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8. The sleeve according to claim 6 , wherein the transfer structure is constructed of a metal selected from titanium and titanium alloys.

6. The sleeve according to claim 1 , wherein the transfer structure is constructed of a patient implantable metal.

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1. A method of explicitly declaring expected relationships between expected entities and a reference entity in a model, said reference entity have a reference data type definition associated therewith, said method comprising: creating an expected targets tag in a markup language schema type declaration in the model; defining one or more elements describing one or more expected entities according to the created expected targets tag in the model; identifying a predefined entity relationship, said described one or more expected entities being expected entities based on the predefined entity relationship and are not required entities based on the predefined entity relationship; associating the expected targets tag with the defined one or more elements and the identified, predefined entity relationship; locating the expected entities in the reference data type definition having the expected targets tag and associated elements and predefined entity relationship; and declaring a relationship between the located expected entities and the reference entity in response to the locating.

2. The method of claim 1 , wherein defining the one or more elements comprises defining an element representing an expected quantity of one of the expected entities.

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9. An apparatus for removing a mark in a document image, the apparatus comprising: an extracting device configured to extract connected components from a binary image corresponding to the document image; a clustering device configured to cluster the connected components based on grayscale features of the connected components to obtain one clustering center; a searching device configured to search, within numerical ranges of a clustering radius R from the clustering center and a grayscale threshold T, for a combination (R, T) which causes an evaluation value based on the grayscale features of the connected components to be higher than a first evaluation threshold; and a fine removing device configured to remove the mark in the document image based on the grayscale threshold in the combination; wherein the grayscale features of the connected components comprise: a minimum one of grayscale values of pixels in the document image, which correspond to all black pixels in each of connected components; wherein the fine removing device is further configured to remove the connected components, the grayscale features of which are greater than the grayscale threshold, as the mark in the document image.

12. The apparatus according to claim 9 , wherein the evaluation value is further based on a number of black pixels in one connected component in the binary image.

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2. A method for providing informational services using a portable navigational apparatus configured to communicate with a server via a wireless communications network, the method comprising: inputting a proprietary search term to find one or more locations of an entity within a geographical area into the navigational apparatus; sending a search query comprising the proprietary search term via the wireless communications network to the server to allow the server to access a unified geographic database (“UGD”) to identify the entity within the geographical area uniquely associated with the proprietary search term, and to use locational information associated with the navigational apparatus to complete a search of the UGD for one or more locations associated with the one or more entities satisfying the search query and limited in geographic scope by the locational information; adding locational information associated with the navigational apparatus to the proprietary search term to limit geographic scope of the search query before sending the search query; receiving a search result from the server via the wireless communications network, the search result comprising one or more locations identified by the proprietary search term that have a relationship with the locational information; and outputting the one or more locations comprising the search result.

24. The method of claim 2 , wherein the locational information comprises a predefined location stored on the navigational apparatus.

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24. The mobile face-to-face interaction monitoring method according to claim 23 , wherein the creating the volume topography based on the sound signals is performed by using a feature vector P(t), wherein the feature vector P(t) is defined as P(t)=(p(t,1), p(t,2), . . . , p(t,np)), where p(t, i) is an average of a square of the sound signals in each mobile device i of the mobile devices at a given time t, and where np is a quantity of the mobile devices in the conversation group.

23. The mobile face-to-face interaction monitoring method according to claim 20 , wherein: the creating the volume topography based on the sound signals is performed during a training period; and the determining the turn by using the volume topography comprises determining current turn by matching current sound signals with the volume topography, after the training period.

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19. The system of claim 18 , further comprising a translation component for translating the processed query into a natural language other than the natural language of the input query.

20. The system of claim 19 , further comprising memory which stores a list of named entities and their translations into the other natural language.

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9. The method of claim 8 wherein: deriving the set of user-specific recognition properties based on the facial expression scores received in response to displaying the plurality of test face images to the user includes storing a plurality of 3-dimensional reference models corresponding to a subset of the plurality of test face images, each 3-dimensional reference model being stored in association with a facial expression score for one or more particular emotions received from the user with respect to a test face image generated from that 3-dimensional reference mode, each 3-dimensional reference model including a set of polygons, each polygon including a set of vertices and a texture map; and generating the set of one or more face images including referencing the derived set of user-specific recognition properties includes: performing a weighted 3-dimensional morphing operation between a 3-dimensional neutral face model and a 3-dimensional reference model of a face stored in association with a facial expression score received from the user for the particular emotion to yield a resulting 3-dimensional model, the weighted 3-dimensional morphing operation including morphing of the set of the vertices and texture maps of each polygon from the 3-dimensional neutral face model to the 3-dimensional reference model using the facial expression score as a weight; and rendering the resulting 3-dimensional model using 3-dimensional graphical rendering.

8. The method of claim 1 wherein: the method further comprises, prior to receiving the authentication request, engaging in an enrollment process for the user, the enrollment process including: displaying a plurality of test face images to the user; for each test face image of the plurality of test face images, receiving, from the user, a facial expression score for a particular emotion; and deriving a set of user-specific recognition properties based on the facial expression scores received in response to displaying the plurality of test face images to the user; and generating the set of one or more face images, each face image of the set of face images having the facial expression score for the particular emotion associated with that face image, the facial expression scores being specific to the user, includes referencing the derived set of user-specific recognition properties.

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19. The method of claim 17, wherein each attribute has one or more properties and each attribute is associated with a corresponding profile that defines default values for the one or more properties, the computer system storing for each profile a list of attributes that are associated with the profile, the method further comprising the steps of: determining whether a user has modified a property of a profile; and automatically updating the corresponding property in each attribute that is associated with the profile to conform to the modified property.

22. The method of claim 19, wherein the one or more properties defined by the simple value profiles, group profiles and object link profiles include a minimum cardinality property that defines a minimum number of data entries that are stored in the relational database for the corresponding attribute and a maximum cardinality property that defines a maximum number of data entries that are stored in the relational database for a corresponding attribute, wherein the step of translating the semantic objects and their associated attributes in the semantic object model into a relational database schema, further comprises the steps of: analyzing each attribute associated with a semantic object to determine if the attribute is a simple value attribute and if so: reading the maximum cardinality property of the simple value attribute; automatically creating an additional column in the relational database table created for the semantic object with which the simple value attribute is associated, to store a data entry for the simple value attribute if the maximum cardinality is less than or equal to one; and otherwise automatically creating a separate relational database table for the attribute, the separate relational database table having at least two columns to store multiple data entries for the simple value attribute and one or more foreign keys that link an entry in the separate relational data base table to an entry in the relational database table created for the semantic object with which the simple value attribute is associated.

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18. A non-transitory computer-readable storage medium encoded with instructions executable by at least one processor to: output, for display at a presence-sensitive display, a graphical user interface comprising a text input field and a graphical keyboard that includes a group of keys; receive an indication of an initial portion of a continuous gesture; determine, based on the initial portion of the continuous gesture, a sequence of keys from the group of keys; output, for display at the text input field, one or more characters associated with the sequence of keys determined based on the initial portion of the continuous gesture; after receiving the indication of the initial portion of the continuous gesture, receive an indication of a subsequent portion of the continuous gesture; responsive to determining that at least one feature of the subsequent portion of the continuous gesture indicates a user intended cancellation of the initial portion and the subsequent portion of the continuous gesture, determine a probability that the subsequent portion of the continuous gesture in combination with the initial portion of the continuous gesture represents a selection of letters to complete any potential word in a language model of the computing device, wherein the at least one feature comprises a zigzag pattern having first and second groups of vertices that are each associated with a respective area of the graphical user interface that is substantially the same size or smaller than one of the keys in the graphical keyboard; and after outputting the one or more characters at the text input field of the graphical user interface and in response to determining that the probability is below a threshold, omit, from the text input field of the graphical user interface, all characters determined based on the continuous gesture including the one or more characters determined based on the initial portion of the continuous gesture.

26. The non-transitory computer-readable storage medium of claim 18 , wherein the instructions, when executed, further cause the at least one processor to: after receiving the indication of the subsequent portion of the continuous gesture, receive an indication of a third portion of the continuous gesture; determine, based only on the third portion of the continuous gesture, a word in the language model; after omitting the one or more characters from the text input field of the graphical user interface, output, for display at the text input field of the graphical user interface, the word.

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6. The method of claim 1 , further comprising: preventing access to the suspected domain name in response to the assigning the second non-zero likelihood.

