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1. A computer implemented method for assigning codes from a standard lexicon to a free text document describing physical or tangible objects, the method comprising the steps of: (a) automatically segmenting said free text document into a plurality of sentences; (b) using a computer processor to retrieve a plurality of propositions by matching said sentences in a semantic mapping table created by domain experts through semantically annotating sentences from a corpus of related documents in a knowledge domain to propositions, (c) using a computer processor to retrieve a plurality of codes in a standard lexicon by matching said propositions to said codes created by a third party, in a code mapping table created by domain experts by annotating said propositions to said codes; wherein one or more of said matching codes from said standard lexicon represents at least a portion of the semantic content of said free text document.

4. The method according to claim 1 further comprising a step of validation prior to step c.

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8. The system of claim 1 , wherein the monitor examines the new geo-tagged items for at least one of the type of item being listed or the seller of the item.

10. The system of claim 8 , wherein the notification component alerts the user when at least one geo-tag preference and at least one seller preference are satisfied.

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10. The method of claim 1 , further comprising: monitoring sources of user content; retrieving user content identified in the monitored sources; extracting knowledge pertaining to the user from the retrieved content; and using the extracted knowledge for subsequent response generation.

11. The method of claim 10 , wherein the sources of user content comprise one or more of: instant messaging content; email content; speech-to-text content; blog content; or online content identified as of interest to the user.

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4. The method of claim 1 , wherein generating the output hierarchical data structure comprises modifying content of the input hierarchical data structure by modifying a key in the input hierarchical data structure by changing the name, value, or value type of the key.

1. A method of transforming an input hierarchical data structure to an output hierarchical data structure by using a transformation template that comprises a plurality of transformation entries, wherein the plurality of transformation entries each comprise at least one matching condition and at least one transformation action, the method comprising: receiving the input hierarchical data structure comprising a plurality of input entries, each input entry comprising input data that comprises at least one key value pair, wherein a key value pair includes a key and an associated value for the key; creating a temporary data structure comprising a plurality of default key value pairs; for each particular input entry in the received input hierarchical data structure that matches a particular transformation entry in the transformation template: based on the input data of the particular input entry, determining whether a matching condition of the particular transformation entry is met; and when the matching condition is met, using at least one particular key value pair of the particular input entry to define an associated key value pair in the temporary data structure based on the transformation action of the particular transformation entry, wherein the associated key value pair overwrites a default value for a default key when the associated key value pair's key matches a default key in the temporary data structure; and generating the output hierarchical data structure by extracting key value pairs from the temporary data structure, wherein extracting comprises defining in the output hierarchical data structure a key value pair for each default key value pair in the temporary storage structure that does not get overwritten by key value pairs of any of the particular input entries that matches a particular transformation entry, said output hierarchical data structure being stored in a computer readable medium.

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18. The system of claim 1 wherein said first probability is the range of from about 0.8 to about 1.0.

1. A system for package fill determination comprising a system controller; a data storage element; a historical data set acquisition element; a sampling manufacturing period acquisition element; a variance model acquisition element; a estimation technique acquisition element; a variance component set acquisition element; a first constraint set acquisition element; a first probability acquisition element; and a product data set acquisition element; wherein said system controller: (i) obtains a historical data set via said historical data set acquisition element; (ii) obtains a sampling manufacturing period via said sampling manufacturing period acquisition element; (iii) obtains a variance model via said variance model acquisition element; (iv) obtains an estimation technique via said estimation technique acquisition element, wherein steps (i), (ii), (iii) and (iv) may be conducted in any order; (v) determines a variance component set, acquired via said variance component set acquisition element, using said variance model, said estimation technique, and said historical data set; and (vi) determines a first target by the method comprising the steps of: a) obtaining a first constraint set via said first constraint set acquisition element; b) obtaining a first probability via said first probability selection element; c) obtaining a product data set via said product data set acquisition element, wherein steps (a), (b) and (c) may be conducted in any order; and d) calculating said first target utilizing said variance component set such that the probability of satisfying said first constraint set is at least as great as said first probability.

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1. A method for context-aware digital play, the method comprising: accessing a virtual scene, the virtual scene comprising digital content; determining that the digital content of the virtual scene comprises a representation of at least one of a plurality of characters, each of the characters being associated with rules, wherein the rules associated with each of the characters are pre-set prior to accessing the virtual scene, do not change during the context-aware digital play, and are related to a known behavior of the character with which the rule is associated; accessing an indication of one or more context-based stimuli, the context-based stimuli being based on the virtual scene; determining whether one or more of the rules associated with the character are also associated with the one or more context-based stimuli that are associated with the accessed indication; and if one or more of the rules are determined to be associated with the context-based stimuli, applying the context-based stimuli to the virtual scene by updating the digital content of the virtual scene based on the one or more rules and modifying the representation of the character based on the one or more rules.

5. The method of claim 1 , further comprising: generating a command signal based on the modified representation of the character, the command signal being sufficient to cause a perceivable change in a physical object, and providing the command signal to the physical object such that the physical object performs a perceivable action determined by the command signal.

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7. A method as recited in claim 6, including the further step of performing a consistency check of results of said identifying step.

13. A method as recited in claim 7, wherein identifying step includes identifying said speaker based on codebooks each formed from a set of clustered feature vectors corresponding to a respective one of said plurality of speakers, said stored representation of speech signals including one of said codebooks, and wherein said consistency check includes: determining whether a feature vector count for one of said codebooks corresponding to the speaker identified in said identifying step meets predetermined criteria; and verifying the speaker identified in said identifying step if said feature vector count meets said predetermined criteria, and further analyzing said codebooks to determine a correct speaker if said feature vector count does not meet said predetermined criteria.

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6. The method as recited in claim 1 , wherein sensitivity categorization is performed using a linear SVM (Support Vector Machines) function.

1. A method for sensitivity categorization of web pages, the method comprising: (a) identifying a space of sensitive pages based on a sensitivity categorization of a first plurality of web pages and a second plurality of web pages, the first plurality of web pages being obtained from search queries using known sensitive words, the second plurality of web pages being randomly selected web pages; (b) identifying a third plurality of web pages that includes web pages on or near a boundary between the space of sensitive pages and a space without sensitive pages; (c) redefining the space of sensitive pages based on a sensitivity categorization of the first, second, and third pluralities of web pages; (d) determining that a given web page is sensitive when the given web page is in the space of sensitive pages; and (e) including the given web page in a marketing operation when the given web page is determined to be not sensitive and discarding the given web page for the marketing operation otherwise, wherein operations (a)-(e) are executed by a processor.

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6. The method of claim 5 , wherein the score assigned to the one or more query term optionalization rules that are specific to the particular query term is based on a ratio of (i) the click count to (ii) the click count and the skip count.

5. The method of claim 2 , comprising assigning a score to the one or more query term optionalization rules that are specific to the particular query term based at least on the click count and the skip count.

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3. The process of claim 1 , further comprising: receiving a written input from the user; determining a language of the written input; and providing a language indicator to the server based on the determined language indicating the language of the written input.

1. A computer-implemented input-method editor process comprising: receiving, at an electronic device, a request from a user of the electronic device for an application-independent input method editor having written and spoken input capabilities, wherein the application-independent input method editor is configured to receive input for a plurality of applications executable by the electronic device; receiving a spoken input from the user using the application-independent input method editor, wherein the spoken input corresponds to an input to an application from the plurality of applications; determining a category for the application; providing the spoken input and data that indicates the application category to a server, wherein the server includes a speech recognition system configured to select one or more language models to generate text based on the spoken input, wherein the one or more language models are selected based on the data that indicates the application category; receiving text from the server, wherein the text represents a transcription of the spoken input; and providing the text as the input to the application.

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21. The system of claim 20 , wherein the processor is further configured to de-obfuscate the PDF file.

20. A system, comprising: a processor configured to: determine that a PDF file does not include script stream data, wherein the PDF file is known to include malicious content; determine an identified cross-reference table from a plurality of cross-reference tables within the PDF file, wherein the identified cross-reference table is identified from the plurality of cross-reference tables based at least in part on a position of the identified cross-reference table relative to respective positions associated with one or more cross-reference tables other than the identified cross-reference table from the plurality of cross-reference tables; and automatically generate a signature for the PDF file from the identified cross-reference table; and a memory coupled to the processor and configured to provide the processor with instructions.

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20. The method of claim 19 , including generating the secured directory structure within which to store the user-submitted publication data, the secured directory structure being distinct from the database.

19. A method to verify data to be published by a computer system, the method including: storing multiple instances of publication information, including publication data, in a database associated with the computer system; retrieving user-submitted publication data from the database; storing the retrieved user-submitted publication data in a secured directory structure; subjecting the retrieved user-submitted publication data to a verification process, the verification process performed on the computer system and adapted to verify that the user-submitted publication data is unassociated with malicious executable code that is executable when the user-submitted publication data is rendered; setting a flag in the database indicating whether the user-submitted publication data is unassociated with malicious executable code; and selectively publishing the user-submitted publication data from the computer system to a client system based on the flag in the database.

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16. A computer-implemented method for building a user profile for a user, the method comprising: labeling and storing, with a computing device, user registration information in a database as a set of demographic nouns; determining, with a computing device, a composite set of taxonomic nouns, the composite set of taxonomic nouns representing the user profile based upon taxonomic nouns, the taxonomic nouns being prepared by one or more of the following steps: analyzing, with a computing device, author-generated classification information regarding at least one document of a set of documents and assigning a set of first taxonomic nouns to characterize the user based upon the author-generated classification information; examining, with a computing device, a user-generated tag from a client computer and characterizing the user of at least one document of the set of documents and assigning a set of second taxonomic nouns to characterize the user based upon the user-generated tag characterization; identifying, with a computing device, a method by which the user accessed at least one document of the set of documents from a content provider and assigning a set of third taxonomic nouns to characterize the user based upon the search term; evaluating, with a computing device, attributes that are related to the method of access and assigning a set of fourth taxonomic nouns to characterize the user based upon the attributes related to the method of access; and processing, with a computing device, at least one document of the set of documents to extract a set of fifth taxonomic nouns to characterize the user; building, with a computing device, the user profile, the user profile based upon the composite set of taxonomic nouns, the author-generated classification information, and at least one of the demographic nouns; comparing, with a computing device, the composite set of taxonomic nouns with taxonomic nouns associated with a plurality of other user profiles corresponding to a plurality of other users; and modifying, with a computing device, the user profile based on the comparison.

19. The computer-implemented method for building a user profile of claim 16 , wherein the attributes related to the method of access include at least one of a network type used to access the document, a Web site from which the document was accessed, an electronic correspondence to which the document was associated, a time of day the document was accessed, a referrer who directed the user to the document, and a user category used by a database storing the document to describe the user.

