality constraints.

16. The method of claim 13 , wherein the communication with the concatenation cost database comprises: extracting a concatenation cost of the pair of acoustic units from the concatenation cost database if the concatenation cost database contains the concatenation cost of the pair of acoustic units; and determining a value of the concatenation cost of the pair of acoustic units if the concatenation cost data base does not contain the concatenation cost of the pair of acoustic units.

16. The method of claim 13 , wherein the communication with the concatenation cost database comprises: extracting a concatenation cost of the pair of acoustic units from the concatenation cost database if the concatenation cost database contains the concatenation cost of the pair of acoustic units; and determining a value of the concatenation cost of the pair of acoustic units if the concatenation cost data base does not contain the concatenation cost of the pair of acoustic units. 20. The method of claim 16 , wherein determining a value of the concatenation cost of the pair of acoustic units comprises computing the concatenation cost of the pair of acoustic units.

8. A computer-implemented method, comprising: executing an application under test in a layout engine module, wherein the application under test comprises one or more instructions in one scripting language, wherein a selected scripting language engine is configured to interpret the one or more instructions in the one scripting language, and wherein the selected scripting language engine is selected using a user interface of testing options; initializing, in the layout engine module, a debugging session for the application under test, wherein the debugging session is configured to generate output data for the application under test, wherein the debugging session is based on a testing option that selects the selected scripting language engine from among a plurality of scripting language engines, and wherein each scripting language engine of the plurality of scripting language engines is configured to interpret the one scripting language; executing a communication module configured to receive at least packed command data associated with the debugging session; and executing an unpack module configured to establish communication between the communication module and the layout engine module at least to provide unpacked command data based on the packed command data to the layout engine module.

8. A computer-implemented method, comprising: executing an application under test in a layout engine module, wherein the application under test comprises one or more instructions in one scripting language, wherein a selected scripting language engine is configured to interpret the one or more instructions in the one scripting language, and wherein the selected scripting language engine is selected using a user interface of testing options; initializing, in the layout engine module, a debugging session for the application under test, wherein the debugging session is configured to generate output data for the application under test, wherein the debugging session is based on a testing option that selects the selected scripting language engine from among a plurality of scripting language engines, and wherein each scripting language engine of the plurality of scripting language engines is configured to interpret the one scripting language; executing a communication module configured to receive at least packed command data associated with the debugging session; and executing an unpack module configured to establish communication between the communication module and the layout engine module at least to provide unpacked command data based on the packed command data to the layout engine module. 14. The method of claim 8 , wherein the application under test comprises a plurality of runtime instances.

3. The method of claim 1 , wherein for each source query of the second set of source queries, the method further includes: identifying a source table included in the respective source query; determining a cache table name based on a name of the identified source table, wherein the storing includes storing the result in a table identified by the cache table name.

3. The method of claim 1 , wherein for each source query of the second set of source queries, the method further includes: identifying a source table included in the respective source query; determining a cache table name based on a name of the identified source table, wherein the storing includes storing the result in a table identified by the cache table name. 5. The method of claim 3 , wherein the generating a third federated query includes: identifying one or more source queries embedded in the second federated query that is specific to an unavailable data source; rewriting each of the identified one or more source queries embedded in the second federated query; and replacing the identified one or more source queries in the second federated query with one or more corresponding rewritten source queries.

18. The non-transitory computer-readable medium claim 17 further comprising a verification module configured to verify the second classification based at least in part one a set of verification criteria.

18. The non-transitory computer-readable medium claim 17 further comprising a verification module configured to verify the second classification based at least in part one a set of verification criteria. 19. The non-transitory computer-readable medium claim 18 , wherein the verification module is further configured to determine that the second classification does not satisfy the set of verification criteria, and to transmit the page for manual classification.

3. The method of claim 1 , wherein the first language section includes a first section title and wherein the method further comprises generating the first section title from the at least two distinct categories of the set of categories, the format of the first section title based on the content characteristic and contextual relationships for each of the at least two distinct categories of the set of categories.

3. The method of claim 1 , wherein the first language section includes a first section title and wherein the method further comprises generating the first section title from the at least two distinct categories of the set of categories, the format of the first section title based on the content characteristic and contextual relationships for each of the at least two distinct categories of the set of categories. 4. The method of claim 3 , wherein the generating of the first section title from the at least two distinct categories of the set of categories includes generating the first section title from a column label and a row label, the format of the first section title based on the content characteristic and contextual relationships of the column label and row label.

9. A computer program product in a non-transitory computer-readable storage medium for collecting and displaying information about printing device related objects on a network, comprising machine-readable code for causing a machine to perform the method steps of: providing a user with a graphic user interface of a main window of a device management application comprising a left pane tree and a central view pane; the user selecting in the left pane tree at least one topic of interest from at least one category of interest from a list of topics of interest which are hierarchically arranged by categories of interest, which left pane tree displays the list of topics of interest and the categories of interest, and which left pane tree does not display printing device related objects, wherein the list of topics provides a central point user interface comprising at least one root category of topics comprising the group consisting of device management, account management, queue management, queue user management, and combinations thereof to enable the user's selection from multiple levels of printing device related objects with circular associations to each other, which printing device related objects comprising traditional printing devices and non-traditional printing device related objects, which non-traditional printing device related objects comprise the group consisting of users, devices, alerts, hosts, queues, jobs, accounts, balances, and combinations thereof; the user specifying displaying in the central view pane of object property information associated with the selected at least one topic of interest about the printing device related objects; and causing displaying in the central view pane of object property information associated with the selected at least one topic of interest about the printing device related objects, while the left pane tree does not display printing device related objects wherein a list of devices can be displayed in the central view pane, and wherein displaying of the list of devices comprises: optionally expanding a Device Management category in order to select a List View topic of interest within the Device Management category; selecting the List View topic of interest within the Device Management category; and causing the list of devices to be displayed in the central view pane using a default object locator ALL, which locates all objects without any filtering, and the list of devices is never displayed in the left pane tree.

9. A computer program product in a non-transitory computer-readable storage medium for collecting and displaying information about printing device related objects on a network, comprising machine-readable code for causing a machine to perform the method steps of: providing a user with a graphic user interface of a main window of a device management application comprising a left pane tree and a central view pane; the user selecting in the left pane tree at least one topic of interest from at least one category of interest from a list of topics of interest which are hierarchically arranged by categories of interest, which left pane tree displays the list of topics of interest and the categories of interest, and which left pane tree does not display printing device related objects, wherein the list of topics provides a central point user interface comprising at least one root category of topics comprising the group consisting of device management, account management, queue management, queue user management, and combinations thereof to enable the user's selection from multiple levels of printing device related objects with circular associations to each other, which printing device related objects comprising traditional printing devices and non-traditional printing device related objects, which non-traditional printing device related objects comprise the group consisting of users, devices, alerts, hosts, queues, jobs, accounts, balances, and combinations thereof; the user specifying displaying in the central view pane of object property information associated with the selected at least one topic of interest about the printing device related objects; and causing displaying in the central view pane of object property information associated with the selected at least one topic of interest about the printing device related objects, while the left pane tree does not display printing device related objects wherein a list of devices can be displayed in the central view pane, and wherein displaying of the list of devices comprises: optionally expanding a Device Management category in order to select a List View topic of interest within the Device Management category; selecting the List View topic of interest within the Device Management category; and causing the list of devices to be displayed in the central view pane using a default object locator ALL, which locates all objects without any filtering, and the list of devices is never displayed in the left pane tree. 10. The computer program product of claim 9 , wherein displaying of the information associated with the selected at least one topic of interest about the printing device related objects comprises: selecting at least one object locator to filter and display printing device related object properties and information that satisfy at least one criterion defined for the selected at least one object locator.

10. The system of claim 9 , wherein sanitizing the sensitive information further comprises: finding a named entity in the transcription; and performing, on the named entity, one of value distortion, value disassociation, and value class membership to preserve privacy in a spoken natural language database.

10. The system of claim 9 , wherein sanitizing the sensitive information further comprises: finding a named entity in the transcription; and performing, on the named entity, one of value distortion, value disassociation, and value class membership to preserve privacy in a spoken natural language database. 11. The system of claim 10 , wherein sanitizing the sensitive information further comprises: performing, on the named entity, two of value distortion, value disassociation, and value class membership to preserve privacy in a spoken natural language database.