1. A method comprising: parsing a character string associated with the suspected domain name of the suspected domain to identify a character in the character string that has a known likelihood of being deceptively substituted for a corresponding legitimate character in a legitimate domain name of the legitimate domain wherein the generating comprising substituting the identified character with the corresponding legitimate character generating an alternate domain name from a suspected domain name; submitting the alternate domain name to a domain name server for resolution; when the alternate domain name is resolved, assigning, a first non-zero likelihood that the suspected domain name is a counterfeit of the legitimate domain; retrieving, by the processor, a first webpage associated with the suspected domain name; retrieving, by the processor, a second webpage associated with the alternate domain name corresponding to the first web page associated with the suspected domain name; computing similarity between a first webpage associated with the suspected domain name and a second webpage associated with the alternate domain name, wherein the second webpage corresponds to the first web page associated with the suspected domain name; in response to the computing, assigning, a processor, a second non-zero likelihood that the suspected domain name is a counterfeit of the legitimate domain, wherein the second non-zero likelihood is greater than the first non-zero likelihood: determining that the first webpage comprises a request for input of personal information; and in response to the determining, assigning, by the processor, a third non-zero likelihood that the suspected domain is a counterfeit of the legitimate domain, wherein the third non-zero likelihood is greater than the second non-zero likelihood.

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17. The method of claim 1 further including the step of determining the resemblance of the data objects in real time.

1. A computer-implemented method of determining the resemblance of a plurality of data objects, comprising the steps of: parsing each data object into a canonical sequence of tokens; grouping overlapping sequences of the tokens of each data object into shingles; assigning a unique identification element to each shingle; permuting the elements of the data objects to form image sets; selecting a predetermined number of minimum elements from each image to form a sketch; partitioning the selected elements of each sketch into a plurality of groups; and assigning another unique identification to each group to generate the features of each data object to determine a level of resemblance of the plurality of data objects.

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2. The method of claim 1 wherein the additional data comprises audio data.

7. The method of claim 2 wherein the audio data comprises speech data and processing said speech data comprises obtaining context keywords from said speech data.

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3. The method according to claim 2 , wherein the one or more linguistic applications comprises one or more of categorization, summarization, and translation.

2. The method according to claim 1 , further comprising processing the one or more sequences of delimited strings in the input data with one or more linguistic applications.

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12. The computer system of claim 1 , wherein the second plurality of algebraic relations composed from the second query language statement have a single operator and a number of operands in the range of from one to three.

1. A computer system comprising: (a) at least one processor; (b) at least one memory, wherein the at least one memory includes a relation store and a data set information store; and (c) computer program instructions stored in the memory and configured to be executed by the processor to provide a requested data set, including: (i) instructions for receiving a first query language statement referencing a plurality of data sets; (ii) instructions for storing information in the data set information store regarding the data sets referenced in the first query language statement, including temporal information regarding the data sets referenced in the first query language statement; (iii) instructions for composing a first plurality of algebraic relations referencing the data sets specified in the first query language statement, wherein each of the algebraic relations in the first plurality of algebraic relations comprises a respective first expression including a symbolic representation of at least a first respective data set, a respective second expression including a symbolic representation of at least a second respective data set, and a relational operator symbolically defining a mathematical relationship between the respective first expression and the respective second expression; (iv) instructions for storing the first plurality of algebraic relations in the relation store; (v) instructions for receiving a second query language statement referencing a second plurality of data sets; (vi) instructions for composing a second plurality of algebraic relations referencing the data sets specified in the second query language statement; (vii) instructions for storing the second plurality of algebraic relations in the relation store; (viii) instructions for providing the requested data set in response to the second query language statement using at least one algebraic relation from the first plurality of algebraic relations and at least one algebraic relation from the second plurality of algebraic relations; and (ix) instructions for removing at least some of the first plurality of algebraic relations from the relation store based, at least in part, on the temporal information regarding the data sets referenced in the first query language statement.

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1. A computer-based method of processing a document having a plurality of input controls, the method comprising: a) in response to a command to display the plurality of input controls, attaching the plurality of input controls to the document; b) in response to a command to hide the plurality of input controls, detaching the plurality of input controls from the document; c) inserting a substitute control in the document and displaying the substitute control in response to the command to hide the plurality of input controls, the command to display the plurality of input controls is invoked by a user selection of the substitute control; and d) sending a value of each input control to a server.

6. The computer-based method of claim 1 , further comprising employing a script associated with the document to insert a field into the document and store the value of each input control in the field.

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

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

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2. The system according to claim 1 , wherein the search server further comprises: a processing module that preprocesses the search results, the preprocessing comprising defining thresholds; and a classification database that stores language modules of various fields.

3. The system according to claim 2 , wherein the thresholds comprise the cluster-name threshold and a correlation threshold.

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17. The system of claim 15 , wherein the first label is generated in response to the proximity score of the second image being above a proximity score threshold and a confidence score of the second label of the second image being above a confidence score threshold.

15. A system, comprising: a data processing apparatus; a data store storing instructions executable by the data processing apparatus and upon execution by the data processing apparatus cause the data processing apparatus to perform operations comprising: accessing, by the data processing apparatus, images stored in an image data store, the images being associated with respective sets of labels, each label describing content depicted in an image and having a respective confidence score that is a measure of confidence that the label accurately describes the content depicted in the image; accessing, by the data processing apparatus, one or more proximity metric values associated with a first image of the images; accessing, by the data processing apparatus, one or more proximity metric values associated with a second image of the images; determining, by the data processing apparatus, a proximity score for the second image based on a comparison of the proximity metric values of the first image and corresponding proximity metric values of the second image, the proximity score being a measure of a relatedness of the second image to the first image; and identifying, by the data processing apparatus, a second label associated with the second image, the second label having a second confidence score that is a measure of confidence that the second label accurately describes the content depicted in the second image; generating, by the data processing apparatus, a first label for the first image based on the second label, the proximity score for the second image, and the second confidence score for the second label; and determining, by the data processing apparatus and for the first label, a first confidence score based on the proximity score for the second image and the second confidence score for the second label, the first confidence score being a measure of confidence that the first label accurately describes the content depicted in the first image.

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15. The method of claim 1 , wherein a search space used by the speech recognition system for the given input utterance is reduced to a region that satisfies the double gain constraint.

1. A method comprising: determining an original receiver operating characteristic (ROC) curve describing performance of a speech recognition system with respect to an original rate of false acceptance (FA) of the speech recognition system versus an original rate of correct acceptance (CA) of the speech recognition system; changing an algorithm used by the speech recognition system for sentence confidence scores, wherein changing the algorithm results in a new ROC curve with respect to a new rate of FA of the speech recognition system versus a new rate of CA of the speech recognition system; receiving a user specification of relative importance of the new rate of FA versus the new rate of CA; and based on the relative importance of the new rate of FA versus the new rate of CA, adjusting a confidence scoring functionality related to recognition reliability for a given input utterance, wherein at or above a given operating point of the speech recognition system, the new ROC curve reflects a double gain constraint relative to the original ROC curve, such that the new rate of FA is equal to or less than the original rate of FA, and the new rate of CA is equal to or greater than the original rate of CA.

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6. The computer-implemented method of claim 1 , further comprising causing storage of the annotation in association with the user account and the first network address at predetermined times as the annotation is being received via the annotation interface.

1. A computer-implemented method comprising: detecting navigation of a browser at a client to a first network address for a resource available via a resource server over a computer network; receiving an indication of at least one annotation associated with the resource, wherein the annotation is input via an annotation interface exposed via the browser; enabling via the annotation interface, selection of one of a plurality of profiles established for a user account to associate with the annotation; responsive to detecting navigation of the browser to a second network address, automatically and independent of a user selection, communicating over the computer network with an annotation server to request storage of the annotation via the annotation server in association with the selected profile for the user account and the first network address without modifying the resource.

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12. The non-transitory computer-readable recording medium according to claim 11 , the morphological analysis includes detection of information of notation or the notation and a part of speech for each word in the text.

11. A non-transitory computer-readable recording medium storing a word retrieving program used in a computer of a word retrieval device and causing the computer to execute a method comprising: carrying out morphological analysis of text; extracting at least one word representing a feature of the text from words of a morphological analysis result by the analyzer, using word information quantity on the words; and retrieving text related to the at least one word from a web page using the at least one word as a retrieval query, wherein the word information quantity is represented by I x , where T x represents a power of an appearance frequency of each word and I x is defined as follows: I x = T x ∑ x = t ⁢ ⁢ T x × 100.