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4. An information search supporting data processing system that includes a processor and memory comprising: a morpheme analysis section which performs morpheme analysis on a natural language sentence; a syntactic and semantic analysis section which performs syntactic analysis on the natural language sentence processed by said morpheme analysis section and further performs semantic analysis on the natural language sentence; and a conversion execution section which converts the natural language sentence processed by said syntactic and semantic analysis section into an inquiry sentence described in an ontology description language to be provided to a search engine by referring to a case frame ontology dictionary in which are stored information indicating to which property in an ontology does a relationship among a predicate, a subject and an object in the natural language sentence correspond; wherein said conversion execution section generates said inquiry sentence by examining, on the basis of said case frame ontology dictionary, a property to which a predicate of said natural language sentence processed by said syntactic and semantic analysis section corresponds and a case frame to which the natural language sentence corresponds and by describing the obtained property and case frame in said ontology description language.

6. The information search supporting data processing system according to claim 4 , wherein said syntactic and semantic analysis section decomposes the natural language sentence on the basis of a relationship among a predicate included in said natural language sentence, a nominative case related to the predicate, and an objective case which is a word acting on the nominative case.

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27. The system of claim 25 , wherein the vendor-specific information element comprises at least an element ID field, a length field, an organizationally unique identifier field, and a data field.

25. The system of claim 21 , wherein embedding the character set information comprises embedding the character set information in a vendor-specific information element.

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9. The article of manufacture of claim 7 , receiving into memory an indication of a selection from the final list of candidate strings.

7. An article of manufacture comprising: a computer storage medium; computer program instructions stored on the computer storage medium which, when processed by a processing device, instruct the processing device to perform a process comprising: receiving an input string of characters into memory; accessing an input history, the input history comprising a plurality of strings of characters previously used and stored in memory, generating in memory an initial list of candidate strings, the candidate strings being strings in the input history having a prefix that matches the input string; selecting M most recent candidate strings and N most frequent candidate strings from the initial list of candidate strings, to create a secondary list of candidate strings; sorting the secondary list of candidate strings by recency in the input history to provide a sorted list of candidate strings; altering the sorted list of candidate strings to provide a final list of candidate strings, such that strings are dissimilar in a set of strings at a beginning of the final list of candidate strings; and presenting the final list of candidate strings for selection in place of the input string.

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27. The system of claim 26 , wherein the instructions further cause: animating the graphical element on the display of the first device.

26. The system of claim 25 , wherein the second modification is associated with a graphical element of the document.

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8. A method for a communication device comprising a microphone, a speaker, an input device, a display, and an antenna, said method comprising: a function implementing step in which one or more specific functions are implemented; wherein said communication device implements a voice communicating function and a multiple language function; wherein a voice communication is implemented by utilizing said microphone and said speaker when said voice communicating function is implemented in said step; and wherein a 1st language data and a 2nd language data are pre-stored in said communication device, wherein said 1st language data indicates a 1st language and said 2nd language data indicates a 2nd language, wherein said communication device functions under a 1st language mode or a 2nd language mode when said multiple language function is implemented in said step, wherein said multiple language function is the functionality enabling the user to manipulate said communication device with the user's preferred language by displaying the user interface with the language selected by the user, wherein when said 1st language mode is selected by the user, a 1st language identification which indicates said 1st language mode is registered in a data storage area and a first command which is the command for the user to manipulate said communication device in a first manner and a second command which is the command for the user to manipulate said communication device in a second manner are displayed by utilizing said 1st language data, wherein when said 2nd language mode is selected by the user, a 2nd language identification which indicates said 2nd language mode is registered in said data storage area and said first command which is the command for the user to manipulate said communication device in said first manner and said second command which is the command for the user to manipulate said communication device in said second manner are displayed by utilizing said 2nd language data, and wherein when said communication device is powered on after being powered off, the language identification stored in said data storage area is identified and said communication device automatically implements the language mode indicated by the language identification stored in said data storage area, and said first command and said second command after said communication device is powered on are automatically displayed with said 1st language if said 1st language mode was selected by the user before said communication device was powered off, and said first command and said second command after said communication device is powered on are automatically displayed with said 2nd language if said 2nd language mode was selected by the user before said communication device was powered off.

9. The method of claim 8 , wherein one of said commands indicates either to open or close file, wherein said one of said commands displayed after said communication device is powered on is displayed (1) with said 1st language if said 1st language mode was manually selected by the user before said communication device was powered off and (2) with said 2nd language if said 2nd language mode was manually selected by the user before said communication device was powered off.

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36. The method of claim 35 , wherein plurality of the ranking logic assessment indicators include at least one of a competition indicator indicating a competitiveness of an advertisement, a service indicator indicating a number of visits of a user, a conversion indicator indicating a Click-Through Rate (CTR), an association indicator indicating an association, and an entry indicator indicating entry barriers, the entry barriers being a level of difficulty associated with registering a keyword.

35. The method of claim 33 , wherein assessing the keyword comprises: generating a plurality of ranking logic assessment indicators based on the weight indicator and the indicator; categorizing each of the ranking logic assessment indicators; and changing a priority and a proportion of the keyword in response to values of the categorized indicators.

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4. The method of claim 2 wherein semantic difficulty is determined from word length, word familiarity, word frequency, and rare word frequency.

2. The method of claim 1 wherein the text variation measures include syntactic complexity, semantic difficulty, degree of academic style, and text cohesion.

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9. The computer-implemented method of claim 8 , wherein using the one or more processors configured to execute the natural language processing application further includes: the one or more processors receiving an indication of a user input indicative of a modification to the representation of the concept space; the one or more processors applying, based on the modification, at least one of the first semantic analysis technique or the second semantic analysis technique to at least one of the concept space of the candidate term or the digital corpus; and the one or more processors displaying, on the user interface, an updated representation of the concept space of the candidate term or a representation of a different concept space based on the application of the at least one of the first semantic analysis technique or the second semantic analysis technique to the at least one of the concept space for the candidate term or the digital corpus.

8. The computer-implemented method of claim 7 , wherein using the one or more processors configured to execute the natural language processing application further includes: the one or more processors displaying, on the user interface, a representation of the concept space of the candidate term, the representation including, for each one or more latently-associated concepts of the candidate term, a respective indication of its association with a respective explicitly-associated or implicitly-associated concept from which the respective latent association was derived; and the one or more processors optionally displaying, on the user interface, a representation of knowledge other than the concept space that is discovered as being associated with the candidate term.

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4. The method of claim 3 wherein evaluating confidence comprises determining that at least one character of the received handwriting input implicates ambiguous candidates.

3. The method of claim 1 wherein sending the data pertaining to at least the first portion of the handwriting input to the second device comprises transmitting to the second device ambiguous handwriting data corresponding to at least one low confidence character recognized from the received handwriting input.

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1. A system configured to use semantic analysis to measure affective response at varying measuring rates, comprising: a semantic analyzer configured to receive a first segment of content comprising data representing first text; the semantic analyzer is further configured to utilize semantic analysis to generate a first feature that describes an aspect of a meaning of a portion of the first text; the semantic analyzer is further configured to generate, based on the first feature, a first indication that a first value related to a predicted emotional response to the first segment does not reach a first predetermined threshold; and a controller configured to select, based on the first indication, a first measuring rate for a device for measuring affective response of a user to the first segment; the semantic analyzer is further configured to receive a second segment of content comprising data representing second text; the semantic analyzer is further configured to utilize semantic analysis to generate a second feature that describes an aspect of a meaning of a portion of the second text; the semantic analyzer is further configured generate based on the second feature a second indication that a second value related to a predicted emotional response to the second segment does reach a second predetermined threshold; the controller is further configured to select, based on the second indication, a second measuring rate for the device for measuring affective response of the user to the second segment; wherein while operating at the first measuring rate, the device takes at least 50% fewer measurements of affective response, per unit of measurement time, compared to measurements of affective response the device takes while operating at the second measuring rate.

4. The system of claim 1 , wherein the first and second predetermined thresholds represent first and second benefit levels, respectively; and wherein the first and second values represent predicted benefits to measuring affective response to the first and second segments, respectively; whereby the first measuring rate is selected when the first indication indicates that a predicted benefit of measuring affective response to the first segment does not reach the first benefit level, and the second measuring rate is selected when the second indication indicates that a benefit of measuring affective response to the second segment does reach the second benefit level.

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9. A computer-readable medium having stored thereon software instructions for a virtual world environment maintained on a server computer that is in concurrent communication with plural client computers, each of the client computers having an associated object representing an entity in the virtual world environment, comprising: software instructions for maintaining a world object database on the server computer, the world object database including plural software objects that provide services in response to calls and correspond to entities within the virtual world environment; software instructions for concurrently delivering to the client computers local object databases that are subsets of the world object database, each local object database at a client computer including the associated object for the client computer and other objects within a bystander region for the associated object, the bystander region representing a perceptual range of effect on other objects of multimedia properties on the selected objects; and multimedia properties for at least selected objects in the local object databases for concurrently presenting the corresponding entities on the client computers in a format other than text.

10. The medium of claim 9 further comprising indications of hierarchical relationships so that each associated object references at least one other object; software instructions for determining in response to a call to a selected service at a selected one of the associated objects whether the selected service is available from the object; and software instructions for passing the call to a referenced object that is referenced by the selected object according to a hierarchical relationship if the selected service is unavailable at the selected object.

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8. The system of claim 7 , wherein the second sender encoder comprises a matrix encoder.

9. The system of claim 8 , wherein the second sender encoder comprises a Circle Surround matrix encoder.

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13. The method of claim 11 , further comprising: providing for display, simultaneously with the cycling of the characters, a software keyboard independent of the cycling interface in response to the first input gesture; and receiving a keyboard input from the software keyboard.

17. The method of claim 13 , further comprising: completing a query term that contains the selected cycled character using a proposed word predicted from characters selected using the cycling interface and the software keyboard.

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1. A modeling system based on a logical relation, comprising: one or more processors programmed to perform the following functions: dividing integrated operation tasks into different classes comprising adding operation objects, deleting operation objects and modifying operation objects, acquiring, command information of the operation tasks and performing task splitting on said command information, putting said operation objects on grid nodes by adopting rectangular lattice description, wiring according to fewest crosses and shortest paths and correcting associated ones of the operation objects on wiring paths, correspondingly performing model addition, deletion and modification actions according to the said putting of said operation objects on grid nodes by adopting rectangular lattice description and said wiring according to fewest crosses and shortest paths and correcting said associated ones of the operation objects on wiring paths, when one or more spatial point operation objects are newly added, automatically shifting the associated spatial points of adjacent areas, calculating a current graph increment according to said putting of said operation objects on grid nodes by adopting rectangular lattice description and said wiring according to fewest crosses and shortest paths and correcting said associated ones of the operation objects on wiring paths, and calculating a current model increment according to the current graph increment acquired by said calculating of a current graph increment; and a memory including a relational database for storing a grid model after the model increment corresponding to the graph increment is corrected, the current graph increment comprising a grid primitive object increment and a topological relation change increment.

2. The modeling system of claim 1 , wherein, an integrated task splitting mechanism is adopted in said performing of task splitting of said command information, which is in accordance with the IEC61968/61970 standard.

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4. The method according to claim 1 , further comprising labeling a number of users among the group of users using an active learning process thereby dividing the group of users into labeled users and unlabeled users.

8. The method according to claim 4 , wherein calculating the weighing factor includes using a stochastic gradient descent technique.