5. The apparatus according to claim 1 or 4 , wherein the function generation unit includes: a selecting unit configured to select one component from a set of a plurality of components of the feature vector; a dividing unit configured to divide the group into two groups using the selected component as a border; a first operation unit configured to calculate a first quantization error when quantizing one of the divided set of the components into quantization number 1; a second operation unit configured to calculate a second quantization error when quantizing the other of the divided set of the components into quantization number n based on the quantization threshold and the first quantization error calculated on quantization number n−1; a third operation unit configured to add the first quantization error and the second quantization error to calculate a quantization error of the quantization number n+1; and a fourth operation unit configured to calculate a division value of the quantization threshold corresponding to a quantization error of the quantization number n+1.

5. The apparatus according to claim 1 or 4 , wherein the function generation unit includes: a selecting unit configured to select one component from a set of a plurality of components of the feature vector; a dividing unit configured to divide the group into two groups using the selected component as a border; a first operation unit configured to calculate a first quantization error when quantizing one of the divided set of the components into quantization number 1; a second operation unit configured to calculate a second quantization error when quantizing the other of the divided set of the components into quantization number n based on the quantization threshold and the first quantization error calculated on quantization number n−1; a third operation unit configured to add the first quantization error and the second quantization error to calculate a quantization error of the quantization number n+1; and a fourth operation unit configured to calculate a division value of the quantization threshold corresponding to a quantization error of the quantization number n+1. 6. The apparatus according to claim 5 , wherein the first quantization error is the minimum quantization error when quantizing one of the divided set of the components into quantization number 1, the second quantization error is the minimum quantization error when quantizing the other of the divided set of the components into quantization number n.

8. The system of claim 7 , further comprising the one or more processors executing a key-value delimiter extractor configured to: group the plurality of slices that are divided from each of the plurality of conversations using the field delimiter of the protocol, into a slice-set for each of the plurality of conversations, wherein the plurality of conversations correspond to a plurality of slice-sets; extract, based on a pre-determined key-value delimiter selection criterion, a plurality of longest common prefixes each shared across a portion of the plurality of slice-sets; and extract a common trailing token in the plurality of longest common prefixes as the key-value delimiter of the protocol.

8. The system of claim 7 , further comprising the one or more processors executing a key-value delimiter extractor configured to: group the plurality of slices that are divided from each of the plurality of conversations using the field delimiter of the protocol, into a slice-set for each of the plurality of conversations, wherein the plurality of conversations correspond to a plurality of slice-sets; extract, based on a pre-determined key-value delimiter selection criterion, a plurality of longest common prefixes each shared across a portion of the plurality of slice-sets; and extract a common trailing token in the plurality of longest common prefixes as the key-value delimiter of the protocol. 12. The system of claim 8 , further comprising the one or more processors executing a command extractor configured to: identify a command of the protocol from a slice of the plurality of slices that does not include a key-value delimiter.

1. A method for controlling a speech recognition function for a data processing system, the data processing system having a display, a speech recognition input device, and a cursor control device, the cursor control device having a first selector and a second selector separate from the first selector, the method comprising the steps of: (a) displaying at least one object and a moveable cursor on the display; (b) controlling the moveable cursor on the display in x and y directions simultaneously in response to user-manipulation of the cursor control device; (c) selecting one of the at least one object displayed on the display in response to user-manipulation of the cursor control device and user-manipulation of the first selector of the cursor control device; (d) activating the speech recognition function in response to engagement of the second selector of the cursor control device; (e) inputting a spoken command for the data processing system by the speech recognition input device; and (f) deactivating the speech recognition function in response to disengagement of the second selector of the cursor control device.

1. A method for controlling a speech recognition function for a data processing system, the data processing system having a display, a speech recognition input device, and a cursor control device, the cursor control device having a first selector and a second selector separate from the first selector, the method comprising the steps of: (a) displaying at least one object and a moveable cursor on the display; (b) controlling the moveable cursor on the display in x and y directions simultaneously in response to user-manipulation of the cursor control device; (c) selecting one of the at least one object displayed on the display in response to user-manipulation of the cursor control device and user-manipulation of the first selector of the cursor control device; (d) activating the speech recognition function in response to engagement of the second selector of the cursor control device; (e) inputting a spoken command for the data processing system by the speech recognition input device; and (f) deactivating the speech recognition function in response to disengagement of the second selector of the cursor control device. 3. The method of claim 1, wherein the inputting step (e) includes the step of inputting the spoken command by the speech recognition input device that includes a microphone.

69. The method of claim 59 , further comprising resubmitting the at least one first predictive background query in response to an update identifying the stored result of the at least one first predictive background query as being invalid.

69. The method of claim 59 , further comprising resubmitting the at least one first predictive background query in response to an update identifying the stored result of the at least one first predictive background query as being invalid. 71. The system of claim 69 , wherein resubmitting the at least one first predictive background query further comprises identifying the result to the at least one first predictive background query that would mostly likely be displayed by the user interface and deleting the result to the at least one first predictive background query that would least likely be displayed by the user interface.

7. A computer-implemented method of storing extensible markup language (XML) instances, comprising: typing a column of a relational database with a container for XML schema namespaces that contains a plurality of XML schema namespaces; storing at least one XML instance in said column of a relational database; and validating said at least one XML instance against at least one XML schema represented by said container for XML schema namespaces, wherein XML instance comprises a set of XML data conforming to a structure provided by XML schema, XML schema identifies and organizes data in an XML instance, and XML schema namespace is used to ensure unique naming of elements and attributes in an XML schema.

7. A computer-implemented method of storing extensible markup language (XML) instances, comprising: typing a column of a relational database with a container for XML schema namespaces that contains a plurality of XML schema namespaces; storing at least one XML instance in said column of a relational database; and validating said at least one XML instance against at least one XML schema represented by said container for XML schema namespaces, wherein XML instance comprises a set of XML data conforming to a structure provided by XML schema, XML schema identifies and organizes data in an XML instance, and XML schema namespace is used to ensure unique naming of elements and attributes in an XML schema. 8. The method of claim 7 , further including creating said container for XML schema namespaces by specifying the name of said container and said plurality of XML schema namespaces.

27. A computer readable tangible, non-transitory medium encoded with a computer program comprising instructions that when executed operate to cause a computer to perform operations: identifying images that appear in a plurality of distinct book content items, the distinct book content items corresponding to published books and lacking explicit electronic links between each other; generating implicit links between two or more of the distinct book content items that each include a similar image, the links represented as weighted edges in a graph in which nodes represent corresponding book content items, each weighted edge representing one or more matches of image content in corresponding book content items as between different books for nodes that define a corresponding weighted edge; for particular distinct ones of the nodes, identifying images that match each other as between different book content items based on multiple descriptor points for each of the images, and assigning weightings to particular edges between the distinct ones of the nodes; and determining a rank score for each of the two or more distinct book content items based on the implicit links between the two or more distinct book content items, the rank score for each of the two or more distinct book content items being a value indicative of the importance of the distinct book content item relative to other distinct book content items.

27. A computer readable tangible, non-transitory medium encoded with a computer program comprising instructions that when executed operate to cause a computer to perform operations: identifying images that appear in a plurality of distinct book content items, the distinct book content items corresponding to published books and lacking explicit electronic links between each other; generating implicit links between two or more of the distinct book content items that each include a similar image, the links represented as weighted edges in a graph in which nodes represent corresponding book content items, each weighted edge representing one or more matches of image content in corresponding book content items as between different books for nodes that define a corresponding weighted edge; for particular distinct ones of the nodes, identifying images that match each other as between different book content items based on multiple descriptor points for each of the images, and assigning weightings to particular edges between the distinct ones of the nodes; and determining a rank score for each of the two or more distinct book content items based on the implicit links between the two or more distinct book content items, the rank score for each of the two or more distinct book content items being a value indicative of the importance of the distinct book content item relative to other distinct book content items. 28. The computer readable medium of claim 27 , wherein generating implicit links comprises: identifying descriptor points from the images appearing in the plurality of distinct book content items, the descriptor points defining localized features of the images, the localized features being characteristics of a portion of the image; and generating an implicit link between two or more distinct book content items that each include an image having a matching descriptor point, each matching descriptor point being identified based on similarities between pairs of descriptor points.