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4. The system of claim 1 , comprising a version control module in operative engagement with the customized report module, the version control module configured to track the version of the custom report generated by the customized report module, and, responsive to instructions received from the client, output a desired version of the custom report to the client.

5. The system of claim 4 , wherein the version control module is configured to retain the user selection instructions, including location information for the selections of interest within the plurality of source documents, in order to regenerate the desired version of the custom report to the client.

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16. An article of manufacture comprising a program storage device for storing instructions that, when read and executed by a computer system, result in the computer system performing a method of optimizing execution of a query that accesses data stored in one or more base tables in a database of the computer system, comprising: performing a first query that accesses the data stored in the base tables, in order to create at least one materialized query table (MQT); using data stored in the MQT as statistics data for determining an optimal execution plan for a second query that accesses the data stored in the base tables; and performing the second query using the optimal execution plan to access the data stored in the base tables in the database for presentation to a user.

20. The article of claim 16 , further comprising identifying the MQT as a source for statistics when the MQT is defined.

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9. An optimization apparatus comprising: a processor; a memory in operable communication with the processor; a multi-file optimized code generation (MFOCG) subsystem which includes a compiler back end and a linker, the MFOCG subsystem executable by the processor using the memory; and an incremental inter-procedural dataflow analysis (IIPDA) code which includes: a call graph input interface that receives a program call graph which identifies procedures of a program, an intermediate language representation input interface, a basis procedure set input interface, and a prior dataflow values input interface, the IIPDA also including an updated dataflow values output interface, and an impacted procedures set output interface which outputs a set IS of the procedures which are impacted directly or indirectly by a set of one or more source code edits, where the set IS is smaller than the set of all procedures identified in the call graph, wherein at least one procedure is represented in IS using at least one of the following: a compiler's internal ID of the procedure, a pointer to a function structure, a pointer to another procedure structure, a key to an entry in a group of symbols used in the program, and wherein the IIPDA is executable by the processor using the memory to transform inputs provided through the input interfaces into outputs provided through the output interfaces as part of a results-equivalent substitute for an exhaustive inter-procedural dataflow analysis by the MFOCG subsystem of all procedures identified in the program call graph.

11. The optimization apparatus of claim 9 , further comprising a basis procedure set that includes at least one procedure whose source code has been edited subsequent to a most recent compilation of the procedure.

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4. The method of claim 2 wherein embedding a physical manifestation of digital information associated with the text also comprises hiding the digital information using font height, font registration, or font spacing variations.

2. The method of claim 1 wherein embedding a physical manifestation of digital information associated with the text comprises: encoding the text with an encoding algorithm to produce the digital information; and affixing a physical manifestation of the digital information to the document.

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1. A method implemented by one or more computing devices specifically programmed to perform operations comprising: automatically, by one or more of the computing devices, parsing purchase confirmation messages corresponding to respective purchase transactions to extract sets of structured text strings for respective target purchase-related field types comprising a product description field type and one or more price field types; for each of one or more of the extracted sets of structured text strings corresponding to a respective multi-product purchase transaction and comprising a respective partial description text string for a first one of the products and one or more total price text strings for the respective purchase transaction but missing an individual price text string for the first product, by one or more of the computing devices, building a respective query as a function of the partial description text string for the first product, determining respective upper and lower price bounds for the first product as a function of one or more of the total price text strings for the respective purchase transaction, sending at least one request comprising the query to at least one network address that triggers at least one server network node to execute a search engine to return at least one electronic document comprising a respective dynamically generated ranked list of product-related items matching the query and comprising respective sets of descriptions and individual prices for respective products, wherein the product-related items in the list are unconnected to the respective purchase transaction and are derived from at least one of (i) records of purchase transactions other than the respective purchase transaction and (ii) records of products offered for purchase, selecting a product-related item in the ranked list of product-related items by evaluating one or more of the individual product prices in one or more of the product-related items against the respective upper and lower price bounds for the first product and one or more heuristics that preferentially select higher ranked product-related items over lower ranked product-related items, excerpting a respective complete product description text string and a respective individual product price text string from the selected product-related item, and augmenting the extracted set of structured text strings for the respective purchase transaction with the excerpted complete product description text string and the respective individual product price text string; and aggregating, by one or more of the computing devices, the extracted and augmented sets of structured text strings to produce actionable data for visualizing purchase graph information.

13. The method of claim 1 , wherein the sending comprises applying the query to records of respective ones of the extracted sets of structured text strings determined to be complete.

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1. A method for generating an Internetworking Operating System (IOS) Command Line Interface (CLI) configuration model using a processor, the processor executing the following method: representing a structure of an IOS CLI configuration base in a formal specification format, the IOS CLI configuration base providing cross-CLI dependencies that specify an ordering of components in the IOS CLI configuration base; representing mapping rules in the formal specification format for mapping the represented structure of the IOS CLI configuration base to a user-defined format; receiving an IOS user-defined configuration that is input by a user on the CLI for the IOS CLI configuration base; generating a machine-readable IOS CLI Dependency Tree (ICDT) from the represented structure of the IOS CLI configuration base, the ICDT being a dependency graph representing the components and the ordering of the cross-CLI dependencies, the ICDT being generated in the user-defined format using the represented mapping rules; and automatically generating a logical topology of the IOS CLI configuration model from the machine-readable ICDT and the IOS user-defined configuration by traversing the dependency graph to determine components in the dependency graph that are usable to configure the logical topology, wherein a logical connection between the determined components is based on the ordering in the dependency graph, the IOS CLI configuration model displaying the logical topology in the IOS user-defined configuration based on the mapping rules.

6. The method in accordance with claim 1 , wherein a word in the represented structure is marked with a caret to indicate a correspondence to a subgraph root in the ICDT.

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10. The computer-readable storage medium of claim 9 , wherein the phonetic scheme is stored in a user-readable file format.

11. The computer-readable storage medium of claim 10 , wherein the user-readable file format is an XML file format.

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14. The system of claim 12 , wherein the results module is to group the result data according to a context data identifier.

12. A system, comprising: a processor; a memory coupled to the processor for storing context data; a context module to receive from a client, context data associated with a context and a user, the context data including information indicative of a category of offerings in a network-based marketplace, the context data identifying at least one category of products or services offered to purchasers in the network-based marketplace, the context module further to automatically discover context attributes associated with the context, the context attributes being automatically discovered by processing attribute data received from a plurality of other users, the attribute data being related to the at least one category of products or services, and the context module to associate the context data and the context attributes with a user identifier corresponding to the user; and a results module to create result data relevant to the user identified by the user identifier and to communicate the result data to the client, the context module and the results module being executable by the processor.

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1. A method, comprising: performing, by a computer system, semantico-syntactic analysis of a natural language text to produce a semantic structure representing a set of semantic classes; associating a first semantic class of the set of semantic classes with a first value reflecting a specified semantic class attribute; identifying a second semantic class associated with the first semantic class by a pre-defined semantic relationship, wherein an instance of the second semantic class is an ancestor of the first semantic class in a semantic hierarchy associated with the set of semantic classes; associating the second semantic class with a second value reflecting the specified semantic class attribute, wherein the second value is determined by applying a pre-defined transformation to the first value; evaluating a feature of the natural language text based on the first value and the second value; determining, by a classifier model using the evaluated feature of the natural language text, a degree of association of the natural language text with a particular category of a pre-defined set of categories; and performing, using the degree of association, a natural language processing operation.

3. The method of claim 1 , wherein the semantic structure is represented by a graph comprising a plurality of nodes corresponding to semantic classes and further comprising a plurality of edges corresponding to semantic relationships.

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16. The phoneme decoding system of claim 15 , wherein said symbol key comprises pictograms for the pronunciation of all the single-source and multi-source phonemes of the English language.

15. The phoneme decoding system of claim 1 , further comprising a symbol key comprising pictograms for the pronunciation of a plurality of single-source and multi-source phonemes of the English language and a pictogram for said silent phoneme.

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3. The invention of claim 1 , further comprising the step of selecting 25 elements that are respectively corresponding to the 25 stroke combination sets whose representative visual representations are: ; Assigning these 25 elements to keys of the keyboard.

1. A computer Chinese character input method comprising: Selecting 10 elements corresponding to the 10 simplified Chinese character simplified strokes which are and Selecting 46 elements corresponding to the 46 stroke combination sets whose representative visual representations are: ; Assigning said 46 elements, and 8 elements, excluding and from said 10 elements, to the keyboard in the following way: TABLE 4 in the table above, elements in the same line are assigned to the same keys, while those in different lines are assigned to different keys; and Determining desired input characters based on the keystrokes typed by a user on this keyboard.