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11. The method of claim 1 , wherein the character is not removed from the data representing the text in the non-image form where the location of the character within the image of the text falls outside the valid content boundary, the character has a font matching a font of a second character within the text that falls inside the valid content boundary, and the character is part of a word that is found within a dictionary.

1. A method comprising: receiving, by a processor, data representing an image of text, and data representing the text in non-image form; determining, by the processor, a valid content boundary within the image of the text, the valid content boundary dividing a portion of the image corresponding to valid text of the image from and excluding another portion of the image corresponding to one or more of stray marks, dirt, debris, and handwritten notes; for each character of a plurality of characters within the text in the non-image form, determining, by the processor, a location of the character within the image of the text; and where the location of the character within the image of the text falls outside the valid content boundary, removing the character from the data representing the text in the non-image form, by the processor.

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1. A computer implemented method for responding to a natural language sentence in which: the terms: “reasoning” is: using a first concept in a natural language sentence to identify a second concept by creating a continuous chain of logic from the first concept to the second concept; and “directed reasoning” is: applying reasoning to a first concept in a natural language sentence to identify a second concept, wherein only reasoning related to the natural language sentence is allowed, the method comprising the steps of: receiving, in a computer, at least one input concept, wherein the at least one input concept is generated from parsing an initial natural language sentence; searching a knowledge base comprising a plurality of natural language sentences using the at least one input concept to retrieve at least one response concept from the knowledge base, the searching step further comprising the step of resolving one or more contextual referents occurring in a natural language sentence of the plurality, wherein each of the one or more contextual referents is a word within the natural language sentence, the word referring to a previously parsed word or a phrase in the natural language sentence or another natural language sentence of the plurality; performing directed reasoning on both the at least one input concept and the at least one response concept to select at least one selected response concept by connecting the at least one response concept to the at least one input concept through a continuous chain of logic based on at least one of inductive reasoning, deductive reasoning or abductive reasoning; and generating at least one response sentence containing the at least one selected response concept.

6. The method of claim 1 , wherein the knowledge base comprises pre-existing natural language sentences found in an information source.

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8. 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 a particular voice query submitted by a user; receiving feedback information from a user after the user is presented with a set of search results that was generated in response to a search query that included a transcription of the particular voice query; generating a score indicative of a probability that the transcription of the voice query is correct based at least on the feedback information; determining that the score that is generated based at least on the feedback information satisfies a threshold; and in response to determining that the score that is generated based at least on the feedback information satisfies the threshold, adapting an acoustic model using the particular voice query.

10. The system of claim 8 , wherein generating a score indicative of the probability that the transcription of the voice query is correct comprises: determining, for at least one of the voice queries, a score indicative of a probability that a transcription of the voice query is correct, wherein the score is generated based on user feedback indicating that the user has selected a particular search result from the set of search results.

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1. A non-transitory computer storage medium storing computer-useable instructions that, when used by one or more computing devices, cause the one or more computing devices to perform operations comprising: sampling data, via a social engine, from one or more social media streams, in accordance with a user selection received via a user device; assigning part-of-speech (POS) tags to text in the data; applying natural language processing, by a trending topic tool, to extract candidate topics from the data using a first rule comprising: identifying a sequence of a plurality of the assigned POS tags, wherein each POS tag of the sequence is selected from a group consisting of at least one of a proper noun tag, a plural proper noun tag, or a cardinal number tag; defining topic boundaries based on the identified sequence; and extracting a portion of the text corresponding to the topic boundaries as one of the candidate topics; ranking the candidate topics, by the trending topic tool, with a relevance score that quantifies relative importance of each candidate topic to determine trending topics; classifying, by the trending topic tool, the trending topics into categories; grouping the candidate topics into topic clusters of semantically-similar topics, by the trending topic tool, and transmitting the classified and clustered trending topics for display on the user device.

2. The non-transitory computer storage medium of claim 1 , wherein the user selection is a time constraint.

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4. The method of claim 1 , further comprising sharing the hand-written annotation with the other users via the camera view in an Augmented Reality Mode.

5. The method of claim 4 , further comprising determining position of the hand-written annotation in the camera view.

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1. A computer-implemented method for determining semantically related terms, the method comprising: presenting, with a processor, one or more terms of a first plurality of semantically related terms to a user based on a predictive error of each term of the first plurality of semantically related terms; receiving, with a processor, an indication of relevance of at least one of the terms of the first plurality of semantically related terms presented to the user; training a model, with a processor, to predict an indication of relevance of a term by the user based on the received indication of relevance of at least one of the terms of the first plurality of semantically related terms presented to the user; receiving a second plurality of semantically related terms from a keyword suggestion tool; and presenting, with a processor, one or more terms of the second plurality of semantically related terms to the user based on the model and one or more properties of each term of the second plurality of semantically related terms.

2. The computer-implemented method of claim 1 , wherein presenting one or more terms of the first plurality of semantically related terms to a user based on a predictive error of each term of the first plurality of semantically related terms comprises: determining a subset of terms of the first plurality of semantically related terms based on a predictive error of each term; and presenting the subset of terms to the user.

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18. The computer-implemented method of claim 15 , further comprising: at a configuration collector layer, receiving a web page configuration.

19. The computer-implemented method of claim 18 , further comprising: at the configuration collector layer, parsing the web page configuration into at least one page component, the at least one page component comprising one or more of (1) a properties attribute configured to pass configurable presentation-related data, (2) a data source attribute configured to specify a server or source to invoke for fetching additional data called for by the at least one page component, or (3) a data analyzer configured to specify a decision point for the at least one page component when an additional decision regarding an additional child page component is called for; and changing the configurable presentation-related data in a live environment without re-deploying a web application associated with the web request context.

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8. The method of claim 6 , further comprising: (E) playing the emphasis-adjusted audio stream.

6. The method of claim 1 , further comprising: (D) modifying an emphasis of the region of the spoken audio stream in accordance with the emphasis factor to produce an emphasis-adjusted audio stream.

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9. A speech recognizing apparatus according to claim 1 further comprising: a recognition result selector for selecting duration data to predicate the duration of a recognition unit, and wherein the duration predicator predicates the duration of one of the recognition units to be subsequently matched using the selected duration data.

1. A speech recognizing apparatus for recognizing an input speech by dividing a total utterance period of the input speech to be recognized into a plurality of small sections, each one of said plurality of small sections forming a recognition unit of the input speech, said speech recognizing apparatus comprising: a matching unit for performing a matching between a reference speech and the input speech for each one of the recognition units and for outputting a matching result; a matching result storage buffer for storing the matching result and duration data representing a duration of each one of the recognition units which have already been matched by the matching unit; and a duration predicator for predicating the duration data for each one of the recognition units to be subsequently matched by the matching unit using the stored duration data; and wherein the matching unit performs the matching between the reference speech and the input speech for each of the recognition units using the duration data predicated by the duration predicator.

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17. A system comprising: wrapping code coupled to an error handler of a routine, in which the routine produces an error and the wrapping code wraps the error with relevant information to provide a wrapped exception instance, including to use an exception type hierarchy to preserve information in the wrapped exception instance including an exception type; and an exception manager that receives the wrapped exception instance and determines one or more actions to take based upon the exception type of the wrapped exception instance, wherein the exception manager takes the one or more actions, including to present an interactive dialog associated with the exception type and wherein the dialog includes an interactive input mechanism, which when selected, results in the exception manager taking further action to navigate back to a prior location, or retry an operation that caused the error.

18. The system of claim 17 in which the wrapped exception type is derived from a higher-level exception type in the exception type hierarchy.

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5. The method of claim 1 , wherein the executing includes capturing speech parameters of the input source phrase, applying the captured speech parameters to the phrase template associated with the destination language, and using the result to mimic characteristics of the input source phrase, wherein the captured speech parameters include the pitch, cadence, and tone characteristics of the input source phrase.

1. A method of providing a portable, real time voice translation for a child of pre-school or elementary school age, the method comprising: making a translation system available to a child of pre-school or elementary school age, the system having a content state selector on the front of the device that includes simplified pictorial representations of a plurality of translatable content for easy selection by children; a processor and a memory, the memory having a computer program that is operable in executing the following functions: accessing a multilanguage database embodied in a non-transitory computer readable storage medium including a plurality of phrase templates associated with a respective plurality of phrases; selecting a source language from a plurality of source languages and a destination language from a plurality of destination languages; inputting a source phrase; transmitting the source phrase (i) to a speech recognition module embodied in a non-transitory computer readable storage medium for converting the spoken source phrase into a recognized source phrase; (ii) a translation engine embodied in a non-transitory computer readable storage medium for translating the recognized source phrase in the selected source language into a destination phrase in a destination language selected from multiple languages, wherein the plurality of phrases in the multilanguage database include the destination phrase; (iii) a template look-up engine embodied in a non-transitory computer readable storage medium for finding the phrase template associated with the destination phrase from among the multiple languages; and, outputting the spoken translation in the selected destination language; and, a graphical user interface on the front of the device that includes a text display of the source phrase and the destination phrase; wherein, the translation system having a total time between the input of the source phrase and output of the destination phrase that is adjustable from a real-time translation to a slow and more easily assimilated translation pace for ease of use by the child of preschool or elementary school age, the real-time pace being no slower than 0.010 seconds.

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1. A method comprising: after receiving, an indication of user input that selects one or more textual characters, receiving, by a computing device, an indication of user input that selects an end-of-word identifier; determining, by the computing device, based at least in part on the one or more textual characters and in response to receiving the end-of-word identifier, a first auto-complete word suggestion from a plurality of auto-complete word suggestions, wherein the first auto-complete word suggestion is determined to be more likely to be a correct word suggestion than a second auto-complete word suggestion from the plurality of auto-complete word suggestions; outputting, by the computing device, for display, the first auto-complete word suggestion that replaces the one or more textual characters followed by the end-of-word identifier; receiving, by the computing device, an indication of user input that deletes the end-of-word identifier, wherein the indication of user input that deletes the end-of-word identifier comprises a combination of a selection of both a backspace key and a spacebar key; and responsive to receiving the indication of user input that deletes the end-of-word identifier, outputting, by the computing device, for display, the second auto-complete word suggestion, wherein the second auto-complete word suggestion replaces the first auto-complete word suggestion; wherein the end-of-word identifier is a first end-of-word identifier and outputting the second auto-complete word suggestion comprises outputting, by the computing device, for display, the second auto-complete word suggestion followed by a second end-of-word identifier replacing the first auto-complete word suggestion and the first end-of-word identifier.

12. The method of claim 1 , wherein outputting the second auto-complete word suggestion comprises outputting, by the computing device, for display at a location of a presence-sensitive screen that corresponds to a portion of the presence-sensitive screen at which the first auto-complete word suggestion was displayed, the second auto-complete word suggestion.

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16. The navigation system of claim 14 , wherein the data storage further contains instructions executable by the processor for carrying out map display functions, the functions including: receiving the result set of document identifiers; retrieving the documents associated with the document identifiers from the geographic database; and displaying a map that identifies locations specified within the documents.