29. A method to blend graphical objects comprising: (a) obtaining a page description language representation of the graphical objects, the graphical objects having transparency characteristics and color characteristics; (b) converting a portion of the page description language representation into a planar map representation, the planar map representation having regions wherein each region is associated with one or more of the graphical objects; (c) assigning a color to a planar map region as a function of the transparency characteristics and color characteristics of the graphical objects associated with the planar map region; (d) sorting the planar map regions into a print order; and (e) rasterizing a second portion of the print ordered planar map regions.

29. A method to blend graphical objects comprising: (a) obtaining a page description language representation of the graphical objects, the graphical objects having transparency characteristics and color characteristics; (b) converting a portion of the page description language representation into a planar map representation, the planar map representation having regions wherein each region is associated with one or more of the graphical objects; (c) assigning a color to a planar map region as a function of the transparency characteristics and color characteristics of the graphical objects associated with the planar map region; (d) sorting the planar map regions into a print order; and (e) rasterizing a second portion of the print ordered planar map regions. 32. The method of claim 29 wherein the transparency characteristics are given by a plurality of pixels wherein each pixel provides a transparency value and a color value.

4. The method as claimed in claim 1 , wherein the transformation of the document fragment of the source document is a changing a visual characteristic of the document fragment of the source document.

4. The method as claimed in claim 1 , wherein the transformation of the document fragment of the source document is a changing a visual characteristic of the document fragment of the source document. 7. The method as claimed in claim 4 , wherein the changed visual characteristic of the document fragment is color.

7. A computer program product stored on a computer readable medium and comprising instructions that when executed cause a computer system to: index a plurality of documents in the document collection by: providing a set of phrases; for a plurality of documents in the document collection: identifying a plurality of phrases from the set of phrases that occurs in the document; for a plurality of the identified phrases, scoring the phrase to produce a phrase relevance score for the phrase with respect to the document, and storing the phrase relevance score for the document in a phrase posting list for the phrase; receive a search query of three or more words; determining a set of valid phrases in the search query by: decomposing, by at least one processor of a computer system, the query into a plurality of candidate phrasifications, including different groupings of words of the query, each candidate phrasification comprising a disjoint union of component phrases, and each component phrase including at least one word or related word of the query; scoring, by at least one of the processors of the computer system, at least two candidate phrasifications, wherein the candidate phrasifications include one or more component phrases, wherein the scoring is based on a probability of occurrence of each of the candidate phrasification's component phrases in a corpus of documents and is based on the number of component phrases constituting the candidate phrasification, wherein candidate phrasifications having relatively fewer component phrases are weighted higher than candidate phrasifications having relatively more component phrases; comparing, by at least one of the processors of the computer system, a score for each scored candidate phrasification to a threshold value; and selecting, by at least one of the processors of the computer system, at least one candidate phrasification, wherein the scores of each selected candidate phrasification exceed a threshold value and identifying the component phrase(s) of the selected candidate phrasification(s) as valid phrases for the search query; for each valid phrase for the search query, obtaining from the phrase posting list for the valid phrase the phrase relevance score for documents in which the valid phrase occurs; and for documents in which a valid phrase of the query occurs, scoring the document to produce a final relevance score using the phrase relevance scores for the document and based on the valid phrases of the search query.

7. A computer program product stored on a computer readable medium and comprising instructions that when executed cause a computer system to: index a plurality of documents in the document collection by: providing a set of phrases; for a plurality of documents in the document collection: identifying a plurality of phrases from the set of phrases that occurs in the document; for a plurality of the identified phrases, scoring the phrase to produce a phrase relevance score for the phrase with respect to the document, and storing the phrase relevance score for the document in a phrase posting list for the phrase; receive a search query of three or more words; determining a set of valid phrases in the search query by: decomposing, by at least one processor of a computer system, the query into a plurality of candidate phrasifications, including different groupings of words of the query, each candidate phrasification comprising a disjoint union of component phrases, and each component phrase including at least one word or related word of the query; scoring, by at least one of the processors of the computer system, at least two candidate phrasifications, wherein the candidate phrasifications include one or more component phrases, wherein the scoring is based on a probability of occurrence of each of the candidate phrasification's component phrases in a corpus of documents and is based on the number of component phrases constituting the candidate phrasification, wherein candidate phrasifications having relatively fewer component phrases are weighted higher than candidate phrasifications having relatively more component phrases; comparing, by at least one of the processors of the computer system, a score for each scored candidate phrasification to a threshold value; and selecting, by at least one of the processors of the computer system, at least one candidate phrasification, wherein the scores of each selected candidate phrasification exceed a threshold value and identifying the component phrase(s) of the selected candidate phrasification(s) as valid phrases for the search query; for each valid phrase for the search query, obtaining from the phrase posting list for the valid phrase the phrase relevance score for documents in which the valid phrase occurs; and for documents in which a valid phrase of the query occurs, scoring the document to produce a final relevance score using the phrase relevance scores for the document and based on the valid phrases of the search query. 8. The computer program product of claim 7 , wherein: scoring the phrase to produce a phrase relevance score for the phrase with respect to the document comprises scoring the phrase and the document using a first scoring function; and scoring the document to produce a final relevance score using the phrase relevance scores for the document comprises scoring the document using a second scoring function.

1. A computer-implemented method of extracting information from co-occurring Hyper Text Mark-up Language (HTML) structured documents, the method comprising: presenting a list of web sites to a user; receiving one or more of the web sites selected from the user for data extraction; collecting a plurality of co-occurring different HTML structured documents for each of the selected web sites at a computer comprising a processor; forming a plurality of clusters comprising different subsets of the co-occurring HTML structured documents, wherein: each cluster comprises a different HTML structured document of the plurality of co-occurring HTML structured documents as a centroid document and other HTML structured documents of the plurality of co-occurring HTML structured documents that achieve a threshold of similarity with respect to the centroid document, the clusters are formed by comparing each co-occurring HTML structured document to each centroid document of each cluster based on relative structural similarity of HTML data structure of each co-occurring HTML structured document with respect to HTML data structure of each centroid document of each cluster, an alignment algorithm is used to determine the co-occurring HTML structured documents that achieve the threshold of similarity with respect to each centroid document by comparing structured locations of data fields for storing data elements within each centroid document and structured locations of corresponding data fields for storing data elements within each of the co-occurring HTML structured documents, the co-occurring HTML structured documents are compared to each centroid document based on similarity of structured locations of corresponding data fields within the HTML data structures without regard to content of data elements stored in the corresponding data fields within the HTML data structures, and the relative structural similarity of a particular co-occurring HTML structured document with respect to a particular centroid document is penalized when the co-occurring HTML structured document includes a data field that is within the particular centroid document in a different structured location; displaying a list of clusters; displaying the centroid document of a particular cluster selected from the list of clusters; marking a data element on the centroid document of the particular cluster; identifying a data element on each of the other HTML structured documents of the particular cluster that is stored within a data field having a structured location that corresponds to the structured location of the data field storing the marked data element within the centroid document of the particular cluster; and providing a user interface displaying content of data elements identified from the other HTML structured documents of the particular cluster on a computer display.