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9. A method for triggering a sub-word unit recognition comprising: drawing one or more strokes of a desired sub-word unit using a stylus on a touch screen, wherein one of the drawn one or more strokes is a first head-line stroke and is a last drawn stroke in the drawn one or more strokes of the desired sub-word unit; inputting an associated data of the drawn one or more strokes via the touch screen into a handwriting recognition engine; computing stroke recognition characteristics of each of the drawn one or more strokes with reference to a horizontal reference line, wherein the stroke recognition characteristics are selected from the group comprising aspect ratio and slope; determining a first trigger stroke in the drawn one or more strokes of the desired sub-word unit that can be used to trigger the sub-word unit recognition based as a function of the computed stroke recognition characteristics of each of the multiple drawn strokes, wherein the first trigger stroke is the first head-line stroke which is drawn substantially parallel to the horizontal reference line; and triggering sub-word unit recognition for the drawn one or more strokes by the handwriting recognition engine upon determining the first trigger stroke.

17. The method of claim 9 , wherein the sub-word unit is selected from the group comprising vowels, consonants, consonant modifiers, vowel modifiers, numerals, and special characters.

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7. The computer program product of claim 6 , wherein the at least one host memory location includes a location of a transport indirect data address (TIDA) specifying a plurality of locations of the output data, and the at least one generated data check word is an intermediate data check word generated using length information of the output data from one of the plurality of locations.

6. The computer program product of claim 4 , wherein the data transfer request is a request for the output data from the host memory location, the at least one generated, data check word is generated using the initial value from the Data Check Word Seed field of a Transport Write Data ACW and a length of the output data that is received at the channel, and the at least one generated data check word is appended at an end of the output data that is routed to the network interface.

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11. The developer's toolkit of claim 9 , wherein the ontology comprises a hierarchy of domain ontologies, and each of the domain ontologies defines a structure for representing knowledge relating to a specific domain.

12. The developer's toolkit of claim 11 , wherein each of the domain ontologies comprises a hierarchical structure of ontological concepts each representing a portion of the knowledge relating to the specific domain.

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6. A computer-implemented method of determining whether a business listing is legitimate, the method comprising: accessing, by a processor, a list of legitimate business titles, each legitimate business title including one or more words; generating a matrix of surprisingness values, where each surprisingness value indicates a likelihood of a word appearing in a legitimate business title, by: examining each legitimate business title to identify pairs of words occurring in that title, adding a count value to the matrix for each pair of words identified, such that the matrix includes a plurality of count values for different pairs of words occurring in the legitimate business titles, and normalizing the plurality of count values for the matrix to generate the matrix of surprisingness values, where each surprisingness value of the matrix of surprisingness values indicates how likely a pair of words are to appear in a legitimate business title; storing the matrix of surprisingness values in memory; accessing a first plurality of business listings each associated with title data including two or more words; identifying, from the first plurality of business listings, a second plurality of business listings all corresponding to one particular business; for each business listing of the identified second plurality of business listings, determining a surprisingness value indicative of the surprisingness of the title included in the particular business listing based on the stored matrix of surprisingness values; determining an average surprisingness value for the identified second plurality of business listings based on the stored matrix of surprisingness values; selecting a particular business listing of the identified second plurality of business listings; and determining whether the particular business listing is legitimate based on whether the surprisingness value for the particular business listing is greater than the average surprisingness value plus a threshold value.

7. The method of claim 6 , further comprising normalizing the matrix of surprisingness values before determining the surprisingness value for each business listing of the identified second plurality of business listings.

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13. A non-transitory, computer-readable medium having computer-executable instructions stored thereon that, when executed by one or more processors, cause the one or more processors to perform a method, the method comprising: selecting a first article of interest from a plurality of articles, the article being associated with a first plurality of comments previously provided from one or more entities having accessed the article; extracting the first plurality of comments; in response to a determination that the first plurality of comments exceed a comment threshold that defines a minimum number of comments to use in determining a native context, determining the native context for the selected first article by: determining a term-comment matrix for the first plurality of comments, the term-comment matrix identifying a plurality of terms found in the first plurality of comments; applying a matrix factorization to the term-comment matrix to obtain the context-aware topic distribution of topics associated with the first plurality of comments; determining a context-aware feature for a comment selected from the extracted first plurality of comments based on the context-aware topic distribution and a similarity between the selected comment and the first article; applying a text classifier to the extracted one or more comments using the determined context-aware feature, the text classifier providing an indication of whether a given comment from the extracted plurality of comments is irrelevant; and in response to the applied text classifier, taking an action on the given comment based on the provided indication; and in response to a determination that the extracted one or more comments do not exceed the comment threshold, determining a transferred context for the selected first article by: extracting a second plurality of comments from a subset of articles selected from the plurality of articles, the extracted second plurality of comments being topically similar to the extracted first plurality of comments, wherein the subset of articles includes articles other than the first article; combining the extracted first plurality of comments and the extracted second plurality of comments to obtain a third plurality of comments; applying a matrix factorization to the third plurality of comments to determine context-dependent semantics of the first plurality of comments; determining a transferred context-aware feature for the first plurality of comments using the determined context-dependent semantics and the third plurality of comments; applying the text classifier to the extracted first plurality of comments using the determined transferred context-aware feature, the text classifier providing an indication whether a given comment from the extracted first plurality of comments is irrelevant; and in response to the applied text classifier, taking an action on the given comment based on the provided indication.

14. The non-transitory, computer-readable medium of claim 13 , wherein the method further comprises: determining a transferred context-aware topic distribution of topics from the third plurality of comments, wherein the determining of the transferred context-aware feature is based on the determined transferred context-aware topic distribution.

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

1. A method of processing a federated query, comprising: receiving an indication that a first set of source queries embedded in a first federated query failed to execute successfully, each source query specifying a set of source tables stored in a target autonomous data source of a plurality of target autonomous data sources belonging to a federation, at least two source queries of the first set of source queries being specific to different data sources, and the first federated query being sent from a client; storing the first set of source queries and metadata associated with the first set of source queries into a data structure, the first set of source queries including a first source query; for each source query of the first set of source queries that is determined to be stored in the data structure, updating metadata of each entry corresponding to the respective source query stored in the data structure, the metadata including a number of times the respective source query has failed and further including a timestamp of the respective failure; selecting a second set of source queries from the data structure, the second set of source queries including the first source query and having a higher probability of failure than a third set of source queries stored in the data structure; submitting the second set of source queries to one or more target data sources; for each result of a source query of the second set of source queries, storing the result in a cache external to the federation; receiving an indication that the first source query embedded in a second federated query failed to execute successfully, the second federated query including a second source query, and the first source query specifying a first set of source tables stored in a first target autonomous data source; generating a third source query by replacing a first set of source table names included in the first source query with a second set of source table names that identifies a second set of source tables, the second set of source tables being stored in the cache and storing data cached from the first set of source tables, and the first set of source table names being different from the second set of source table names; generating a third federated query including the second and third source queries; submitting each source query embedded in the third federated query to one or more data sources, the third source query specifying the second set of source tables and being submitted to the cache; and sending a combined result set responsive to the third federated query to the client, the combined result set including a first result set responsive to the second source query and further including a cached result set responsive to the third source query, and the cached result set being stored in the cache.

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7. The method of claim 1 , further comprising: training a classifier to identify documents having one or more questions and corresponding answers, wherein identifying the plurality of documents having one or more questions and, for each question, a corresponding answer comprises using the trained classifier to identify documents having one or more questions and corresponding answers.

1. A computer-implemented method comprising: identifying a plurality of documents having one or more questions and, for each question, a corresponding answer; generating a plurality of question-answer pairs from the questions and respective corresponding answers occurring in the plurality of documents; training a statistical machine translation model using the plurality of question-answer pairs, including using each question of each question-answer pair as a source language input and a corresponding answer of the question-answer pair as a target language input, wherein each question and each corresponding answer are in the same natural language; translating, using the statistical machine translation model trained on the plurality of question-answer pairs, a phrase into one or more corresponding translated phrases; and determining one or more synonym pairs by comparing the phrase with the one or more corresponding translated phrases.

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12. The steps of claim 7 consisting of submitting one or more documents nested in their prescribed formats adding subject identifiers as well as entry of dictated subject captions wherein one reminder list eliminates duplication of dictated subject captions for one dictation session.