17. The navigation system of claim 16 , wherein the documents are point of interest records in the geographic database and the point of interest records include location data associated with a point of interest.

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19. A system comprising: a processor; and a storage coupled to said processor, the storage storing instructions to develop a forest supervised classifier, sum the affinity between said first and second points in each tree of the forest and average the result and determine a non-binary affinity measure between two data points using the supervised classifier, and provide a visualization of the relationships between data points using said non-binary affinity measure.

22. The system of claim 19 wherein said storage stores instructions to determine a kernel based supervised classifier.

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1. A method of managing a dialog with a user, the method comprising: generating communicative goals based on a communication received from the user, the communicative goals being related to further information needed from the user in addition to information in the communication; generating a plurality of prompts, each prompt of the plurality of prompts being a viable and potentially usable prompt in response to the communication received from the user and generated in response to the communication from the user based on the communicative goals; ranking the plurality of prompts independent of the user; and outputting to the user an output prompt based on the ranking of the plurality of prompts.

10. The method of claim 1 , wherein the method is processed in one of a customer care system, a reservation system, a parts ordering system, a navigation system, an information gathering system, and an information retrieval system.

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22. A system for determining a relation between search queries, the system comprising: a database for maintaining a search session associated with at least one search query which has been received from a user terminal during the search session, wherein the database is updated at predetermined time intervals; a counter configured for counting a total number of search sessions for each user terminal during a time interval, a first number of search sessions where a first search query is initially received and a second query is subsequently received from said each user terminal during the time interval, a second number of search sessions where a third search query is received from said each user terminal during the time interval, and a third number of search sessions where the first search query and the second search query are initially received, and the third search query is subsequently received from said each user terminal during the time interval, by referring to the database; a conditional probability information unit configured for calculating conditional probability from comparing said first number of search sessions with said third number of search sessions; a correlation information generation unit configured for calculating correlation by using the total number of search sessions, the first number of search sessions, the second number of search sessions, and the third number of search sessions; and a relation determination unit configured for determining a relation between the first search query and the second search query, and the third search query based, at least in part, upon the conditional probability and the correlation.

26. The system of claim 22 , wherein the search session is set when a search window is provided to the user terminal, and terminated when data is not transmitted from the user terminal during a predetermined time, and an additional search session is started when an additional search query is received from the user terminal after the search session is terminated.

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9. The data updating method according to claim 8 , further comprising the step of updating, by the terminal, in a case of which a word included in the confusion information sent from the server is included in confusion information for a different area, confusion information for the different area corresponding to the word.

8. A data updating method for a terminal coupled to a server via a network, the terminal including a speech recognition system and holding map data including a landmark, the speech recognition system managing recognition data including a word corresponding to a name of the landmark included in the held map data, the data update method comprising the steps of: sending, by the terminal, update area information indicating an area of the map data to be updated, and update data on the area to be updated to the server; generating, by the server, in a case of which recognition data of the area indicated by the update area information sent from the terminal has been changed, after a time point indicated by the update data sent from the terminal, difference data between latest recognition data and recognition data corresponding to the area indicated by the update area information at a time indicated by the update data; sending, by the server, the generated difference data to the terminal along with map data on the area indicated by the update area information; updating, by the terminal, the map data held in the terminal based on the map data sent from the server; and updating, by the terminal, the managed recognition data based on the difference data sent from the server; wherein: the recognition data includes confusion information, the confusion information including a confusion word having a tendency to cause a recognition error with the word corresponding to a name of the landmark, and a confusion score which represents the tendency of the confusion word to cause recognition error; the server sends the difference data including the confusion information to the terminal; and the method further comprises the step of updating, by the terminal, the confusion information included in the recognition data held by the terminal based on the confusion information sent from the server.

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13. A computer-readable storage device encoded with a computer program product, the computer program product comprising instructions that when executed on one or more computers cause the one or more computers to perform operations comprising: receiving a first data set of data samples, each data sample comprising input data and corresponding output data, wherein the first data set is new relative to (i) an initial training data set and (ii) a plurality of previously received update data sets of data samples, wherein the initial training data set was used to train each trained predictive model in a repository of trained predictive models, at least some which are updateable, and wherein the plurality of previously received update data sets of the data sample were used to retrain one or more updateable trained predictive models in the repository; assigning a richness score to each of the data samples included in the first data set and to each of a set of retained data samples from the initial training data and the plurality of previously received update data sets, wherein the richness score for a particular data sample indicates how information rich the particular data sample is, relative to other data samples in the set of retained data samples and the first data set, for determining an accuracy of a trained predictive model; ranking the data samples included in the first data set and the set of retained data samples based on the assigned richness scores; selecting a first set of test data from the data samples included in the first data set and the set of retained data samples based on the ranking; testing how accurate each of the trained predictive models in the repository is in determining predictive output data for given input data using the first set of test data and determining respective accuracy scores for each of the trained predictive models based on the testing; and selecting a first trained predictive model from the repository based on the accuracy scores and providing access to the first trained predictive model to a client computing system for generating predictive output data based on input data received from the client computing system.

18. The computer-readable storage device of claim 13 , the operations further comprising: after determining the accuracy score for the trained predictive model, retraining the trained predictive model using the first data set of data samples.

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20. The storage medium of claim 15 , wherein said accessing the data representing the plurality of reference strokes comprises accessing data that was generated by one or more skilled artists and that is stored in one or more reference stroke libraries.

15. A non-transitory, computer-readable storage medium storing program instructions that when executed on one or more computers cause the one or more computers to perform: receiving data representing a query stroke, wherein the data representing the query stroke comprises trajectory data indicating a trajectory of the query stroke with respect to two degrees of freedom; accessing data representing a plurality of reference strokes, wherein the data representing each of the plurality of reference strokes comprises trajectory data indicating a trajectory of the reference stroke with respect to the two degrees of freedom and pose data for the reference stroke with respect to one or more other degrees of freedom; creating a sequence of synthesized poses that follows the trajectory of the query stroke, wherein pose data for the synthesized poses is dependent, at least in part, on the data representing the plurality of reference strokes, the data representing the query stroke comprising data for fewer degrees of freedom than the data representing the reference strokes and fewer degrees of freedom than the data representing the sequence of synthesized poses; and outputting data representing the sequence of synthesized poses.

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8. The method of claim 1 , further comprising: identifying a plurality of translation variants for a word or expression in the second translation; identifying a respective relevancy value for each of the plurality of translation variants; and outputting one or more of the plurality of translation variants whose respective relevancy value exceeds a threshold value.

1. A method comprising: generating, by at least one processor, a first translation of a first portion of a source text into a target language, wherein generating comprises extracting at least one first lexical meaning of the first portion; selecting, by the processor, a first semantic class for the first lexical meaning from one or more first semantic classes in a semantic hierarchy, wherein the first semantic class corresponds to the first lexical meaning in the semantic hierarchy; extracting, by the processor, a plurality of lexical meanings of a second portion of the source text, wherein the semantic hierarchy comprises a plurality of second semantic classes corresponding to the lexical meanings; selecting, by the processor, at least one second lexical meaning for the second portion from the lexical meanings based on distances between the second semantic classes and the first semantic class within the semantic hierarchy; generating, by the processor, a second translation of the second portion into the target language based on the selected second lexical meaning; and outputting the generated second translation.

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1. A method of displaying in a calculator, comprising: receiving a first mathematical expression in an expression entry area on the calculator; displaying results in a first window on the calculator, wherein the results displayed in the first window comprise a result for the first mathematical expression; displaying, in a second window on the calculator, a usage history comprising the first mathematical expression, a second mathematical expression, and results displayed in the second window, wherein the results displayed in the second window comprise the result for the first mathematical expression and a result for the second mathematical expression, wherein the usage history is displayed in a hierarchical tree such that a result for a given mathematical expression is displayed in the second window on a different hierarchical level than the given mathematical expression; and displaying organizational tabs for the second window, wherein each organizational tab is operable to display a given category of data associated with the usage history, and wherein the results in the first window and the results in the second window are displayed simultaneously.

11. The method of claim 1 , wherein the calculator processes waveforms used to simulate an electronic circuit.

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73. The apparatus of claim 67 , wherein the apparatus further comprises: means for performing a second specified action associated with responding to messages that are not unsolicited, when the reputation score is better than a second predefined threshold, wherein the first predefined threshold is different from the second predefined threshold.

74. The apparatus of claim 73 , wherein the means for performing the second specified action comprises means for indicating that the message is valid.

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2. The method according to claim 1 , further comprising, after the sentence pair extraction step, a keyword-related phrase presentation step in which if, in the sentence pair extraction step, two or more sentence pairs are extracted for a keyword input in the input step and if two or more different keyword-related phrases in the source language are detected from the partial correspondence information, then the detected two or more keyword-related phrases in the source language are presented to a user such that the user is allowed to select a keyword-related phrase from the presented two or more keyword-related phrases, wherein in the keyword-related phrase storage step, if the user selects a keyword-related phrase from the presented two or more keyword-related phrases, a keyword-related phrase in the target language corresponding to the selected keyword-related phrase in the source language is described in the keyword-related phrase table.

1. A method of generating a text sentence in a target language different in a source language, based on one or more words in the source language input as keywords, the method comprising: an input step in which one or more keywords in the source language are input via an input means without inputting a full text sentence in the source language, the one or more keywords being a segment of the full text sentence in the source language; a sentence pair extraction step in which a sentence pair extraction means extracts one or more sentence pairs each including more than one of the keywords from a parallel corpus database including partial correspondence information indicating correspondence between a word/phrase in the source language and a word/phrase in the target language in each sentence pair; a keyword-related phrase storage step in which a target-language keyword-related phrase corresponding to each source-language keyword-related phrase is detected from the partial correspondence information of each sentence pair and stored as a pair of keyword-related phrases in the source language and in the target language in the form of a keyword-related phrase table in a storage means; a text sentence candidate generation step in which a text candidate generation means performs dependency relationships of each keyword-related phrase in the source language and in the target language of the pair of keyword-related phrases assumes dependency relationships among keyword-related phrases in the target language described in the keyword-related phrase table and generates one or more target-language text sentence candidates by using a target language keyword-related phrase generation model and a language model by assuming dependency relationships of two or more pairs of keyword-related phrases; and an output step in which at least one text sentence candidate is output from an output means corresponding to the full text sentence in the source language.

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1. A computer-implemented method for automating accounting, the method comprising: receiving a check voucher at a system, wherein the check voucher corresponds to a check, wherein the check voucher is neither a check nor an invoice; performing an optical character recognition (OCR) operation on the check voucher to identify a set of tokens printed on the check voucher; searching a dictionary of tokens for open invoices to identify a match between the set of tokens printed on the check voucher and tokens associated with an open invoice; determining an amount of the check by determining the value of an amount token printed on the check voucher; applying a payment for the amount of the check to the open invoice; determining a layout for the check voucher, wherein the layout indicates a position and a type of each token on the check voucher; saving the layout in a library to facilitate processing of subsequent check vouchers with the same layout; determining an identifying token on the check voucher; and using the identifying token to retrieve the layout for the check voucher.