1. A computer-implemented method of extracting information from co-occurring Hyper Text Mark-up Language (HTML) structured documents, the method comprising: presenting a list of web sites to a user; receiving one or more of the web sites selected from the user for data extraction; collecting a plurality of co-occurring different HTML structured documents for each of the selected web sites at a computer comprising a processor; forming a plurality of clusters comprising different subsets of the co-occurring HTML structured documents, wherein: each cluster comprises a different HTML structured document of the plurality of co-occurring HTML structured documents as a centroid document and other HTML structured documents of the plurality of co-occurring HTML structured documents that achieve a threshold of similarity with respect to the centroid document, the clusters are formed by comparing each co-occurring HTML structured document to each centroid document of each cluster based on relative structural similarity of HTML data structure of each co-occurring HTML structured document with respect to HTML data structure of each centroid document of each cluster, an alignment algorithm is used to determine the co-occurring HTML structured documents that achieve the threshold of similarity with respect to each centroid document by comparing structured locations of data fields for storing data elements within each centroid document and structured locations of corresponding data fields for storing data elements within each of the co-occurring HTML structured documents, the co-occurring HTML structured documents are compared to each centroid document based on similarity of structured locations of corresponding data fields within the HTML data structures without regard to content of data elements stored in the corresponding data fields within the HTML data structures, and the relative structural similarity of a particular co-occurring HTML structured document with respect to a particular centroid document is penalized when the co-occurring HTML structured document includes a data field that is within the particular centroid document in a different structured location; displaying a list of clusters; displaying the centroid document of a particular cluster selected from the list of clusters; marking a data element on the centroid document of the particular cluster; identifying a data element on each of the other HTML structured documents of the particular cluster that is stored within a data field having a structured location that corresponds to the structured location of the data field storing the marked data element within the centroid document of the particular cluster; and providing a user interface displaying content of data elements identified from the other HTML structured documents of the particular cluster on a computer display. 8. The method of claim 1 , further comprising extracting data from the other HTML structured documents of the particular cluster based on the identified data element on each of the other HTML structured documents of the particular cluster.

9. A system for assigning a gesture dictionary, comprising: a memory bearing instructions that, upon execution by a processor, cause the system at least to: determine a characteristic of a user that is independent of a motion or pose made by the user; correlate the characteristic of the user to a first gesture dictionary of a plurality of gesture dictionaries, each gesture dictionary of the plurality of gesture dictionaries identifying a set of input commands to a computer that may be invoked by a performance of a corresponding gesture; assign the first gesture dictionary to the user, the first gesture dictionary corresponding to the characteristic; and process captured data with the first gesture dictionary to identify whether a second motion or pose by the user in the captured data invokes an input command to the computer.

9. A system for assigning a gesture dictionary, comprising: a memory bearing instructions that, upon execution by a processor, cause the system at least to: determine a characteristic of a user that is independent of a motion or pose made by the user; correlate the characteristic of the user to a first gesture dictionary of a plurality of gesture dictionaries, each gesture dictionary of the plurality of gesture dictionaries identifying a set of input commands to a computer that may be invoked by a performance of a corresponding gesture; assign the first gesture dictionary to the user, the first gesture dictionary corresponding to the characteristic; and process captured data with the first gesture dictionary to identify whether a second motion or pose by the user in the captured data invokes an input command to the computer. 10. The system of claim 9 , wherein the instructions that, upon execution by the processor, cause the system at least to determine the characteristic of the user that is independent of a motion or pose made by the user further cause the system at least to: determine whether the user is left handed or right handed.

10. A method comprising: querying historical linguistic data sets of exchanges between customers and customer relations representatives over social media; extracting conversations from the historical linguistic data sets between said customers and said customer relations representatives; wherein extracting conversations uses a customer relations representative identifier and extracts all messages sent by said customer relations representative identifier or to said customer relations representative identifier; wherein said messages are grouped by a customer using a customer associated identifier; ordering chronologically a list of all said messages by said customer associated identifier; extracting from the list of all said messages groups of messages, wherein each group of messages is related to an individual subject; aggregating said groups of messages into pseudo synchronous conversations; separating each pseudo synchronous conversation of the pseudo synchronous conversations into one or more segments and associating one or more classes to each of the one or more segments; labeling each segment in said each pseudo synchronous conversation according to conversation analysis; and, computing engagement metrics from said labels of each said segment, wherein said engagement metrics are selected from the group consisting of: resolution rate, properly handled threads rate, customer hang-up, conversion rate, and happy customer rate.

10. A method comprising: querying historical linguistic data sets of exchanges between customers and customer relations representatives over social media; extracting conversations from the historical linguistic data sets between said customers and said customer relations representatives; wherein extracting conversations uses a customer relations representative identifier and extracts all messages sent by said customer relations representative identifier or to said customer relations representative identifier; wherein said messages are grouped by a customer using a customer associated identifier; ordering chronologically a list of all said messages by said customer associated identifier; extracting from the list of all said messages groups of messages, wherein each group of messages is related to an individual subject; aggregating said groups of messages into pseudo synchronous conversations; separating each pseudo synchronous conversation of the pseudo synchronous conversations into one or more segments and associating one or more classes to each of the one or more segments; labeling each segment in said each pseudo synchronous conversation according to conversation analysis; and, computing engagement metrics from said labels of each said segment, wherein said engagement metrics are selected from the group consisting of: resolution rate, properly handled threads rate, customer hang-up, conversion rate, and happy customer rate. 11. The method of claim 10 , wherein said data sets are asynchronous and decentralized.

1. A method implemented by a data processing apparatus, comprising: receiving an original query from a user device, the query including original query terms; receiving data identifying a set of web page resources that are determined to be responsive to the original query, each web page resource being a web page and identified by a corresponding URL address; determining, for the original query, a resource quality measure that is a measure of quality of the set of web page resources determined to be responsive to the original query; in response to determining that the resource quality measure does not meeting a resource quality measure threshold: determining, for each original query term of the query, a respective quality measure for the original query term, wherein each respective quality measure determined for an original query term is a quality measure that is different from the resource quality measure determined for the query; determining that an original query term of the original query term is a potentially inaccurate term based on the respective quality measure not meeting a respective quality measure threshold, and in response to determining that the original query term of the original query is a potentially inaccurate term: generating derivative queries from the original query, each derivative query not including the potentially inaccurate term; for each of the derivative queries, submitting the derivative query for a search of a resource corpus index of resources and receiving data identifying a respective set of resources that are determined to be responsive to the derivative query, wherein each resource is a web page resource hosted by a server and having a corresponding resource address; determining a corrected term that is different from the potentially inaccurate term based on the identified resources responsive to each of the one or more derivative queries, wherein the corrected term is determined independent of a set of resources identified as being responsive to the original query; generating a corrected query that that includes and the corrected term substituted for the potentially inaccurate term; performing a search operation that uses the corrected query as input; and providing results of the search operation to the user device in response to the original query.

1. A method implemented by a data processing apparatus, comprising: receiving an original query from a user device, the query including original query terms; receiving data identifying a set of web page resources that are determined to be responsive to the original query, each web page resource being a web page and identified by a corresponding URL address; determining, for the original query, a resource quality measure that is a measure of quality of the set of web page resources determined to be responsive to the original query; in response to determining that the resource quality measure does not meeting a resource quality measure threshold: determining, for each original query term of the query, a respective quality measure for the original query term, wherein each respective quality measure determined for an original query term is a quality measure that is different from the resource quality measure determined for the query; determining that an original query term of the original query term is a potentially inaccurate term based on the respective quality measure not meeting a respective quality measure threshold, and in response to determining that the original query term of the original query is a potentially inaccurate term: generating derivative queries from the original query, each derivative query not including the potentially inaccurate term; for each of the derivative queries, submitting the derivative query for a search of a resource corpus index of resources and receiving data identifying a respective set of resources that are determined to be responsive to the derivative query, wherein each resource is a web page resource hosted by a server and having a corresponding resource address; determining a corrected term that is different from the potentially inaccurate term based on the identified resources responsive to each of the one or more derivative queries, wherein the corrected term is determined independent of a set of resources identified as being responsive to the original query; generating a corrected query that that includes and the corrected term substituted for the potentially inaccurate term; performing a search operation that uses the corrected query as input; and providing results of the search operation to the user device in response to the original query. 2. The method of claim 1 , wherein: determining that a term of the original query is a potentially inaccurate term comprises determining that the term is typographically incorrect; and generating one or more derivative queries from the original query comprises generating a derivative query that includes only the terms of the original query that are not the potentially inaccurate term.

10. A bitmask table for facilitating encoding communication data, the bitmask table being executed by a computer, the bitmask table comprising: a character number related to a character, the character number defining an index into the bitmask table; and a bitmask character value related to the character number, the bitmask character value identifying one or more character sets that include the character, wherein the bitmask character value is adjusted based on an origin language corresponding to the character when the origin language is a Chinese, Japanese, or Korean (CJK) language and wherein the adjusted bitmask character value excludes a character set for a non-origin CJK language that includes the character.