7. The voice recording recited in claim 1 is transcribed/proofread by any source or system so a subsequent transcript can be submitted by the means recited computer processing for use by the dictationbase system comprising steps of: Including: a) creating the dictationbase dictation application for one or more documents wherein the tagged subject identifiers are stored in the dictationbase by the means computer processing from preformatted word processing templates or list to be used to assemble documents including matching dictated subject captions together with prose, type once data subject identifiers for subsequent transcript processing, including a plurality of tagged subject identifiers comprised of each subject, document element, database field and prescribed connections to resource systems, type once data fields, industry standard identification or staff assigned document data subject/elements; b) the dictation user designated for each application assigns and enters familiar (one or more words) comprising subject identification for dictated subject captions in their dictation routine or uses the subject identifiers absent any assigned entry as the dictated subject captions; c) the dictationbase application produces the reminder list of dictation subject captions to guide the user for the voice recording technique recited in claim 1 comprised of uttering the dictation subject captions with or without modifiers followed by free expression information relevant to said subjects and at least one term indicating an end of said information “end-it”; d) the voice recording dictated in claim 1 is transcribed so the subsequent transcript text file with dictated subject captions including prose can be processed by submitting for computer processing including dictated subject captions review for user dictated modifiers and assembly functions for preformatted documents as well as storing each captured dictated subject caption with dictated prose in the dictationbase by all identifications. e) the computer processing recited in claim 1 comprised of assembling one or more documents while capturing and storing a plurality of dictated subject captions with prose as computable data in the dictationbase including executing prescribed connection updates for resource database fields.

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14. The method of claim 13 , wherein selectively sampling performs direct sampling from the user through one or more prompts requesting the user to classify the user's state of interruptability.

13. A computer-implemented method of performing selective sampling of data to enhance model performance, comprising: with at least one processor, creating a model of a state of a user based on a set of data; testing the model against the set of data to determine performance results; and initiating selective sampling to obtain additional data by probing the user when the performance results generated from the set of data are below a criterion, the selective sampling occurring at a time determined based on a state of the user.

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7. A method for predicting sand-grain composition and sand texture comprising: establishing one or more root nodes in a Bayesian network; establishing one or more leaf nodes in the Bayesian network; coupling the root nodes to the leaf nodes to enable the Bayesian network to predict sand-grain composition and texture.

15. The method of claim 7 further comprising: establishing one or more intermediate nodes; and where coupling the root nodes to the leaf nodes to enable the Bayesian network to predict sand-grain composition and texture comprises: coupling at least some of the one or more root nodes to at least some of the one or more leaf nodes through the one or more intermediate nodes.

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5. A method for training acoustic models in speech recognition systems, wherein the speech recognition system comprises a neural network, the method comprising the steps of: a. extracting acoustic features from a speech signal using the neural network; and b. processing the acoustic features into an acoustic model by the speech recognition system, wherein the extracting of step (a) further comprises the step of optimizing a cost function, wherein the cost function is capable of transforming general non-linear functions generated by the neural network.

8. The method of claim 5 , wherein the neural network carries activation functions with variable parameters.

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1. A method for generating an image with a realistic personalized text insert via software loaded on a computer, the method comprising the steps of: a. defining a number of curves, where the curves define alphabetic or numeric characters, and storing the curves in a storage area of the computer; b. providing a base image with at least one position frame defined within the base image, the position frame defining an area where the text insert is insertable; c. choosing at least one curve by indicating a letter of the text insert; d. assembling letters of the text insert comprising the steps of: i. selecting several individual images, the individual images consisting of at least two pixel elements; ii. sizing automatically the individual images as a function of the at least one curve; and iii assembling automatically the several individual images along the at least one curve; where the steps c and d are performed separately for each letter of the text insert by the software, and the sizing and assembling is done according to a random function for providing different arrangement of the individual images for indentical letters of the text insert; e. assembling the letters within the position frame to compose the text insert thereby creating an output image where the individual images appear to be a part of the base image; and f. outputting the output image to an output unit.

10. The method of claim 1 further comprising the step of contrasting edge areas of the elements to form a three dimensional embossed or indented effect.

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17. The method of claim 11 , further including a step: forming a concatenation of words and/or phrases derived from said speech query and using said concatenation as a search query for a database.

18. The method of claim 17 , wherein a plurality of potential responses from said database are further evaluated by a natural language engine to determine a single best answer.

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19. A data gathering system in accordance with claim 18 wherein the second program entity is a synchronizing program which controls the periodic execution of a variety of computer system executable jobs.

18. A data gathering system in accordance with claim 17 wherein the first program entity is a computer system job scheduler which includes means for accepting job identifiers corresponding to executable job definitions and means for executing the corresponding jobs on a priority basis in response to receipt of the identifiers from other computer system entities.

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6. The method as defined in claim 5 , wherein applying a first first-level transform of the document stored in raw XML form comprises applying a subscription-level transform to the internal representation of the document to create a subscription-level document.

7. The method as defined in claim 6 , wherein the subscription-level transform enables content filtering of the internal representation in accordance with a user's request.

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6. The method of claim 1 , wherein at least a subset of the multiple media items is stored remotely from the electronic device.

1. A method for searching for media items using a voice-based digital assistant, comprising: at an electronic device with a processor and memory storing instructions for execution by the processor: providing multiple media items wherein at least some of the media items are each associated with a respective tag comprising at least one of a time tag, a date tag, or a geo-code tag; providing a natural language text string corresponding to a search query for one or more media items, wherein the search query includes one or more query terms; searching at least one information source to identify at least one parameter associated with at least one of the one or more query terms, wherein the at least one parameter comprises at least one of a time parameter, a date parameter, or a geo-code parameter, wherein the at least one information source comprises user-specific descriptive information, and wherein the at least one parameter is not provided in the search query; comparing the respective tags to the at least one parameter to identify at least one media item whose tag matches the identified parameter; and facilitating the presentation of the at least one media item to a user.

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21. A computer readable medium for storing a program that causes a computer to: execute a pre-scan of a document, on which document the user has marked a plurality of document blocks, and creating pre-scan image data from which pre-scan image data the plurality of document blocks that is marked by the user are detected; extract the document block that is digital image data representing a portion of the scanned document, the scanned document having document images and a background, the document block includes document image data and background image data, the document image data represents some of the document images on the scanned document, wherein all the document image data in the extracted document block represents fewer document images than are present in the scanned document; generate character code data for character image data within the document block; reconstruct the document block into a single document block in a specific shape based on the extracted document block; and laying out the generated character code data within the reconstructed document block to create a layout image; wherein the layout image includes a character image of a headline and a character image of body text corresponding to the headline.

22. The computer readable medium as claimed in claim 21 , wherein the program further comprises a step of arranging the character code data corresponding to the character image of the headline at a specific position within the reconstructed document block.

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10. A method being performed by one or more computing devices including at least one processor, the method for utilizing keystroke logging to determine items for presentation, the method comprising: receiving a search query including submitted content and keystroke logging information, the keystroke logging information being captured between engagement with a search query input region and execution of a search query; receiving keystroke logging information associated with the search query, the keystroke logging information being captured between engagement with a search query input region and execution of a search query; and determining at least one of a search result and an advertisement for presentation in response to the received search query, the at least one of the search result and the advertisement being determined, at least in part, based upon the keystroke logging information.

16. The method of claim 10 , wherein determining at least one of a search result and an advertisement for presentation in response to the received search query comprises determining at least one search result, and wherein determining the at least one search result comprises ranking the at least one search result among a plurality of other search results based, at least in part, on the keystroke logging information.

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3. The non-transitory storage medium of claim 1 , wherein said at least one keyword is selected by highlighting a portion of text contained in said body of said message's text.

1. A non-transitory storage medium encoded with machine-readable computer program code for providing search and reference functions for a messaging system, the non-transitory storage medium including instructions for causing a computer to implement a method, comprising: receiving a request to search a data archive for reference information relating to at least one keyword selected by a messaging system user, said messaging system user actively engaged in composing a message or a response to a message, and wherein further, said at least one keyword is selected from a body of said message's text; searching said data archive; if a reference is found, presenting said reference to said messaging system user within said message; wherein said data archive includes information gathered from said messaging system user's message folder and at least one of: a local data storage system; and a shared online repository; and further comprising instructions for causing said computer to implement: integrating process software for providing said search and reference functions for a messaging system, said integrating process software further comprising: determining if said process software will execute on at least one server; identifying an address of said at least one server; checking said at least one server for operating systems, applications, and version numbers for validation with said process software, and identifying any missing software applications for said at least one server that are required for integration; updating said at least one server with respect to any operating system and application that is not validated for said process software, and providing any of said missing software applications for said at least one server required for said integration; identifying client addresses and checking client computers for operating systems, applications, and version numbers for validation with said process software, and identifying any software applications missing from said client computers that are required for integration; updating said client computers with respect to any operating system and application that is not validated for said process software, and providing any missing software application for said client computers required for said integration; and installing said process software on said client computers and said at least one server.