6. The method of claim 1 , further comprising: searching the dictionary of tokens for open invoices to identify a match between the set of tokens printed on the check voucher and tokens associated with a set of open invoices; and applying a payment for the amount of the check to the set of open invoices.

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6. The method as recited in claim 2 , wherein applying one or more schematic rules to the one or more segments of the text that have been classified as temporal data comprises: applying the one or more schematic rules to the one or more of the plurality of tokens that have been classified as temporal data.

2. The method as recited in claim 1 , wherein assigning a label to one or more of a plurality of segments of the text comprises: parsing the text into a sequence of a plurality of tokens; and assigning a label to one or more of the plurality of tokens, wherein the label assigned to the one or more of the plurality of tokens classifies the corresponding one or more of the plurality of tokens as temporal data in one of the plurality of classes of temporal data.

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2. The method of claim 1 , wherein step of generating the summarized video according to the detected at least one semantic event comprises generating the summarized video with a desired length by combining the detected at least one semantic event.

1. A method of processing a sports video, comprising the steps of: analyzing the sports video to detect at least one semantic event, each of which is associated with a segment length; assigning a segment length to the at least one semantic event according to importance of a corresponding event; extracting a scene segment associated with the at least one semantic event out of the sports video according to the segment length; and generating a summarized video according to the detected at least one semantic event; wherein the segment length of each semantic event varies according to importance and weight of each semantic event and varies according to a context analysis result showing the relative importance of each semantic event comparing to its prior and subsequent events throughout the sports video; and the step of extracting the scene segment associated with the at least one semantic event out of the sports video according to the segment length comprises: generating the semantic event according to at least one of base bag, score, and out in a score board region.

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11. The one or more computer-readable storage media of claim 10 , where the first name is located to the right of the second name on a command line or in a software code listing.

13. The one or more computer-readable storage media of claim 11 , where the second name is associated with an instance and the first name is associated with a method or a property.

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12. The spine implant of claim 10 wherein said elongated member has about a constant diameter with the first and second springs having dimensions within said constant diameter such that said elongated member can be introduced adjacent to a spine through a cannula.

10. A spine implant adapted to be attached to another spine implant wherein the another spine implant includes a first member secured relative to a first portion of a vertebra and a second member secured relative to a second portion of the vertebra, the spine implant including: an elongated member with a first end and a second end, said first end adapted to be secured to the first member and the second end adapted to be secured to the second member; the elongated member including a platform and a first spring located between the platform and the first end and a second spring located between the platform and the second end; and a vertical rod assembly having a first rod end connected to the platform, and a second rod end configured for attachment to a second vertebra.

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4. An apparatus according to claim 1, wherein: said heading candidate extraction means comprises doc memory means for storing all document data input at said input means, and means for detecting segmentation codes from the document data stored in said document memory means and extracting the document data in units of segmentation codes.

1. A document generating apparatus comprising: means for inputting document data; heading dictionary storing means for storing words and phrases frequently used as headings, said headings being arranged in a column direction; heading candidate extraction means for extracting as heading candidate particular words and phrases of said words and phrases stored in said heading dictionary storing means, said heading candidate extraction means connecting to said heading dictionary storing means, said heading candidate extraction means connecting to said means for inputting document data; heading rule dictionary means for storing rules which are used to determine said headings, said heading rule dictionary means connecting to a heading deciding means; said heading deciding means connecting to said heading candidate extracting means, said heading deciding means being used to check said heading candidate extraction means according to said rules stored in said heading rule dictionary means, said heading deciding means deciding whether said heading candidate is a suitable heading; document architecture rule dictionary means for storing rules associated with document logical architecture, said document architecture rule dictionary means being connected to a document architecture deciding means; and said document architecture deciding means being used for deciding a document logical architecture of said headings, said document architecture deciding means comparing relations between said headings and comparing a relation between said headings and at least one non-heading in accordance with said rules associated with document logical architecture stored in said document architecture rule dictionary means, said headings and said at least one non-heading being decided by said heading deciding means, said document architecture deciding mean being connected to said heading deciding means.

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16. The system of claim 11 , where the document includes a partial address.

17. The system of claim 16 , where the partial address includes a street name and does not include a postal code, a city name, or a state name.

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1. A method, comprising: determining that a received query comprising a plurality of selection predicates requests values of sensitive data stored in a secure database table of a database; determining that a first selection predicate of the plurality of selection predicates does not specify at least one of a plurality of specific values stored in the secured database table; evaluating a plurality of possible values for each selection predicate of the plurality of selection predicates other than the first selection predicate; determining that each of the plurality of possible values for each selection predicate is evaluated as a true value; computing a security score for the received query based on the first selection predicate of the received query not specifying the at least one of the plurality of specific values stored in the secured database table and that each of the plurality of possible values for each selection predicate other than the first selection predicate is evaluated as a true value; determining that the security score exceeds a security threshold value; and upon determining that the security score exceeds the security threshold value, performing, by a database management system (DBMS) executing on a computer processor, a predefined operation to restrict access to the requested values of the sensitive data.

7. The method of claim 1 , further comprising: prior to computing the security score, determining that processing the received query returns a number of rows from the secure database table that exceeds a threshold number of rows, wherein the security score is further computed based on the determining that that processing the received query returns the number of rows from the secure database table that exceeds the threshold number of rows.

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1. In a mobile consumer messaging system (MCMS), in which the MCMS is in electronic communications with a plurality of mobile device users via one or more mobile carrier networks and is in electronic communications with one or more chat agents utilizing one or more chat platforms, a method for facilitating messages between the plurality of mobile devices users and the one or more chat agents, comprising the steps of: receiving a particular chat message at the MCMS from a specific mobile device user via a respective mobile carrier network, wherein the particular chat message includes message content and message identifying information; extracting via the MCMS the message content and message identifying information from the particular chat message and storing the message content and message identifying information in an MCMS database; generating via the MCMS a new message in a format acceptable to a respective chat platform, wherein the new message includes the message content and is based on the message identifying information; and transmitting the new message from the MCMS to a respective chat agent associated with the respective chat platform.

12. The method of claim 1 , wherein the MCMS includes a management computer system for performing functions of the MCMS.

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5. A texture unit of claim 1 wherein said level of detail component utilizes a sparse texture residency translation map to indicate a minimum resident LOD.

1. A texture unit comprising: an instruction input component for receiving texture instructions and texture coordinates; a level of detail component for determining a level of detail for performing said instructions at said texture coordinates, wherein said level of detail component weighs residency of texture information in determining said level of detail; a texture determination component for calculating an address of a texture corresponding to said level of detail determined by said level of detail component and fetching said texture; and a texture filter component for filtering said texture and forwarding a result.

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9. The apparatus of claim 1 wherein the phonetic sequence search component searches for the at least one phoneme within the at least one phoneme part of the combined lattice subject to the absence or low certainty of the at least one first search result.

1. An apparatus for detecting an at least one word in an at least one audio signal, the apparatus comprising a computing platform executing: a phonetic and text decoding component for generating from the at least one audio signal a combined lattice, the combined lattice comprising an at least one text part and an at least one phoneme part, the phonetic and text decoding component comprising: a quality monitoring component for obtaining a quality assessment of multiple parts of the at least one audio signal; a speech to text engine for detecting an at least one indexed word from an at least one first part of the at least one audio signal, the at least one first part having high quality; a phoneme detection engine for detecting an at least one indexed phoneme from an at least one second part of the at least one audio signal, the at least one second part having lower quality than the at least one first part; and a phoneme and text lattice generator for generating a combined lattice from the at least one indexed word and the at least one indexed phoneme; and a search component for searching for the at least one word within the combined lattice, the search component comprising: a text search component for searching for the at least one word within the at least one text part of the combined lattice and generating an at least one first search result; a grapheme to phoneme converter for extracting an at least one phoneme from the at least one word; a phonetic sequence search component for searching for the at least one phoneme within the at least one phoneme part of the combined lattice and generating an at least one second search result; and a fusion component for fusing the at least one first search result with the at least one second search result to obtain a fused result.

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9. The one or more devices of claim 1 , wherein the method further comprises providing a selectable more providers indicator that, if selected, presents additional providers.

1. One or more computer hardware devices storing computer-executable instructions that, when executed by a processor in a computing device, cause the computing device to perform a method of facilitating presentation of actions and providers associated with entities, the method comprising: determining a query intent of a received query, using a query log of one or more users, wherein the query intent comprises at least a portion of the query; identifying a group of entity sets, from a plurality of stored entity sets, having a same first entity that matches the determined query intent, wherein each entity set includes an entity, an action corresponding with the entity, and a provider that implements the action on the entity; using the identified group of entity sets to identify a plurality of actions associated with the same first entity that matches the determined query intent, each of the plurality of actions representing a different function to perform; identifying one or more providers associated with each of the identified plurality of actions associated with the same first entity, each of the one or more providers implementing the associated action on the same first entity; and providing the plurality of actions and the corresponding one or more providers associated with the same first entity for concurrent integration with a representation of the associated first entity on a search results page including at least one search result having a content link configured for redirecting to a location associated with the at least one search result, wherein the first action is an action to be implemented on the first entity by the first provider and the second provider, and wherein the second action is an action to be implemented on the first action by the third provider and the fourth provider.

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9. The method of claim 1 , wherein said segmentation scheme defines boundaries between text segments in a formatted text document.

10. The method of claim 9 , wherein said boundaries between text segments comprise one or more of at least: paragraphs; empty lines; table cells; and other semantically meaningful separators used in said formatted text documents.

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2. The method of claim 1 further comprising: analyzing said at least one application to determine an application relevance ranking for said at least one application; and ranking said at least one application with respect to at least one other application based on said application relevance ranking.

1. A method comprising: performing a first search using a first query against only help documentation that provides trusted descriptions of Application Programming Interface (API) calls; accessing a first search result associated with the first search, the first search result comprising an API call identifier; combining said first query with said API call identifier to form a second query; and performing a second search using said second query to obtain a second search result comprising at least one application identifier of at least one application.

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19. A computer-readable storage device encoded with a computer program product, the computer program product comprising instructions that when executed on one or more computers cause the one or more computers to perform operations comprising: receiving a request from a client-subscriber computing system for access to a trained predictive model from a repository of trained predictive models, which trained predictive model can generate a predictive output in response to receiving input data having one or more input types, wherein the one or more input types can be determined from the request; determining from information that describes each of the trained predictive models in the predictive model repository that one or more models included in the predictive model repository match the request from the client-subscriber computing system, wherein determining a match is based at least in part on a comparison of the one or more input types determined from the request to input types included in the information that describes the trained predictive models; and providing access to at least one of the one or more models to the client-subscriber computing system; wherein the one or more models that match the request are models that were trained using training data provided by a computing system other than the client-subscriber computing system.

27. The computer-readable storage device of claim 19 , wherein: at least one of the one or more input types determined from the request is included in a hierarchy of input types, wherein the at least one input type can be represented by incrementally broader input types by incrementing to higher levels in the hierarchy; and determining a match based at least in part on a comparison of the one or more input types determined from the request to input types included in the information that describes the trained predictive models includes incrementing to one or more higher levels in the hierarchy until an input type that describes a trained predictive model matches the at least one input type determined from the request as represented by incrementally broader input types.