10. A bitmask table for facilitating encoding communication data, the bitmask table being executed by a computer, the bitmask table comprising: a character number related to a character, the character number defining an index into the bitmask table; and a bitmask character value related to the character number, the bitmask character value identifying one or more character sets that include the character, wherein the bitmask character value is adjusted based on an origin language corresponding to the character when the origin language is a Chinese, Japanese, or Korean (CJK) language and wherein the adjusted bitmask character value excludes a character set for a non-origin CJK language that includes the character. 12. A bitmask table as recited in claim 10 , wherein the character number is a Unicode character number.

1. One or more non-transitory computer-readable media storing computer-executable instructions executable by a processor, the media storing one or more instructions for: providing a graphical model including: a first entity associated with a first modeling domain, wherein the first modeling domain is one of a statechart domain, a time-based block diagram domain, a physical system domain, a data flow diagram domain, a unified modeling language domain, a discrete event modeling domain, or a compiled code domain, and a second entity associated with a second modeling domain, wherein the second modeling domain is of a different type than the first modeling domain and is one of the statechart domain, the time-based block diagram domain, the physical system domain, the data flow diagram domain, the unified modeling language domain, the discrete event modeling domain, or the compiled code domain; providing a programming interface to a debugger; transferring information associated with the first entity via the programming interface to the debugger after executing the first entity; generating a first domain-specific debugger view of the graphical model, the generated first domain-specific debugger view consistent with the first modeling domain, the generating first domain-specific debugger view being based on the transferring information associated with the first entity; transferring information associated with the second entity via the programming interface to the debugger after executing the second entity; generating a second domain-specific debugger view of the graphical model, the generated second domain-specific debugger view consistent with the second modeling domain, the generating the second domain-specific debugger being based on the transferring information associated with the second entity; displaying the first domain-specific debugger view on a display device, where the first domain-specific debugger view is displayed: after the first entity is executed, and using a first user interface element; automatically transitioning to an updated domain display, the transitioning occurring when the second entity is executing; and displaying the second domain-specific debugger view on the display device, where the second domain-specific debugger view is displayed: based on the transitioning, after the second entity is executed, and using a second user interface element.

1. One or more non-transitory computer-readable media storing computer-executable instructions executable by a processor, the media storing one or more instructions for: providing a graphical model including: a first entity associated with a first modeling domain, wherein the first modeling domain is one of a statechart domain, a time-based block diagram domain, a physical system domain, a data flow diagram domain, a unified modeling language domain, a discrete event modeling domain, or a compiled code domain, and a second entity associated with a second modeling domain, wherein the second modeling domain is of a different type than the first modeling domain and is one of the statechart domain, the time-based block diagram domain, the physical system domain, the data flow diagram domain, the unified modeling language domain, the discrete event modeling domain, or the compiled code domain; providing a programming interface to a debugger; transferring information associated with the first entity via the programming interface to the debugger after executing the first entity; generating a first domain-specific debugger view of the graphical model, the generated first domain-specific debugger view consistent with the first modeling domain, the generating first domain-specific debugger view being based on the transferring information associated with the first entity; transferring information associated with the second entity via the programming interface to the debugger after executing the second entity; generating a second domain-specific debugger view of the graphical model, the generated second domain-specific debugger view consistent with the second modeling domain, the generating the second domain-specific debugger being based on the transferring information associated with the second entity; displaying the first domain-specific debugger view on a display device, where the first domain-specific debugger view is displayed: after the first entity is executed, and using a first user interface element; automatically transitioning to an updated domain display, the transitioning occurring when the second entity is executing; and displaying the second domain-specific debugger view on the display device, where the second domain-specific debugger view is displayed: based on the transitioning, after the second entity is executed, and using a second user interface element. 12. The non-transitory computer-readable media of claim 1 wherein at least one of the first domain-specific debugger view or the second domain-specific debugger view includes a display of a call chain of methods that are called during an execution of the graphical model.

17. A non-transitory computer-readable medium storing a computer program having instructions for localizing display of applications for download, the instructions comprising: receiving, from a user device, a request to download a desired application, the desired application being localized in different languages; determining, by an online store, a local language associated with the user device, wherein determining the local language comprises: determining a plurality of factors associated with the user device, wherein each factor is associated with a secondary language and a weight; grouping the plurality of factors based on the secondary language to form a list of secondary languages; ranking the languages in the list of secondary languages according to the weight of each factor, and assigning a secondary language from the list of secondary languages as the local language according to the ranking; and presenting, by the online store, an interface to download a version of the desired application in the local language when the local language is one of the different languages.

17. A non-transitory computer-readable medium storing a computer program having instructions for localizing display of applications for download, the instructions comprising: receiving, from a user device, a request to download a desired application, the desired application being localized in different languages; determining, by an online store, a local language associated with the user device, wherein determining the local language comprises: determining a plurality of factors associated with the user device, wherein each factor is associated with a secondary language and a weight; grouping the plurality of factors based on the secondary language to form a list of secondary languages; ranking the languages in the list of secondary languages according to the weight of each factor, and assigning a secondary language from the list of secondary languages as the local language according to the ranking; and presenting, by the online store, an interface to download a version of the desired application in the local language when the local language is one of the different languages. 29. The non-transitory computer-readable medium of claim 17 , the instructions further comprising: identifying a conflict between two or more of the plurality of factors, wherein the weight associated with each of the two or more of the plurality of factors is based on the identified conflict.

6. The method recited in claim 1 , further comprising modifying the graphical mashup in response to a user input placing a third digital object in the working area proximate the graphical mashup.

6. The method recited in claim 1 , further comprising modifying the graphical mashup in response to a user input placing a third digital object in the working area proximate the graphical mashup. 7. The method recited in claim 6 , further comprising modifying the graphical mashup in response to a user input removing one of the first, second, or third digital objects from the working area.

4. The method of claim 1, wherein said determining geographic location step includes the step of analyzing received dialed digits.

4. The method of claim 1, wherein said determining geographic location step includes the step of analyzing received dialed digits. 5. The method of claim 4, wherein said received dialed digits include digits that alert a network that a calling party desires to make an international call, digits representing a dialed country code, and digits representing said called party's phone number.

1. A computer-implemented method performed by a data processing apparatus, the method comprising: receiving audio data that corresponds to an utterance; obtaining a first transcription of the utterance that was generated using a limited speech recognizer, wherein the limited speech recognizer comprises a speech recognizer that includes a language model that is trained over a limited speech recognition vocabulary that includes one or more terms from a voice command grammar, but that includes fewer than all terms of an expanded grammar; obtaining a second transcription of the utterance that was generated using an expanded speech recognizer, wherein the expanded speech recognizer comprises a speech recognizer that includes a language model that is trained over an expanded speech recognition vocabulary that includes all of the terms of the expanded grammar; aligning the first and second transcriptions of the utterance to generate an aligned transcription; and classifying the utterance, based at least on a portion of the aligned transcription, as a voice command or a voice query.

1. A computer-implemented method performed by a data processing apparatus, the method comprising: receiving audio data that corresponds to an utterance; obtaining a first transcription of the utterance that was generated using a limited speech recognizer, wherein the limited speech recognizer comprises a speech recognizer that includes a language model that is trained over a limited speech recognition vocabulary that includes one or more terms from a voice command grammar, but that includes fewer than all terms of an expanded grammar; obtaining a second transcription of the utterance that was generated using an expanded speech recognizer, wherein the expanded speech recognizer comprises a speech recognizer that includes a language model that is trained over an expanded speech recognition vocabulary that includes all of the terms of the expanded grammar; aligning the first and second transcriptions of the utterance to generate an aligned transcription; and classifying the utterance, based at least on a portion of the aligned transcription, as a voice command or a voice query. 7. The method of claim 1 , wherein the operations of at least one of the limited speech recognizer and the expanded speech recognizer are performed at a mobile device.

15. The system of claim 11 wherein the question formulation unit defines one or more characteristics associated with the uttered proper name; and formulates the indirect confirmation question by incorporating at least one characteristic of the one or more characteristics in the first machine response.