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17. The system of claim 13 , wherein said one or more documents comprise documents being processed by at least one user, and wherein said processor is further configured to maintain a word count for each word in said one or more documents and wherein said word frequencies in said predefined misspelling criteria are based on said word counts.

18. The system of claim 17 , wherein said processor is further configured to suggest a correction of said at least one given word using said word within said predefined edit distance and having a frequency above said predefined high threshold.

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31. A method in accordance with claim 29 , wherein: the at least one psychological profiling algorithm provides an interpretation of at least one of the psychological state and the risk of at least one combination of at least one of the words, phrases or subjects represented by the at least one computer generated communication.

29. A method in accordance with claim 1 , wherein the at least one psychological profiling algorithm quantifies words, phrases, or subjects chosen from: I, we, me negatives, quantifiers, retractors, direct references, explainers, expression of feeling, evaluators, adverbial intensifiers, rhetorical questions, interruptions, interrogatives and imperatives.

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19. The computer program product of claim 15 wherein the actions further comprise: identifying one or more naming attributes included in the identified expansion clause; comparing the identified naming attributes to one or more column names corresponding to the selected columns, the comparing resulting in one or more renamed columns; generating an SQL AS clause corresponding to each of the renamed columns wherein each of the SQL AS clauses includes a new name derived from one of the identified naming attributes; and modifying each of the generated plurality of SQL column selection statements corresponding to each of the renamed columns by adding the respective SQL AS clause.

15. A computer program product stored in a non-transitory computer readable medium, comprising computer instructions that, when executed by an information handling system, causes the information handling system to perform actions comprising: identifying an expansion clause within a Structured Query Language (SQL) statement, wherein the SQL statement identifies a relational database table; comparing one or more column attributes associated with the identified relational database table to one or more attributes included in the identified expansion clause; selecting one or more columns included in the relational database table based on the comparison; generating a plurality of SQL column selection statements, with each of the generated SQL column selection statements corresponding to a different one of the selected one or more columns; and including the generated SQL column selection statements in the SQL statement.

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18. The method of claim 13 , wherein the electronic document is a search result, and wherein the content source is a website that is linked in the search result.

13. A computer-implemented method for providing a legitimacy rating of a content source, comprising: receiving at a computer system a request for a document; providing, by the computer system, an electronic document by a document provider based on the request, wherein the electronic document is associated with a content source; and providing, by the computer system, a legitimacy rating of the content source, wherein the legitimacy rating comprises a history rating and a transaction volume rating wherein the transaction volume rating is based on the number of documents associated with the content source that have been provided by the document provider and wherein at least one of the history rating and the transaction volume rating comprises a metric that is normalized based on at least one of history ratings and transaction volume ratings of a subset of content sources who provide documents to the document provider, wherein the subset of content sources are selected based on a similarity to the content source.

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9. A system comprising: a platform configured to: provide an input panel having user-selectable modes comprising at least a text input mode and a shape input mode, wherein a user selects from the user-selectable modes before entering digital ink to the input panel; receive the digital ink as input to the input panel after the user selects from the user-selectable modes; provide the digital ink to a recognition service that recognizes the digital ink; receive a recognition result from the recognition service, the recognition result comprising recognized text or a recognized non-textual shape; in a first instance when the user has placed the input panel into the text input mode before entering the digital ink and the recognition result comprises the recognized text, provide the recognized text to a text processing application; in a second instance when the user has placed the input panel into the shape input mode before entering the digital ink and the recognition result comprises the recognized non-textual shape, provide the recognized non-textual shape to a non-textual shape processing application that is different from the text processing application; and in a third instance when the user has placed the input panel into the shape input mode before the digital ink is received and the recognition result comprises the recognized text, provide the recognized text as a keyword to the non-textual shape processing application to use in a keyword search to locate another non-textual shape that is related to the keyword; and at least one processing device configured to execute the platform.

18. The system according to claim 9 , wherein the platform is further configured to, in a fourth instance when the user has placed the input panel into an equation mode, provide the recognized non-textual shape to an equation recognizer that recognizes at least the following symbols from the digital ink entered by the user: “a 2 ”; “b 2 ”; “+”; and “{square root over ( )}.”

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12. The computerized device according to claim 10 , said passages comprising text passages.

10. A computerized device comprising: a processor; and a user interface operatively connected to said processor, said user interface receiving a question comprising question terms, said processor automatically searching sources of data containing passages using a processor of said computerized device to produce candidate answers to said question, said searching being based on said question terms, and said searching identifying sources of evidence that support each of said candidate answers based on scoring features that indicate whether said candidate answers are correct answers to said question, said sources of evidence comprising passages, said processor automatically creating a scoring feature-specific matrix for each scoring feature of said scoring features, each said scoring feature-specific matrix specifying all different combinations of said passages, said candidate answers, and said question terms and comprising score fields for score values for each specific question term with respect to a specific passage and a specific candidate answer, each score field containing a score value indicating how a passage term of said specific passage aligns with said specific question term to support said specific candidate answer as being a correct answer to said question with respect to said scoring feature, and multiple ones of said different combinations of said passages, said candidate answers, and said question terms forming vectors, said processor automatically combining said vectors by calculating a statistical measure of said vectors to produce a collapsed score for each of said question terms, said processor automatically combining collapsed scores for each of said question terms to produce a combined score for each of said candidate answers, and said processor automatically ranking said candidate answers based on said combined score for each of said candidate answers.

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7. A computer-readable medium having computer executable instructions stored therein, said instructions being executed by a computer, for performing the steps of claim 6.

6. The method of claim 5 wherein the receiving step includes receiving a characteristic of a user and wherein the accessing step accesses the decision graph of the first node to determine whether the user would likely like to visit a category of web sites based on the characteristic.

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2. The method of claim 1 , further comprising: prior to step (a), receiving from a host device, a first font ID identifying said first font and receiving a second font ID identifying said second font; and determining said location of said first font using said first font ID; and determining said location of said second font using said second font ID.

1. A method of managing multiple fonts in a printing device, wherein said multiple fonts are stored within a font-storage memory of said printing device, the method comprising the following steps: (a) activating a first font from said font-storage memory by storing a first font location of said first font in a first font-access memory, said first font-access-memory being first in a font access order, said first font including a first plurality of characters each identified by a corresponding first character index; (b) activating a second font from said font-storage memory by storing a second font location of said second font in a second font-access memory, said second font-access memory being second in said font access order, said second font including a second plurality of characters each identified by a corresponding second character index; and (c) requesting and receiving a character index; wherein upon receiving said requested character index: determining if the requested character index matches any of said first character indices, if a match is found, then outputting the character within said first font that corresponds to the matched first character index; else determining if the requested character index matches any of said second character indices, if a match is found, then outputting the character within said second font that corresponding to the matched second character index; wherein said second font is a complete character set, and said first font is a partial character set, said partial character set being a subset of said complete character set, and each character within said partial character set having a corresponding character within said complete character set.

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3. The affective model device of claim 1 , further comprising: an affective information manager configured to update the fourth affective component using at least one of: the first affective component, the second affective component, and the third affective component; and an affective information communication unit configured to provide the updated fourth affective component to the behavior deciding unit.

1. An affective model device, comprising: an emotion information storage configured to store at least two of: a first affective component that is based on input specificity and a variation interval; a second affective component comprising a relatively higher input specificity than the first affective component; a third affective component comprising a relatively smaller variation interval than the first affective component; and a fourth affective component comprising a relatively smaller variation interval than the second affective component and a relatively higher input specificity than the third affective component; and a behavior deciding unit configured to decide a behavior of the affective model device based on at least one of: the first affective component, the second affective component, the third affective component, and the fourth affective component.

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3. The method according to claim 2 , wherein setting up a context in a debugger comprises: displaying a waveform of misbehaved signals associated with the anomaly in a waveform window; and centering the waveform window around a simulation time at which the anomaly is detected.

2. The method according to claim 1 , further comprising: setting up a context in a debugger for debugging the anomaly.

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1. An e-mail sorting and routing system, the system comprising: a web server for providing a web-site at which clients generate e-mail messages to a host organization, the web server being configured to determine the language in which the web-site is written and to append a meta-tag to each e-mail message that identifies that web-site language; and a response server configured to sort the e-mail messages by language through reference to the appended meta-tags.