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14. The method of claim 12 , wherein encapsulating, utilizing one or more processors, the converted first text in a first rheme object is performed at a second server.

12. The method of claim 1 , wherein converting, utilizing one or more processors, the first utterance to first text is performed at a first server remote from the electronic device.

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17. A computer readable medium comprising data encoded therein for generating a normalized configuration model, wherein the data comprises code executable by a processor to: generate product configuration instances from one or more product configuration models that include non-normalized feature references; identify non-normalized feature references included in one or more of the product configuration instances; access a mapping file, wherein the mapping file includes a map of specific product feature references to normalized feature references; locate normalized feature references that correlate with non-normalized feature references included in the generated product configuration instances; replace non-normalized feature references with correlating normalized feature references in accordance with the mapping file; and generate a normalized configuration model corresponding to the generated product configuration instances using the normalized feature references replacements, wherein the normalized configuration model is configured for use with a configuration system which presents the normalized feature references to a user of the configuration system to allow the user to configure a product using the normalized feature references.

19. The computer readable medium of claim 17 wherein the code to locate normalized feature references comprises code to: iterate over all product configuration instances on a feature-by-feature basis.

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13. The system of claim 11 , wherein determining the first information gain value is further based on an area of the first portion of the first electronic document and an area of the second portion of the first electronic document, and wherein determining the second information gain value is further based on an area of the third portion of the first electronic document and an area of the fourth portion of the first electronic document.

11. A system comprising: one or more computer processors; and one or more non-transitory computer readable devices that include instructions that, when executed by the one or more computer processors, causes the processors to perform operations, the operations comprising: receiving a first electronic document; determining an entropy value for the first electronic document; determining a first information gain value associated with a first line that divides the first electronic document into a first portion and a second portion, comprising: a) determining an entropy value for the first portion of the first electronic document and an entropy value for the second portion of the first electronic document, b) based on the entropy value for the first portion of the first electronic document and the entropy value for the second portion of the first electronic document, determining an entropy value associated with the first line, and c) determining the first information gain value by determining a difference between i) the entropy value for the first electronic document and ii) the entropy value associated with the first line; determining a second information gain value associated with a second line that divides the first electronic document into a third portion and a fourth portion, comprising: a) determining an entropy value for the third portion of the first electronic document and an entropy value for the fourth portion of the first electronic document, b) based on the entropy value for the third portion of the first electronic document and the entropy value for the fourth portion of the first electronic document, determining an entropy value associated with the second line, and c) determining the second information gain value by determining a difference between i) the entropy value for the first electronic document and ii) the entropy value associated with the second line, wherein each of the entropy values is based at least in part on document objects in the respective portions of the first electronic document; determining which of the first information gain value and second information gain value is greater; in response to determining that the first information gain value is greater, generating a second electronic document that includes at least a portion defined by the first line and using the first information gain value to recursively divide the portions defined by the first line; in response to determining that the second information gain value is greater, generating a third electronic document that includes at least a portion defined by the second line and using the second information gain value to recursively divide the portions defined by the second line, wherein the entropy value for the first portion of the first electronic document and the entropy value for the second portion of the first electronic document are based at least on a variation in pixel intensity for pixels that the first line intersects in the first electronic document, and the entropy value for the third portion of the first electronic document and the entropy value for the fourth portion of the first electronic document are based at least on a variation in pixel intensity for pixels that the second line intersects in the first electronic document.

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4. The method according to claim 3 , wherein constructing a name entity association database by using the data further comprises: extracting from the name entity list of the history short message at least one name entity; inquiring about whether or not there exists the name entity in the name entity database; if the result of the inquiry is No, adding the name entity to the name entity database; and associating the identification information of the contact of the history short message with the name entity.

3. The method according to claim 2 , wherein constructing the critical object association database by using the data comprises constructing a name entity association database by using the data.

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4. The method of claim 2 , further comprising performing value class membership by replacing a value with a generic token.

2. The method of claim 1 , 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.

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1. A method comprising: enabling a user to define a redaction of a part of a document in a corpus of documents, the redaction definition including a scope defining a range of documents in the corpus to which the redaction applies, wherein the document is produced as a bitmap image file in which a redacted region appears as a region of black pixel data; creating a temporary image file representing an unredacted version of the document; creating a temporary image file representing a redacted version of the document utilizing custom fonts in rendering which result in foreground and background colors of redaction regions being an inverse of the foreground and the background colors of fonts used for non-redaction regions; performing XOR operations between corresponding sections in the temporary image files of the unredacted and the redacted documents; and creating a mapping between a redacted token and pixel space bounds of the redacted token, thereby creating the region of black pixel data.

36. The method of claim 1 , further comprising: providing an option to disable a redaction, after the redaction has been made, with respect to one or more matters.

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3. The apparatus of claim 1, wherein said electronic signal is a television signal.

1. For use in connection with home television video recording, playback, and viewing equipment, apparatus for processing an electronic signal including audio portions and video portions corresponding to audible and visible portions of the electronic signal, with said audio portions containing a spoken component related to the audible portion and with said video portions containing an auxiliary information component providing a visible representation of a respective concurrent spoken component of said electronic signal, said apparatus comprising: a video input to receive video portion of an electronic signal with said video portion containing a synchronized auxiliary information component corresponding to a visible representation of a concurrent spoken component; an audio input to receive audio portion of an electronic signal with said audio portion corresponding to said video portion auxiliary information comment; a video output by which the video portion of an electronic signal is made available to a user of the apparatus; an audio output by which the audio portion of an electronic signal is made available to a user of the apparatus; a programmed microcomputer including a data memory for receiving said auxiliary information component from said video portion; said microcomputer being programmed for analyzing said auxiliary information component in order to determine if said auxiliary information component contains undesirable words or phrases received in said memory; a switch for muting a corresponding audio portion having a concurrent spoken comment if undesirable words or phrases are detected within an auxiliary information component segment; said microcomputer being programmed for removing or replacing with another word or phrase any detected undesirable word or phrase found within said auxiliary information segment; said switch being connected to disable mute at the conclusion of receipt of the modified auxiliary information component segment; and, an on-screen display and video combining unit connected to provide a modified auxiliary information component containing signal to said video output.

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25. A method according to claim 24 , further comprising: providing an authoring environment comprising at least one of a visual interface and a non-visual interface that is selected from the group comprising an audio interface, tactile interface, and programmatic application programming interface.

24. A method for enabling in-context authoring of alternate substitute content for one or more non-textual objects, comprising: accessing an electronic document comprising content data; and facilitating in-context editing of substitute content corresponding to a non-textual object, comprising: selecting a segment of at least part of the content data; selecting one non-textual object from a plurality of non-textual objects contained within a data source; associating the selected non-textual object with the content data segment; substituting the non-textual object for the content data segment; and storing the content data segment as the substitute content for the non-textual object.

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1. A computer-implemented method of processing Web activity data, comprising: obtaining a collection of Web activity data generated by a plurality of users at a plurality of Webpages associated with a plurality of unaffiliated Websites, wherein the Web activity data comprises a plurality of query Uniform Resource Locators (URLs) that represent searches performed by users; extracting a plurality of search terms from the plurality of query URLs and associating each of the plurality of search terms with a corresponding Webpage, wherein extracting the plurality of search terms comprises extracting a target class of information from a URL query field that has been identified by a classifier generated by a training system; and generating statistical data from the Web activity data based, at least in part, on the search terms, wherein the statistical data comprises a first statistical data for a target search term associated with a target Webpage and a second statistical data for the target search term associated with another Webpage.

3. The computer implemented method of claim 1 , wherein generating the statistical data comprises maintaining a set of counters, each counter corresponding to the target search term and one of the Webpages in the Web activity data, and each counter tracks the number of times that the corresponding target search term occurs at the corresponding Webpage.

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1. A system for optimizing a query, comprising: a memory, and at least one processor operatively coupled to the memory; a construction module executed via the at least one processor and capable of building a data structure for a query, the data structure including a plurality of components, wherein each of the plurality of components corresponds to at least one graph pattern; a flow module executed via the at least one processor and capable of determining a plurality of flows of query variables between the plurality of components; and a cost determination and ranking module executed via the at least one processor and capable of determining a combination of the plurality of flows between the plurality of components that results in a minimum cost to execute the query; wherein a component of the plurality of components represents an access method for evaluating a graph pattern, the access method comprising a way to evaluate the graph pattern based on indexing of data in one or more databases; and wherein the minimum cost is a function of the access method for evaluating the graph pattern.

3. The system according to claim 1 , further comprising a constraint module capable of generating one or more constraints for ruling out invalid flows of the plurality of flows.

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27. The method of claim 26 , wherein: starting the local binary translation thread comprises: determining, in the non-preemptive mode in the binary translation scheduler, whether a binary translation exists for the hotspot in a global translation cache; and starting, in a preemptive mode on the processor core and in response to determining that the binary translation does not exist for the hotspot, a translation thread to: (i) generate the binary translation for the hotspot and (ii) enqueue the binary translation for installation when completed, wherein enqueuing the binary translation for installation comprises generating the pending global translation cache operation; and further comprising: determining, in the non-preemptive mode and in the global thread, whether the binary translation has been enqueued for installation; and installing, in the preemptive mode and in the global thread, the binary translation into the global translation cache in response to determining that the binary translation has been enqueued for installation.

31. The method of claim 27 , further comprising installing locally on the processor core, in the non-preemptive mode in the binary translation scheduler, the binary translation from the global translation cache in response to determining that the binary translation exists for the hotspot.

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6. The method of claim 5 , wherein the extracting each pattern of the plurality of patterns comprises using a suffix link when traversing the suffix tree.

5. The method of claim 1 , wherein the identifying the pattern, comprises: building, by the processor, a suffix tree for each one of the plurality of documents that are clustered; extracting, by the processor, each pattern of a plurality of patterns from the suffix tree for the each one of the plurality of documents that are clustered; and identifying, by the processor, the pattern from the plurality of patterns that occurs above a predefined number of documents of the plurality of documents that are clustered.