15. The system of claim 11 wherein the question formulation unit defines one or more characteristics associated with the uttered proper name; and formulates the indirect confirmation question by incorporating at least one characteristic of the one or more characteristics in the first machine response. 17. The system of claim 15 wherein the system generates a second machine response the user utterance including an additional indirect confirmation question related to the proper name, wherein the second machine response does not repeat the proper name, if the second confidence score is below the defined threshold value.

1. A method comprising: automatically observing user interaction with a particular web form; producing one or more URL fetch patterns corresponding to the particular web form based, at least in part, on the observed user interaction; generating a first URL, based, at least in part, on the one or more URL fetch patterns, wherein the first URL includes a first value for a particular parameter associated with the particular web form; automatically crawling the first URL to obtain a first record; automatically extracting business information from the first record; applying one or more tags, based on the business information, to an entry in a business listing database; generating a second URL, based, at least in part, on the one or more URL fetch patterns, wherein the second URL includes a second value for the particular parameter; wherein the second value is distinct from the first value; automatically crawling the second URL to obtain a second record; and automatically extracting second business information from the second record; wherein the method is performed by one or more computing devices.

1. A method comprising: automatically observing user interaction with a particular web form; producing one or more URL fetch patterns corresponding to the particular web form based, at least in part, on the observed user interaction; generating a first URL, based, at least in part, on the one or more URL fetch patterns, wherein the first URL includes a first value for a particular parameter associated with the particular web form; automatically crawling the first URL to obtain a first record; automatically extracting business information from the first record; applying one or more tags, based on the business information, to an entry in a business listing database; generating a second URL, based, at least in part, on the one or more URL fetch patterns, wherein the second URL includes a second value for the particular parameter; wherein the second value is distinct from the first value; automatically crawling the second URL to obtain a second record; and automatically extracting second business information from the second record; wherein the method is performed by one or more computing devices. 5. The method of claim 1 , wherein the business listing database is constructed using a web crawler.

13. A computer storage medium encoded with a computer program, the program comprising instructions that when executed by data processing apparatus cause the data processing apparatus to perform operations comprising: identifying one or more queries that were received with a reference to a given factual entity, wherein the one or more queries identified for the given factual entity are different from one or more queries identified for one or more other factual entities; identifying one or more resources related to the given query; obtaining search results that are responsive to a received query; determining that the given factual entity is referenced by the received query; identifying a type of entity for the given factual entity; identifying, from a set of different knowledge panel templates, a knowledge panel template specified for the type of entity, the identified knowledge panel template including placeholders for content relevant to the type of entity; selecting, from the one or more resources related to the given factual entity, content for display in a knowledge panel for the given factual entity, the selected content including a first content item obtained from a first resource and a second content item obtained from a second resource different than the first resource, each given content item of the selected content being selected based on a number of the one or more received queries that reference both (i) the given factual entity and (ii) information presented by the given content item; generating the knowledge panel for the given factual entity by populating the placeholders of the identified knowledge panel template with the selected content; and providing data that causes the identified search results and the knowledge panel to be presented on a search results page, the knowledge panel presenting the selected content in a knowledge panel area alongside at least a portion of the search results.

13. A computer storage medium encoded with a computer program, the program comprising instructions that when executed by data processing apparatus cause the data processing apparatus to perform operations comprising: identifying one or more queries that were received with a reference to a given factual entity, wherein the one or more queries identified for the given factual entity are different from one or more queries identified for one or more other factual entities; identifying one or more resources related to the given query; obtaining search results that are responsive to a received query; determining that the given factual entity is referenced by the received query; identifying a type of entity for the given factual entity; identifying, from a set of different knowledge panel templates, a knowledge panel template specified for the type of entity, the identified knowledge panel template including placeholders for content relevant to the type of entity; selecting, from the one or more resources related to the given factual entity, content for display in a knowledge panel for the given factual entity, the selected content including a first content item obtained from a first resource and a second content item obtained from a second resource different than the first resource, each given content item of the selected content being selected based on a number of the one or more received queries that reference both (i) the given factual entity and (ii) information presented by the given content item; generating the knowledge panel for the given factual entity by populating the placeholders of the identified knowledge panel template with the selected content; and providing data that causes the identified search results and the knowledge panel to be presented on a search results page, the knowledge panel presenting the selected content in a knowledge panel area alongside at least a portion of the search results. 20. The computer storage medium of claim 13 , wherein the given factual entity comprises a person, and wherein the identified knowledge panel template includes a placeholder for each of an image of the person, a description of the person, and at least one fact about the person.

1. A method comprising: establishing, via at least one host server, a video conference session between a plurality of meeting clients, each meeting client comprising a computing device that communicates via a network with the at least one host server and computing devices of other meeting clients; establishing a presenter for the video conference session, the presenter comprising at least one of the meeting clients; providing audio and video content generated from the presenter to other meeting clients within a base room of the video conference session, wherein the presenter provides audio content to the base room in a first language; assigning a sub-presenter to a private room associated with the video conference session; providing audio and video content generated from the presenter to the sub-presenter; facilitating control of audio and video content provided to the private room by the sub-presenter, wherein the sub-presenter generates and provides audio content along with video content generated from the presenter to the private room, the audio content generated by the sub-presenter comprising an audio broadcast of a translation of the audio content in the first language into a second language, wherein the facilitating control of the audio and video content provided to the private room by the sub-presenter comprises: facilitating control by the sub-presenter of a time lag for the video content provided in the private room by providing the sub-presenter with a graphical user interface that allows the sub-presenter to perform at least one of pausing, slowing down and speeding up of the video content generated from the presenter to allow the sub-presenter to sync the video content with the audio content translated into the second language and being provided to the private room; and in response to at least one meeting client selecting a translation of the audio content from the presenter and in the first language into the second language, assigning the at least one meeting client selecting the second language in which to receive audio content to the private room such that the at least one meeting client receives audio and video content from the sub-presenter.

1. A method comprising: establishing, via at least one host server, a video conference session between a plurality of meeting clients, each meeting client comprising a computing device that communicates via a network with the at least one host server and computing devices of other meeting clients; establishing a presenter for the video conference session, the presenter comprising at least one of the meeting clients; providing audio and video content generated from the presenter to other meeting clients within a base room of the video conference session, wherein the presenter provides audio content to the base room in a first language; assigning a sub-presenter to a private room associated with the video conference session; providing audio and video content generated from the presenter to the sub-presenter; facilitating control of audio and video content provided to the private room by the sub-presenter, wherein the sub-presenter generates and provides audio content along with video content generated from the presenter to the private room, the audio content generated by the sub-presenter comprising an audio broadcast of a translation of the audio content in the first language into a second language, wherein the facilitating control of the audio and video content provided to the private room by the sub-presenter comprises: facilitating control by the sub-presenter of a time lag for the video content provided in the private room by providing the sub-presenter with a graphical user interface that allows the sub-presenter to perform at least one of pausing, slowing down and speeding up of the video content generated from the presenter to allow the sub-presenter to sync the video content with the audio content translated into the second language and being provided to the private room; and in response to at least one meeting client selecting a translation of the audio content from the presenter and in the first language into the second language, assigning the at least one meeting client selecting the second language in which to receive audio content to the private room such that the at least one meeting client receives audio and video content from the sub-presenter. 3. The method of claim 1 , wherein the facilitating control of audio and video content provided to the private room further comprises: providing video content generated by the presenter at a time lag within the private room in relation to the same video content being provided within the base room.

11. A method comprising: displaying message text in a message received at an apparatus over a communications network; receiving an input at the apparatus indicating a first keyword of the displayed message text; identifying, by a controller in the apparatus, a category associated with the first keyword, the identified category being one of a plurality of categories of keywords, each category associated with an operation and with at least one keyword; and performing the operation associated with the identified category to generate a response message including information corresponding to the first keyword; and transmitting the response message from the apparatus over the communications network.

11. A method comprising: displaying message text in a message received at an apparatus over a communications network; receiving an input at the apparatus indicating a first keyword of the displayed message text; identifying, by a controller in the apparatus, a category associated with the first keyword, the identified category being one of a plurality of categories of keywords, each category associated with an operation and with at least one keyword; and performing the operation associated with the identified category to generate a response message including information corresponding to the first keyword; and transmitting the response message from the apparatus over the communications network. 19. A method according to claim 11 , wherein the association of the at least one keyword with a category is based on a semantic meaning of the keyword.