2. The system of claim 1 , wherein the web server is further configured to determine a topic to which each e-mail message applies and to append a meta-tag to each e-mail message that identifies that topic, and wherein the response server is further configured to sort the e-mail messages through reference to the topic meta-tags, such that each email message is sorted first by language and then by topic.

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15. The voice-enabled spoken dialog service of claim 1 , wherein the frequently asked questions module further performs: if the utterance is a frequently asked question, determining whether the frequently asked question is a first frequently asked question by the user; if the utterance is the first frequently asked question asked by the user: providing a first frequently asked question introduction prompt using the second voice; and answering the frequently asked question in the second voice; and if the utterance is not the first frequently asked question asked by the user: providing an introductory prompt different from the first frequently asked question introductory prompt in the second voice; and answering the frequently asked question in the second voice.

1. A voice-enabled spoken dialog service comprising: an automatic speech recognition module comprising a general-purpose acoustic model and a domain-specific model, wherein the general-purpose model is used for bootstrapping at initial deployment of the spoken dialog service and the domain-specific model is used to adapt the automatic speech recognition module after deployment, wherein a language corpus for the domain-specific acoustic model is drawn from at least one domain-specific website; a spoken language understanding module that performs text normalization, entity extraction and semantic classification using a boosting approach that balances human-crafted rules with available data; a dialog management module that comprises an interpreter, finite state machine engine and an action template; a text-to-speech synthesis module for synthesizing speech; and a frequently asked questions module, wherein the spoken dialog service communicates with the user in a first voice, and wherein the frequently asked questions module performs: receiving an utterance from the user; determining whether the utterance is a frequently asked question; if the utterance is a frequently asked question, answering the frequently asked question in a second voice; and after answering the frequently asked question in the second voice, providing a prompt in the second voice to return the user to the first voice.

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8. The method of claim 1 , in which the two-way communication channel notifies the user device of a coupon based at least in part on the data extracted from the at least one monitored data source.

1. A method of analyzing data, comprising: generating, by an entity, a query based at least in part on a topic of interest; executing the query on a plurality of data sources, at least one of the plurality of data sources comprising information related to at least one of financial transactions, rewards, deals, coupons, customer incentives, or a combination thereof; selecting, by the entity, a data source from the plurality of data sources for monitoring based on a correlation between the data source and the topic of interest, the correlation determined based on results of the executed query; monitoring, based on a set schedule, the data source for matches to the query to extract data from the data source when at least an update to stored data matches the query, newly added data matches the query, or a combination thereof; extracting data from the data source when at least an update to stored data matches the query, newly added data matches the query, or a combination thereof; determining an extraction rate for extracting the data, the extraction rate indicating an amount of the data that is extracted over a first time period; determining a first processing rate for processing the extracted data with a number of parallel processors, the first processing rate indicating an amount of extracted data that is processed over a second time period; dynamically adjusting the number of parallel processors for analyzing the extracted data based on the extraction rate to obtain a second processing rate that is greater than the first processing rate; analyzing, with the parallel processors, the extracted data to determine at least one of a sentiment, an index, a pattern, or a combination thereof; establishing a two-way communication channel, between at least the entity that selected the data source for monitoring and a user device of a user that provided data to the data source, based on the analysis of the extracted data; transmitting, from the entity via the two-way communication channel, a first message directed to the user device based on the analysis of the extracted data; and receiving, from the user device via the two-way communication channel, a second message in response to the first message directed to the user device.

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1. A method, comprising: receiving a data source selection from a user or software application, the data source including medical information of a plurality of patients; receiving, from the user or software application, a data pattern that is related to a concept to be explored in the data source; querying the data source to find information that approximately matches the data pattern; and receiving the information from the data source, wherein the information includes unstructured data, assigning a classification to individual parts of the information based on the part's relationship to the data pattern; and outputting the classified information, including the unstructured data as a table including a classification label and a passage header columns, to the user or software application, the passage column being of the unstructured data separated into the individual parts, the unstructured data of the passage column comprising fragment passages that are one or more sentences, one or more clauses, or one or more sentences and clauses, which include a query term and surrounding text, and the classification label indicating positive or negative or not applicable to support of corresponding passages of the passage column and wherein the method is performed using a processor.

9. The method of claim 1 , wherein the concept is a medical question.

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14. The non-transitory computer-readable storage medium of claim 11 , wherein said integrated model is configured to: identify a first category on a first level of said plurality of decision levels based on data of a set of features corresponding to said first level; identify a second division that is nested with said first division on a second level of said plurality of decision levels based on data of a set of features corresponding to said second level; and selecting said one or more predictive models based on a set of features corresponding to an intermediate level that is immediately above said lowest level.

11. The non-transitory computer-readable storage medium of claim 10 , wherein said method further comprises: accessing a plurality of sets of training data; and generating said plurality of predictive models based on said plurality sets of training data.

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1. A method for determining a sentiment, comprising: receiving or accessing, using a microprocessor, a text; processing the received or accessed text and determining, from the text including formatting information related to parts of the text, a sentiment expressed by at least one of the parts, wherein the sentiment is determined automatically using the microprocessor and is determined based on formatting information related to the at least one of the parts, the determining of the sentiment is based on an analysis of an order of sentences in the text, the formatting information includes at least one of an underlining, an italic printing, a color, a font style, and/or a font size of characters, for each of a plurality of the parts, a respective sentiment and a respective level of importance are determined by performing analysis of the text using the microprocessor, a score is generated depending on values assigned to the respective sentiments of the plurality of the parts of the text, the score being generated by determining a weighted sum of the respective sentiments of the plurality of the parts, a weight of a respective sentiment being determined based on a respective level of importance and/or on a respective strength of the respective sentiment, and the sentiment is determined depending on a cultural back-ground of an author of the text, by performing an analysis of particular information associated with the text including choice of words; generating a visual indication associated with the score and the sentiment that is based on the formatting information, the order of sentences, and the cultural back-ground; outputting the generated visual indication to a display; and in response to receiving or accessing a plurality of texts, evaluating, for the plurality of texts, a respective sentiment with respect to a semantic content of at least one respective part of each of the texts by using statistical methods.

6. The method according to claim 1 , wherein the formatting information includes at least one of a font type, a bold type, a paragraph alignment, a paragraph side margin, an itemization character, a punctuation character, an abbreviation for sentiment expression, a numbering, and/or a sequence of paragraphs used in the text.

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15. The one or more non-transitory machine-readable storage media of claim 9 , wherein obtaining the geographic areas comprises calculating the geographic areas.

9. One or more non-transitory machine-readable storage media storing instructions that are executable by one or more processing devices to perform operations comprising: receiving an input query from a computing device; comparing words in the input query to keywords, the keywords being associated with content items that can be provided to computing devices; generating, based on the comparing, matching scores indicating how well the input query matches keywords for different content items; obtaining geographies associated with the computing device and associated with the content items that can be provided to computing devices; identifying geographic matches between the computing device and at least some of the content items; including, in an auction, content items having matching scores that exceed a threshold and that match a geography of the computing device, the auction for receiving bids from content providers to determine which of the content items in the auction to output in response to the input query; determining, based at least in part on bids provided in the auction, candidate content items for output in response to the input query; obtaining geographic areas associated with the candidate content items; selecting a candidate content item having a smallest geographic area; and outputting the selected candidate content item in response to the input query.

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2. The system of claim 1 , wherein the processing module is to identify the first product is associated with the first cluster of queries.

1. A system to generate a synonym dictionary, the system comprising: a computer processor; a memory coupled to the computer processor which, when executed by the computer processor, causes the computer processor to execute modules comprising: a receiving module to generate demand information based on a first plurality of queries, the first plurality of queries comprises a first query, the receiving module to identify search results based on the first query, the search results includes a first item, the receiving module to receive a selection over a network from a user that identifies the first item in the search results and responsive to the receipt of the selection: identify the first item as a first product, and store an association between the first query and the first product as an instance of demand information; and a processing module to identify a second plurality of queries as a first cluster of queries from the first plurality of queries based on the demand information, the demand information associated with the first cluster of queries is associated with a first plurality of search results that are associated with selections that identify items that are identified as the first product, the processing module to further identify a first synonym set based on an association map that comprises a first plurality of constraints identified from the second plurality of queries, the second plurality of queries includes a second query that includes at least two constraints and a third query that includes at least two constraints, the processing module utilizes the association map to identify the first synonym set including at least one constraint in the second query that matches at least one constraint in the third query, and the processing module to further store the first synonym set in a synonym dictionary.