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12. A method for collaborative review of a clinical content structure operating on a programmed computer, the method comprising: displaying, using a server, a first default clinical content structure provided by an information provider to a first set of users of a first authoring environment for reviewing and modifying the first default clinical content structure, wherein the clinical content structure comprises a set of one or more evidence-based options that are selectable by a clinician during patient care and the first authoring environment operates on a first set of one or more programmed computers associated with a first protocol; displaying, using a server, the first default clinical content structure provided by an information provider to a second set of users of a second authoring environment for reviewing and modifying the first default clinical content structure, wherein the second authoring environment operates on a second set of one or more programmed computers associated with a second protocol, the first set of one or more programmed computers and the second set of one or more programmed computers are communicatively coupled to a server via a network, and the first protocol and the second protocol are different; enabling, using a server, the first set of users and the second set of users to collaboratively input modification data associated with the first default clinical content structure by granting the first set of users access to the first authoring environment and granting the second set of users access to the second authoring environment; modifying, using a server, the first default clinical content structure based on the modification data received from the first set of users of the first authoring environment and the second set of users of the second authoring environment to create a modified clinical content structure; storing, using a server, a first plurality of data translation rules associated with the first authoring environment, wherein each data translation rule of the first plurality of data translation rules maps at least one term associated with the first authoring environment to one or more terms of one or more standard term libraries; storing, using a server, a second plurality of data translation rules associated with the second authoring environment, wherein each data translation rule of the second plurality of data translation rules maps at least one term associated with the second authoring environment to one or more terms of the one or more standard term libraries; automatically translating, using a server, the modified clinical content structure into a first standard structure using the first plurality of data translation rules; automatically translating, using a server, the modified clinical content structure into a second standard structure using the second plurality of data translation rules; converting, using a server, the first standard structure into a first export structure that is executable by the first protocol of the first set of one or more programmed computers; converting, using a server, the second standard structure into a second export structure that is executable by the second protocol of the second set of one or more programmed computers; transmitting, using a server, the first export structure to the first set of one or more programmed computers and the second export structure to the second set of one or more programmed computers; identifying, using a server, a second default clinical content structure that comprises an updated version of the first default clinical content structure; and providing, using a server, the second default clinical content structure to the first set of users of the first authoring environment and the second set of users of the second authoring environment, wherein the first set of users and the second set of users are enabled to use the second default clinical content structure.

18. The method of claim 12 wherein the authoring environments are associated with different medical institutions.

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18. The animation server system of claim 17 , wherein the metadata for the first animation further comprises whether the 3D character in the first animation ends on a floor level, a higher level, or a lower level than at the start of the first animation.

17. The animation server system of claim 16 , wherein the metadata for the first animation comprises: information about a start and end pose; a number of frames of the first animation; a global orientation of the 3D character in a first frame of the first animation; a global orientation of the 3D character in a last frame of the first animation; whether the first animation is in place or in motion; and whether there is any foot planting in the first animation.

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18. The system of claim 12 , wherein processing the message further comprises substituting a word in the message with a non-inflected lexical form of the word.

12. A system comprising: a processor; a computer-readable storage medium storing instructions which, when executed by the processor, cause the processor to perform operations comprising: receiving a message comprising a subject, a first sentence and a second sentence; processing the message to yield a processed subject based on the subject, a processed first sentence based on the first sentence, and a processed second sentence based on the second sentence, wherein processing the message comprises identifying words in the message that are of a predefined word type; and selecting exactly one of the first sentence or the second sentence as a summary text, which summarizes the message, based on: (i) a first number of words of the predefined word type in the processed first sentence; (ii) a second number of words of the predefined word type in the processed second sentence; (iii) first overlapping words, of the predefined word type, occurring both in the processed subject and the processed first sentence; and (iv) second overlapping words, of the predefined word type, occurring both in the processed subject and the processed second sentence.

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14. The media of claim 9 , wherein determining the first score for each grammar is based on the identified edges corresponding to the query tokens of the grammar.

9. One or more computer-readable non-transitory storage media embodying software that is operable when executed to: access a social graph comprising a plurality of nodes and a plurality of edges connecting the nodes, each of the edges between two of the nodes representing a single degree of separation between them, the nodes comprising: a first-user node corresponding to a first user associated with an online social network; and a plurality of second nodes that each correspond to a concept or a second user associated with the online social network; receive, from a client system of the first user, an unstructured text query inputted by the first user, wherein the unstructured text query comprises one or more n-grams; identify, based on the unstructured text query, one or more edges and one or more second nodes of the social graph, each of the identified edges or identified nodes corresponding to at least one of the n-grams, wherein identifying the one or more edges and one or more second nodes comprises: determine a second score for each n-gram that corresponds to one of the edges or second nodes; select one or more edges having a second score greater than an edge-threshold score, each of the identified edges corresponding to at least one of the n-grams; and select one or more second nodes having a second score greater than a node-threshold score, each of the identified second nodes being connected to at least one of the identified edges, each of the identified second nodes corresponding to at least one of the n-grams; access a context-free grammar model comprising a plurality of grammars, each grammar comprising one or more query tokens; identify one or more grammars, each identified grammar having one or more query tokens corresponding to at least one of the identified second nodes and at least one of the identified edges of the social graph; determine a first score for each identified grammar; generate one or more structured queries, each structured query corresponding to an identified grammar having first score greater than a grammar-threshold score wherein the structured query comprises a natural-language string generated by the identified grammar, each structured query comprising the query tokens of the corresponding identified grammar, wherein one or more of the query tokens of the structured query corresponds to at least one of the identified second nodes and at least one of the identified edges of the social graph; and send, to the client system of the first user, one or more of the structured queries as suggested queries for display to the first user in response to the unstructured text query inputted by the first user.

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1. A method of imbuing an artificial intelligence system with idiomatic traits, the method comprising: collecting, by one or more processors, electronic units of speech from an electronic stream of speech, wherein the electronic stream of speech is generated by a first entity; identifying, by one or more processors, tokens from the electronic stream of speech, wherein each token identifies a particular electronic unit of speech from the electronic stream of speech, and wherein identification of the tokens is semantic-free such that the tokens are identified independently of a semantic meaning of a respective electronic unit of speech; populating, by one or more processors, nodes in a first speech graph with the tokens; identifying, by one or more processors, a first shape of the first speech graph; matching, by one or more processors, the first shape to a second shape, wherein the second shape is of a second speech graph from a second entity in a known category; assigning, by one or more processors, the first entity to the known category in response to the first shape matching the second shape; modifying, by one or more processors, synthetic speech generated by an artificial intelligence system based on the first entity being assigned to the known category, wherein said modifying imbues the artificial intelligence system with idiomatic traits of persons in the known category; and incorporating, by one or more processors, the artificial intelligence system with the idiomatic traits of persons in the known category into a robotic device in order to align the robotic device with cognitive traits of the persons in the known category.

3. The method of claim 1 , wherein the first entity is a person, wherein the electronic stream of speech is an electronic recording of a stream of spoken words from the person, and wherein the method further comprises: receiving, by one or more processors, a physiological measurement of the person from a sensor, wherein the physiological measurement is taken while the person is speaking the spoken words; analyzing, by one or more processors, the physiological measurement of the person to identify a current emotional state of the person; modifying, by one or more processors, the first shape of the first speech graph according to the current emotional state of the person; and further modifying, by one or more processors, the synthetic speech generated by the artificial intelligence system based on the current emotional state of the person according to the modified first shape.

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17. A computer readable storage device storing a software program to cause a computing device to: determine a user context based on a tunable parameter; determine a first aspect of the user context and a second aspect of the user context, wherein the first aspect of the user context includes data indicative of text being accessed by a user and the second aspect of the user context includes data indicative of at least one user task from a plurality of user tasks, wherein the at least one user task is determined based upon the user context of the user's interaction with one or more software applications; formulate a first query and a second query based on the first aspect of the user context, the first query and the second query being different than the user context; submit the first query to a first search engine; receive a first plurality of search results from the first search engine, the first plurality of search results being based on the first query; submit the second query to a second different search engine; receive a second plurality of search results from the second different search engine, the second plurality of search results being based on the second query; determine a first plurality of scores associated with the first plurality of search results at least in part by comparing data indicative of the first plurality of search results to data indicative of the first aspect of the user context; determine a second plurality of scores associated with the second plurality of search results at least in part by comparing data indicative of the second plurality of search results to the data indicative of the first aspect of the user context; and display a subset of the plurality of search results on a client device, wherein at least one of the first plurality of search results is filtered from being displayed to the user based on at least a portion of the first plurality of scores, and wherein at least one of the second plurality of search results is filtered from being displayed to the user based on at least a portion of the second plurality of scores.

19. The computer readable storage device of claim 17 , wherein the user context is based on at least one of (a) a number of words in the text being accessed by the user, and (b) a number of sentences in the text being accessed by the user.

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1. A method for detecting hidden information embedded in a document comprising characters, wherein, a document for detection is formed by embedding hidden information in an original document by performing layout transformation on characters in the original document according to a predetermined embedding rule, and the method comprises: determining layout transformation for each character in the document for detection compared with the original document; obtaining a code sequence embedded in the document for detection based on the layout transformation of each character in the document for detection and the predetermined embedding rule; decoding the code sequence to get the hidden information embedded in the document for detection; wherein, the predetermined embedding rule comprises: acquiring a code sequence for each class of layout transformation by coding the hidden information to be embedded in the class of layout transformation; wherein, each class of layout transformation is considered as a channel to embed the hidden information; selecting the characters to be subjected to the layout transformation for each class of the layout transformation from the document respectively, length of the characters to be subjected to the layout transformation being larger than or equal to length of the code sequence corresponding to each class of the layout transformation; performing the layout transformation on the selected characters according to the acquired code sequence for each class of the layout transformation in a cyclic application, each value in the code sequence for each class of the layout transformation corresponding to a transformation number of each class of the layout transformation.

7. The method according to claim 1 , wherein obtaining a code sequence based on the layout transformation of each character in the document for detection and the predetermined embedding rule comprises: obtaining the code sequence based on the known code length and the transformation number for the layout transformation of each character in the document for detection.

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1. A translation method, comprising: compiling, by a developer using a computer system, a set of files including non-localizable files and localizable files into a set of install files, the localizable files including string data from at least one source in a first human language for translation into a target human language, the non-localizable files lacking data requiring translation from the first human language into the target human language, the install files for installation onto a computer, the non-localizable files including a computer-executable binary file; extracting, by a foreign country vendor using a translation tool executed by a computer remote from the computer system, the string data and exporting the extracted string data to a formatted file, the foreign country vendor being located in a country different from the developer; executing the translation tool independently of whether any of the non-localizable files are executed; receiving, at the remote computer, a translated formatted file that includes translated string data, the translated string data corresponding to the extracted string data that has been at least partially translated from the first human language into a target human language; importing, by the remote computer, the translated string data into the localizable files to produce translated localizable files and replacing the localizable files with translated localizable files that includes the imported translated string data; and packaging the translated localizable files together with the non-localizable files into a set of target install files without requiring recompilation from source code to computer-executable code of any of the non-localizable files including the binary file.

6. The method of claim 1 , wherein the formatted file is in a spreadsheet format.

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20. The computer-readable non-transitory storage medium of claim 16 , wherein the testing said plurality of tuning recommendations further comprises measuring an execution time period for executing the particular database query language statement with said one or more tuning recommendations enabled.