1. A document processing system comprising: a unique information acquiring section that acquires unique information to be recorded; a similar document element selecting section that selects one similar document element corresponding to the unique information from each of one or a plurality of groups of similar document elements which relate to one or a plurality of document elements respectively, the one or the plurality of document elements contained in document data; a replacement document data acquiring section that acquires replacement document data which is generated by replacing the document elements with the respective similar document elements selected by the similar document element selecting section; a position information acquiring section that acquires a piece or pieces of position information indicating a position or positions of the one or the plurality of document elements contained in the document data; and a registering section that registers the groups of similar documents elements in a storage unit in such a manner that the pieces of position information acquired by the position information acquiring section are correlated with the document elements respectively, wherein the one or the plurality of document elements are selected by a user, and each of the one or the plurality of document elements in a word contained in the document data, wherein the registering section registers the groups of similar document elements in such a manner that similar document element numbers starting from 0 are correlated, in number order, with the respective similar document elements of each group, and where the registering section registers the document elements in number order as a table form with weights correlated with the respective document elements in such a manner that a weight corresponding to a first document element is 1 and a weight corresponding to an Nth document element is obtained by multiplying a weight of an (N −1)th document element by a number of similar document elements of a group of similar document elements which relates to the (N−1)th document element, when N is an integer that is larger than or equal to 2.

1. A document processing system comprising: a unique information acquiring section that acquires unique information to be recorded; a similar document element selecting section that selects one similar document element corresponding to the unique information from each of one or a plurality of groups of similar document elements which relate to one or a plurality of document elements respectively, the one or the plurality of document elements contained in document data; a replacement document data acquiring section that acquires replacement document data which is generated by replacing the document elements with the respective similar document elements selected by the similar document element selecting section; a position information acquiring section that acquires a piece or pieces of position information indicating a position or positions of the one or the plurality of document elements contained in the document data; and a registering section that registers the groups of similar documents elements in a storage unit in such a manner that the pieces of position information acquired by the position information acquiring section are correlated with the document elements respectively, wherein the one or the plurality of document elements are selected by a user, and each of the one or the plurality of document elements in a word contained in the document data, wherein the registering section registers the groups of similar document elements in such a manner that similar document element numbers starting from 0 are correlated, in number order, with the respective similar document elements of each group, and where the registering section registers the document elements in number order as a table form with weights correlated with the respective document elements in such a manner that a weight corresponding to a first document element is 1 and a weight corresponding to an Nth document element is obtained by multiplying a weight of an (N −1)th document element by a number of similar document elements of a group of similar document elements which relates to the (N−1)th document element, when N is an integer that is larger than or equal to 2. 5. The document processing system according to claim 1 , wherein the unique information includes information which corresponds the weights correlated with the respective document elements and the similar document element numbers of the respective selected similar document elements.

6. The method of claim 1 , further comprising determining, for the particular user, one or more measurements of similarity between titles of the plurality of titles based on the plurality of statistical models and the plurality of weight values, wherein the recommended titles are identified based on the one or more measurements of similarity.

6. The method of claim 1 , further comprising determining, for the particular user, one or more measurements of similarity between titles of the plurality of titles based on the plurality of statistical models and the plurality of weight values, wherein the recommended titles are identified based on the one or more measurements of similarity. 8. The method of claim 6 , further comprising correcting the one or more measurements of similarity for one or more of title popularity or noise.

6. The method of claim 4 , further comprising: receiving, by the processing device, a user selection of one of the new application cards; launching, by the processing device, a native application indicated by an access mechanism of the set of access mechanisms; and setting, by the processing device, the native application to a state indicated by the access mechanism.

6. The method of claim 4 , further comprising: receiving, by the processing device, a user selection of one of the new application cards; launching, by the processing device, a native application indicated by an access mechanism of the set of access mechanisms; and setting, by the processing device, the native application to a state indicated by the access mechanism. 7. The method of claim 6 , wherein the access mechanism is a script.

1. A method comprising: receiving, by a computing system having at least one processor, a first request to analyze a first network document, the first request including an identifier of the first network document; in response to the first request: analyzing, by the computing system, the first network document according to one or more search engine algorithms; generating a display of a first scoring analysis including a first score of the first network document; and generating a display of an option to view a second scoring analysis of a second network document within the first network document, wherein the second network document includes at least one link contributing to the first scoring analysis; in response to receiving a selection of the option to view the second scoring analysis, generating a display of results of the second scoring analysis including a second score of the second network document, wherein the display of the results of the second scoring analysis includes a link flow distribution that indicates a likelihood that a user will access the second network document relative to a third network document within the first network document, and wherein the display of the results of the first scoring analysis includes at least one factor contributing to the first score and wherein the display of the results of the second scoring analysis does not include the at least one factor contributing to the first score; receiving a second request to view a third scoring analysis of the at least one link, wherein the third scoring analysis includes an evaluation of one or more traffic-independent attributes of the at least one link, wherein the one or more traffic-independent attributes of the at least one link is different from attributes of a network destination specified by the at least one link; and in response to receiving the second request, generating a display of results of the third scoring analysis of the at least one link including the one or more traffic-independent attributes.

1. A method comprising: receiving, by a computing system having at least one processor, a first request to analyze a first network document, the first request including an identifier of the first network document; in response to the first request: analyzing, by the computing system, the first network document according to one or more search engine algorithms; generating a display of a first scoring analysis including a first score of the first network document; and generating a display of an option to view a second scoring analysis of a second network document within the first network document, wherein the second network document includes at least one link contributing to the first scoring analysis; in response to receiving a selection of the option to view the second scoring analysis, generating a display of results of the second scoring analysis including a second score of the second network document, wherein the display of the results of the second scoring analysis includes a link flow distribution that indicates a likelihood that a user will access the second network document relative to a third network document within the first network document, and wherein the display of the results of the first scoring analysis includes at least one factor contributing to the first score and wherein the display of the results of the second scoring analysis does not include the at least one factor contributing to the first score; receiving a second request to view a third scoring analysis of the at least one link, wherein the third scoring analysis includes an evaluation of one or more traffic-independent attributes of the at least one link, wherein the one or more traffic-independent attributes of the at least one link is different from attributes of a network destination specified by the at least one link; and in response to receiving the second request, generating a display of results of the third scoring analysis of the at least one link including the one or more traffic-independent attributes. 8. The method of claim 1 , further comprising receiving a third request to generate a report including scoring analyses for a plurality of network documents within the first network document.

16. A data analysis system for performing a data analysis task having an associated analysis context, the system comprising: a processor configured to define the analysis context associated with the data analysis task, wherein data items have a variable relevance in accordance with the context, and wherein data items are grouped together to form logical transactions conducted by a user over a predetermined time period; a network interface configured to receive the logical transactions exchanged over a communication network; the processor configured to define a plurality of logical transactions based on one or more transaction criteria, wherein each logical transaction of the plurality of logical transactions comprise at least two data items of the plurality of data items, process the logical transactions with a set of prioritization rules to generate relevance scores that quantify a relevance of the logical transactions to the analysis context, wherein the relevance scores are assigned according to a common scale, and generate a prioritization of the logical transactions based on the relevance scores.

16. A data analysis system for performing a data analysis task having an associated analysis context, the system comprising: a processor configured to define the analysis context associated with the data analysis task, wherein data items have a variable relevance in accordance with the context, and wherein data items are grouped together to form logical transactions conducted by a user over a predetermined time period; a network interface configured to receive the logical transactions exchanged over a communication network; the processor configured to define a plurality of logical transactions based on one or more transaction criteria, wherein each logical transaction of the plurality of logical transactions comprise at least two data items of the plurality of data items, process the logical transactions with a set of prioritization rules to generate relevance scores that quantify a relevance of the logical transactions to the analysis context, wherein the relevance scores are assigned according to a common scale, and generate a prioritization of the logical transactions based on the relevance scores. 19. The system of claim 16 wherein the plurality of data items comprises a web page, an e-mail message, a file transfer session, an instant messaging chat session, and a textual transcript of a telephone call.

8. A computer program product comprising a computer readable medium including one or more computer readable instructions stored thereon that, when executed by a computer, cause the computer to automatically generate a naturally reading narrative product summary including assertions about a specific product selected by a user, said computer readable instructions comprising: instructions for receiving the selection of the specific product from the user, the specific product associated with a plurality of attributes; instructions for determining at least one attribute of the plurality of attributes for comparison for the specific product selected by the user and selecting a comparable product based on the at least one attribute, the comparable product being the product having a high value rating for the at least one attribute; and instructions for generating the naturally reading narrative product summary including assertions about the specific product selected by the user and a recommendation of the comparable product.

8. A computer program product comprising a computer readable medium including one or more computer readable instructions stored thereon that, when executed by a computer, cause the computer to automatically generate a naturally reading narrative product summary including assertions about a specific product selected by a user, said computer readable instructions comprising: instructions for receiving the selection of the specific product from the user, the specific product associated with a plurality of attributes; instructions for determining at least one attribute of the plurality of attributes for comparison for the specific product selected by the user and selecting a comparable product based on the at least one attribute, the comparable product being the product having a high value rating for the at least one attribute; and instructions for generating the naturally reading narrative product summary including assertions about the specific product selected by the user and a recommendation of the comparable product. 14. The computer program product of claim 8 , wherein said selected comparable product is ranked within a predetermined near-rank margin of said user selected product.

9. A system comprising: a processor; a computer readable storage medium storing processor-executable computer program instructions, the computer program instructions comprising instructions for: characterizing multiple items of content accessed over a network, each item of content associated with a content descriptor and characterized with a set of multiple depersonalized keywords, the set comprising words submitted to an online search engine by a population comprising multiple entities in the past to locate the content; recording, at a keyword mapping system, a consumption history of a specified entity, the consumption history comprising at least two different content descriptors, each content descriptor associated with a respective item of content accessed over the network by the specified entity; selecting, from the recorded consumption history, at least two content descriptors which have been stored for longer than a specified timeframe; converting the recorded consumption history to a protected consumption history by representing each selected content descriptor with the set of multiple depersonalized keywords which characterize the respective selected content descriptor's item of content; removing the selected content descriptors from the protected consumption history; receiving a bid request from an advertising exchange, the bid request comprising an identifier of the specified entity; determining a frequency of each specified keyword from a list of specified keywords in the depersonalized keywords of the protected consumption history; configuring a bid response by assessing the suitability of the specified entity to receive advertising content based at least in part on the frequencies; and sending the bid response to the advertising exchange.

9. A system comprising: a processor; a computer readable storage medium storing processor-executable computer program instructions, the computer program instructions comprising instructions for: characterizing multiple items of content accessed over a network, each item of content associated with a content descriptor and characterized with a set of multiple depersonalized keywords, the set comprising words submitted to an online search engine by a population comprising multiple entities in the past to locate the content; recording, at a keyword mapping system, a consumption history of a specified entity, the consumption history comprising at least two different content descriptors, each content descriptor associated with a respective item of content accessed over the network by the specified entity; selecting, from the recorded consumption history, at least two content descriptors which have been stored for longer than a specified timeframe; converting the recorded consumption history to a protected consumption history by representing each selected content descriptor with the set of multiple depersonalized keywords which characterize the respective selected content descriptor's item of content; removing the selected content descriptors from the protected consumption history; receiving a bid request from an advertising exchange, the bid request comprising an identifier of the specified entity; determining a frequency of each specified keyword from a list of specified keywords in the depersonalized keywords of the protected consumption history; configuring a bid response by assessing the suitability of the specified entity to receive advertising content based at least in part on the frequencies; and sending the bid response to the advertising exchange. 12. The system of claim 9 wherein configuring the bid response comprises: selecting an advertising campaign which is suitable for the specified entity.

7. A decoder comprising: a module that decodes a predetermined code from a first stream that is associated with an animation mimic within a second stream; and a module that starts an animation using the animation mimic based on the decoded predetermined code.

7. A decoder comprising: a module that decodes a predetermined code from a first stream that is associated with an animation mimic within a second stream; and a module that starts an animation using the animation mimic based on the decoded predetermined code. 10. The decoder of claim 7 , wherein a correspondence between the predetermined code and the animation mimic is established during an encoding process of the first stream.

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.

17. The method of claim 16 , wherein the set of nodes includes at least one of a leaf node that is a standalone node with no child node and a nugget node that includes one or more child nodes, each child node being a nugget or leaf node.

17. The method of claim 16 , wherein the set of nodes includes at least one of a leaf node that is a standalone node with no child node and a nugget node that includes one or more child nodes, each child node being a nugget or leaf node. 18. The method of claim 17 , wherein the child node in the nodal structure represents a next operation following an operation of the nugget node, wherein the next operation is conditioned on a determined value of the nugget node when executed in the linked list.

13. The method of claim 11 , further comprising: by one or more computing devices, generating an example data point, wherein the data point comprises one of a positive example or a negative example for the user features of the first user and the content features of the content item; and by one or more computing devices, updating the set of weights of the respective recommendation model based on the example data point.

13. The method of claim 11 , further comprising: by one or more computing devices, generating an example data point, wherein the data point comprises one of a positive example or a negative example for the user features of the first user and the content features of the content item; and by one or more computing devices, updating the set of weights of the respective recommendation model based on the example data point. 14. The method of claim 13 , wherein: the example data point is a positive example when a viewing user having the user features of the first user fails to convert on a viewed content item having the content features of the content item after an impression; and the example data point is a negative example when the viewing user having the user features of the first user converts on the viewed content item having the content features of the content item after the impression.

1. An emotion recognition apparatus comprising: at least one processor; a sampling unit of the at least one processor configured to extract, from input data for emotion recognition, sampling data corresponding to a time period, wherein the input data comprises a facial image of a user, a voice of the user, text input by the user, a temperature of the user, a location of the user, or a kind of an application being used by the user; a data segment creator of the at least one processor configured to accumulatively segment the sampling data, based on one or more predetermined time-domain windows, to form a plurality of data segments, wherein each of the data segments subsequent to a first data segment, among the plurality of data segments, includes portions of the sampling data from a current sampling time period and all previous sampling time periods; an emotional segment creator of the at least one processor configured to create a plurality of emotional segments that comprise a plurality of emotions corresponding to each of the respective data segments; and an emotion deciding unit of the at least one processor comprising a complex emotion deciding unit configured to decide at least two of the emotional segments as the user's complex emotion.

1. An emotion recognition apparatus comprising: at least one processor; a sampling unit of the at least one processor configured to extract, from input data for emotion recognition, sampling data corresponding to a time period, wherein the input data comprises a facial image of a user, a voice of the user, text input by the user, a temperature of the user, a location of the user, or a kind of an application being used by the user; a data segment creator of the at least one processor configured to accumulatively segment the sampling data, based on one or more predetermined time-domain windows, to form a plurality of data segments, wherein each of the data segments subsequent to a first data segment, among the plurality of data segments, includes portions of the sampling data from a current sampling time period and all previous sampling time periods; an emotional segment creator of the at least one processor configured to create a plurality of emotional segments that comprise a plurality of emotions corresponding to each of the respective data segments; and an emotion deciding unit of the at least one processor comprising a complex emotion deciding unit configured to decide at least two of the emotional segments as the user's complex emotion. 11. The emotion recognition apparatus of claim 1 , wherein the emotion deciding unit further comprises a changed emotion deciding unit configured to decide a time-ordered arrangement of the emotional segments as the changed emotion.

9. A display controller as in claim 8 further comprising strand means for arranging said child documents in accordance with said parent document attributes such that said child document display outlines are displayed in an ordered sequence within the workspace.

9. A display controller as in claim 8 further comprising strand means for arranging said child documents in accordance with said parent document attributes such that said child document display outlines are displayed in an ordered sequence within the workspace. 11. A display controller as in claim 9 wherein said ordered sequence comprises a staggered overlapping arrangement such that at least some of said child documents are rendered in a piled configuration within the workspace.