16. A computer-readable non-transitory storage medium storing instructions, wherein the instructions include instructions which, when executed by one or more processors, cause the one or more processors to perform steps of: from a workload set, automatically selecting a plurality of database query language statements for automatic tuning, wherein the workload set comprises multiple database query language statements and performance data for the multiple database query language statements; automatically tuning the plurality of database query language statements until one or more time periods for said tuning the plurality of database query language statements are reached or exceeded, thereby generating a plurality of tuning recommendations for a subset of database query language statements of said plurality of database query language statements, each tuning recommendation of said plurality of tuning recommendations being a tuning recommendation for a database query language statement of said subset of database query language statements; automatically testing the plurality of tuning recommendations against a database, wherein testing the plurality of tuning recommendations comprises, for one or more tuning recommendations of said plurality of tuning recommendations, automatically executing a particular database query language statement from said subset of database query language statements with said one or more tuning recommendations enabled thereby automatically generating an execution plan for the particular database query language statement based on said one or more tuning recommendations enabled for testing and automatically testing said execution plan against one or more other alternative execution plans for the particular database query language statement generated based on enabling, for testing, one or more other tuning recommendations from the plurality of tuning recommendations; based at least in part on automatically testing the plurality of tuning recommendations against the database, automatically implementing at least one tuning recommendation of said plurality of tuning recommendations and not implementing at least another tuning recommendation of said plurality of tuning recommendations.

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1. A computer-implemented method practiced on a client device, comprising: receiving a new term from an application on the client device; segmenting the new term into a set of n-grams; applying a differential privacy algorithm to a selected n-gram in the set of n-grams, generating a differentially private n-gram sketch; selecting a row of the differentially private n-gram sketch; storing the new term and selected row of the differentially private n-gram sketch to a sample buffer of candidates for transmission to a new term learning server.

10. The method of claim 1 , further comprising: selecting a classification of terms, wherein at least one new term in the sample buffer has the selected classification; randomly selecting a new term having the classification from the sample buffer; and transmitting the new term and differentially private n-gram sketches to a new term learning server.

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15. The computer-implemented system of claim 10 , wherein the system is further programmed to determine that changes have been made in the document, identify one or more rows whose height could be affected by the changes, and mark the one or more rows in the cache as dirty, without immediately making a new determination of the heights of the one or more rows in the cache identified as dirty.

16. The computer-implemented system of claim 15 , wherein the system is further programmed to, after multiple instances of determining that changes have been made in the document, and associated marking of identified rows as dirty, identify a batch of dirty rows, determine heights for each of the identified rows, and record the heights in the cache for the identified rows.

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7. The method of claim 1 wherein the deletion and addition of the original fixed sized code word node includes: ordering the nodes in the dictionary in a binary tree by the time they are used to represent subsequent strings of data; and moving the node representing the original fixed sized code word to the top of the order in the binary tree when the code word is used to represent a subsequent string of data.

1. A method of compressing digital data for transmission over a communications channel, the method comprising: determining strings of digital data; assigning a fixed size code word to a string of digital data; storing the fixed size code word and corresponding string of digital data in a dictionary as a node; storing subsequent fixed size code words as new nodes in the dictionary representing subsequent different strings of digital data ahead of the node storing the first fixed sized code word; examining a subsequent part of digital data for the string of digital data; and deleting the fixed size code word's node from the dictionary and adding the fixed size code word's node to the dictionary when a match is made between the string of digital data and the subsequent part of the digital data.

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1. A system for providing web services for a business hierarchy, comprising: a web server comprising a processor and computer-readable storage media communicatively coupled to a network; a website resident on the storage media of the webserver and made available on the network by the webserver, wherein the website is configurable to display content to a user in one of a plurality of operational modes, each operational mode corresponding to a geographic granularity of the business hierarchy, the operational modes including a local mode comprising content pertaining to a particular location, a regional mode comprising content pertaining to a plurality of locations, and a national mode comprising information pertaining to a plurality of regions; and a configuration module communicatively coupled to the website to configure the website to display content to a user in a selected operational mode responsive to a request from the user for content from the web server, wherein the operational mode is selected based on a geographic granularity associated with the user.

4. The system of claim 1 , wherein the plurality of operational modes comprises one or more operational modes corresponding to an advertising association within the business hierarchy.

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10. The method of claim 9 , the user preference cues comprising itinerary information stored within a memory of the electronic device.

9. The method of claim 4 , the one or more contextual cues further comprising user preference cues, further comprising filtering, with the one or more processors, a plurality of destinations satisfying the request for navigational information as a function of the transit mode, the direction of transit, the speed of transit, the geo-location, and the user preference cues to select a destination for the navigation information response.

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16. An infusion pump assembly comprising: a reservoir assembly configured to contain an infusible fluid; a motor assembly configured to act upon the reservoir assembly and dispense at least a portion of the infusible fluid contained within the reservoir assembly; processing logic configured to control the motor assembly; wherein the processing logic includes: one or more circuit partitioning components configured to divide the processing logic into primary processing logic and backup processing logic; a primary microprocessor included within the primary processing logic and configured to execute one or more primary applications written in a first computer language; and a safety microprocessor included within the backup processing logic and configured to execute one or more safety applications written in a second computer language that is different than the first computer language.

20. The infusion pump assembly of claim 16 wherein the first computer language is chosen from the group consisting of Ada, Basic, Cobol, C, C++, C#, Fortran, Visual Assembler, Visual Basic, Visual J++, Java, and Java Script.

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6. The machine-readable medium of claim 4 , the process further comprising: in response to the conditional variant corresponding to an implicit condition, assigning a probability to each conditional variant model associated with the target attribute, using Bayesian reasoning and observed data of the current driving session.

4. The machine-readable medium of claim 1 wherein probabilistically determining which of the plurality of conditional variant models associated with the target attribute currently corresponds to a condition of the target attribute comprises: determining if a conditional variant corresponds to an explicit or implicit condition.

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7. The device according to claim 1 , wherein the character description generator is arranged to acquire, as the character description, a description of a character which describes the character as an ordered description of identified elements of writing.

1. A device for detecting characters in an image, comprising: a Hough transformer arranged to identify, as identified elements of writing, circular arcs or elliptical arcs in the image or in a preprocessed version of the image; a character description generator arranged to acquire, on the basis of the identified circular arcs or elliptical arcs, a character description which describes locations of the identified circular arcs or elliptical arcs; and a database comparator arranged to compare the character description with a plurality of comparative character descriptions which have character codes associated with them, so as to provide, as a result of the comparison, a character code of a detected character; wherein the Hough transformer is arranged to identify, as identified elements of writing, individual circular arcs or elliptical arcs, which approximate a course of line of a character within a surrounding of local extremes, in the image or in a preprocessed version of the image, and to provide information about an orientation of an identified circular arc or elliptical arc; and the character description generator is arranged to acquire, based on the identified circular arcs or elliptical arcs and the information about the orientation, a character description which describes locations of the individual identified circular arcs or elliptical arcs.

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1. A system that facilitates text recognition comprising: a word group component for recognition of word groups that are predefined based on joining rules of a language associated with the text, the joining rules defining a Part of an Arabic word (PAW), the word component to label the word groups based on relative horizontal overlaps between the word groups, and to extract features from the text to recognize the word groups of the text utilizing a first classifier to scale the text to a fixed size grid while maintaining an aspect ratio of the text and a second classifier based on directional codes associated with the word groups; and a letters component for recognition of constituent letters that form the word groups, the letters component to recognize the constituent letters substantially simultaneously as the word group component to recognize the word groups.

9. The system of claim 1 , wherein the word group component utilizes a Beam search to recognize the word groups.

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1. A method for optimizing search, the method, comprising: identifying a location identifier of a webpage having content associated with a semantic type; extracting a pattern from the location identifier of the webpage that is different from the location identifier but including a portion of the location identifier; storing the pattern in a computer database embodied in a computer-readable storage medium; wherein, the pattern corresponds to a semantic type of the content in the webpage; using the pattern stored in the computer-readable storage medium, performing pattern recognition on multiple location identifiers having a same domain name as the location identifier; detecting location identifiers from the multiple location identifiers matching the pattern extracted from the location identifier; identifying, a set of type-determined web pages having the matching location identifiers; wherein, each of the set of type-determined web pages has content that is related to the semantic type; wherein, the semantic type is associated with multiple attributes that are user-defined; wherein, the pattern is extracted from the location identifier using user input; data mining the content of each of the set of type-determined web pages to further determine relevancy to the semantic type; and identifying a refined set of type-determined web pages from the set of type-determined web pages based on the relevancy to the semantic type determined via the data mining.

22. The method of claim 1 , wherein, the pattern includes a domain name segment or a wildcard segment.

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5. The system of claim 1 , wherein the operations further comprise: determining a fourth token value of the message element, different from the first token value and the second token value, from the token values; in response to receiving a second indication related to transitioning the message element to a third device display area of the device display, determining a third layout of the message comprising the message element displayed based on the third token value and the third device display area, wherein the determining the third layout comprises complying with the scaling rule that is related to the selectable ratio of an updated summed token area and the messaging environment area of the device display; and facilitating a third display of the message on the device display based on the third layout.

7. The system of claim 5 , wherein the receiving the second indication comprises receiving newer message content newer than the first message content, and wherein the selectable ratio results in a minimum number of rows of text that are displayed in conjunction with the message element at the fourth token value.

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17. The system of claim 13 , wherein the system further comprises instructions to determine the likelihood based on a question posed about the segment of text.

13. A system including memory and one or more processors operable to execute instructions stored in the memory, the memory comprising instructions to: identify, in a segment of text, a subject and an action performed by the subject; determine a likelihood that the action is performable by a class of subjects with which the subject is associated, the likelihood based at least in part on a plurality of reference subjects and associated reference actions found in a corpus of textual documents; and provide an indication of the likelihood.

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10. The method of claim 1 , further comprising: providing the visual gesture to a computer system having a video sensor; and activating one or more computer commands associated with the computer system using the visual gesture.

1. A computer-implemented method for recognizing a visual gesture, the method comprising: receiving a visual gesture formed by a part of a human body, the visual gesture being captured in a video having a plurality of video frames; determining a region of interest (ROI) in the plurality of video frames of the video based on motion vectors associated with the part of the human body, a centroid of the ROI aligned to be a centroid of a cluster of the motion vectors; selecting a visual gesture recognition process based on a user selection of a visual gesture recognition process from a plurality of visual gesture recognition processes; applying the selected visual gesture recognition process to the plurality of video frames to recognize the visual gesture; determining variations in the centroid, shape, and size of an object within the ROI of the plurality of video frames, the centroid, shape, and size of the object changing according to motion of the object in the plurality of video frames in an affine motion model, wherein said determination of the variations in the centroid, shape and size of the object within the ROI is performed by a track-learning-detection-type (TLD-type) process, wherein the TLD-type process is a signal processing scheme in which following functions are performed simultaneously: object tracking, by use of motion estimation in the affine motion model, either using optical flow, or block-based motion estimation and employing estimation error metrics comprising a sum of absolute differences (SAD) and normalized correlation coefficient (NCC); object feature learning, which automatically learns features of objects within the ROI, the features including size, centroids, statistics and edges; and object detection comprising: feature extraction employing edge analysis, spatial transforms, and background subtraction, feature analysis employing clustering and vector quantization, and feature matching employing signal matching using similarity metrics, neural networks, support vector machines, and maximum posteriori probability; and deriving three or more dimensional information and relationships of objects contained in the visual gesture from the plurality of video frames capturing the visual gesture based on the analysis of the variations in the centroid, shape, and size of the object within the ROI.