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11. The memory medium of claim 6 wherein a specification of a value of “<” or “<-” for Directional-operator 2 parameter indicates that that the value indicated by the Entity 2 parameter is a subject of the value indicated by the Action parameter.

6. The memory medium of claim 1 wherein the base component specifies values for the desired relationship parameters in a general relationship form of: Entity 1 Directional-operator 1 Action Directional-operator 2 Entity 2 wherein at least one of Entity 1 , Entity 2 , and Action parameters contains a non null value that indicates a search term, the Directional-operator 1 parameter specifies the direction of the relationship between the Entity 1 and the Action parameters, and the Directional-operator 2 parameter specifies the direction of the relationship between the Entity 2 and the Action parameters.

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13. The method of claim 1 , further including: automatically determining at least one updated field extraction rule that extracts as one or more values of the one or more fields for both the first event and the second event; and causing display of a fourth user interface including an annotated version of the plurality of events, wherein the annotated version indicates the portions of text within the plurality of events extracted by the updated field extraction rule.

1. A computer-implemented method comprising: accessing in memory a set of events each event identified by an associated time stamp; wherein each event in the set of events includes a portion of raw data; causing display of a first user interface including a plurality of events; receiving data indicating selection of a first event from among the plurality of events; causing display of a second user interface presenting the first event to be used to define field extraction; receiving data indicating a selection of one or more portions of text within the first event to be extracted as one or more fields; automatically determining at least one field extraction rule that extracts one or more values for the one or more fields from the respective selections of the portions of text within the events when the extraction rule is applied to the events; causing display of a third user interface including an annotated version of the plurality of events, wherein the annotated version indicates the portions of text within the plurality of events extracted by the field extraction rule and presenting a second event to be used to refine field extraction; and receiving further data indicating a selection of at least one portion of text within the second event to be extracted as into at least one of the fields by at least one updated field extraction rule.

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6. The system of claim 1 , wherein the system further comprises a graphical user interface engine configured to generate graphical user interface screens based on the hierarchical model, the graphical user interface screens comprising: one or more data attributes from the hierarchical model; and operational data from the hierarchical model.

7. The system of claim 6 , wherein the graphical user interface engine is configured to generate the graphical user interface screens based on the information contained in at least one of a Display table, a Shape table, a Range table and a Query table, the hierarchical model further comprising: an Nclass_Group table configured to comprise information defining a group of node_classes within a Node_Class table that build a pool of nodes within a mapping; a Domain table configured to comprise information defining data element types that belong to a node, data element types that belong to a mapping, and data structures stored within the hierarchical model; an Attribute table configured to comprise operational data associated with a node and operational data associated with a mapping; the Display table configured to comprise information defining a graphical representation of a specific view of the hierarchical model; the Shape table configured to comprise one or more graphical parameters defining how node information is displayed, wherein the node information comprises the specification of a position, a size, a shape, and a background; the Range table configured to comprise color information corresponding to shapes for displaying a shape as part of a query result; and the Query table configured to comprise display information defining how a result of an SQL statement executed on the hierarchical model is displayed.

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38. A device for filtering material from a multimedia program, comprising: a learning module to generate filter criteria learned based on user instructions by examples of objectionable content; at least two of a visual analysis module to extract image features; an audio analysis module to extract second verbal audible features; a transcript analysis module to extract text features based on a transcript; and a filter to process the multimedia program, according to the extracted features and the titter criteria generated by the learning module, and generate a numeric ranking corresponding to the filter criteria learned based on the user instructions by examples of objectionable content and applied to a portion of the multimedia program, and eliminate, in accordance with the numeric ranking, objectionable material from the multimedia program.

47. The device of claim 38 , where the multimedia program has plural segments, and where: the at least two of a visual analysis module, an audio analysis module and a transcript analysis module are configured to extract respective features from the set of audio features, video features or text features, for each segment of the multimedia program; and the filter is to eliminate the objectionable material on a segment-by-segment basis.

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6. A simultaneous performance control method of a robot system, wherein the robot system comprises a primary robot and at least one secondary robot, characterized in that, the method comprises steps as follows: the primary robot and the secondary robot establishing a communication connection between a primary robot and the at least one secondary robot by wireless communication; the primary robot requesting the secondary robot to report an ordered list of the programs, then comparing the received list with local stored program number after the primary robot and the at least one secondary robot establish a communication connection, and performing transmission of unstored program to and from the secondary robot, the primary robot determining which simultaneous performance the robot system is going to give under a default strategy, and sending the number of the performance to be given to the secondary robot; the primary robot and the secondary robot respectively resetting clocks to zero and establishing a reference point of synchronous clock signal for simultaneous performance via wireless communication by the primary robot and the secondary robot; the primary robot and the secondary robot respectively reading and executing corresponding programs for the performance according to the reference point of synchronous clock signal by the primary robot and the secondary robot, and generating control signals of different time-points relative to the reference point of synchronous clock signal; and the primary robot and the secondary robot respectively executing different operations according to the control signal according to the control signals of different time-points by the operation execution modules of the primary robot and the secondary robot.

7. The method according to claim 6 , characterized in that: the step of respectively giving performance according to the control signal by means of the operation execution modules of the primary robot and the secondary robot comprises steps as follows: respectively giving audio performance according to the control signal by means of the operation execution modules of the primary robot and the secondary robot; and/or respectively giving motor performance according to the control signal by means of the operation execution modules of the primary robot and the secondary robot.

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11. The method of claim 10 , further comprising performing the sorting algorithm in one of: a corpus comprised of all records in a view of the free text field; and a corpus comprised of all records in a store of the free text field.

12. The method of claim 11 , further comprising: providing the new focus words to perform one or more of: removing noise words from a view's display of the free text field; removing only noise words prior to a first non-noise word from the view's display of the free text field; and reordering the words in the view's display of the free text field.

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23. The computer-readable medium of claim 22 , wherein the instructions for implementing the test node include instructions that causes execution of a proxy-server coupled with the computer system for communicating with the browser, wherein the proxy server performs portions of the test suite on each candidate creative before the creative is provided to the creative vector in the browser.

22. A non-transitory computer-readable medium having encoded thereon software for receiving creatives from advertisers and distributing creatives to publishers, the software including instructions for causing a computer system to: define a queue for holding creatives; implement a technical audit system for periodically inspecting technical attributes of the creatives as the creatives are rotated through the queue to find a hidden technical attribute detectable under unknown specified circumstances before delivery thereof to a publisher, wherein the instructions for implementing the technical audit system comprise instructions for: implementing a test node configured to cause execution of a browser for loading a creative vector containing a creative; generating the creative vector containing the candidate creative; launching the browser by a test daemon of the test node; loading the creative vector into the browser; and executing a test suite on the candidate creative by constituent elements of the test node; implement a content audit system for receiving fixed creatives from the technical audit system, the fixed creatives comprising constituent elements that do not change each time a creative vector thereof is loaded into the browser, wherein the content audit system classifies creatives according to content attributes thereof; delivering the fixed candidate creatives directly to the publisher from the server; and detecting a flag to not re-inspect the fixed candidate creatives based on the fixed candidate creatives remaining unchanged.

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10. A method of automatically generating lessons based on content of a digital programming file, the method comprising: by a content analysis engine processor, executing programming instructions that cause the content analysis engine processor to: analyze a set of text, corresponding to words spoken in a digital programming file, wherein the set of text comprises a transcript that includes a plurality of text segments and a plurality of timestamps each corresponding to one of the text segments, extract a sentence from the set of text by: extracting a sequential group of the text segments from the set of text, parsing the group of text segments to identify the sentence within the group, and using the timestamps of each text segment that is at least partially included within the sentence to determine a start time and a duration for the sentence by: (i) identifying a first text segment that is at least partially included in the sentence, (ii) determining a number of syllables of the first text segment that are in the sentence and a total number of syllables in the first text segment, (iii) determining a ratio of the number of syllables in the first text segment that are in the sentence and the total number of syllables in the first text segment, (iv) multiplying the ratio by a duration of the first text segment to yield a segment duration for the first text segment, (v) repeating steps (i)-(iv) for each text segment that is at least partially included in the sentence, and (vi) summing the segment durations for each text segment that is at least partially included in the sentence to yield the duration for the sentence, and generate a digital media clip that corresponds to the sentence, wherein the digital media clip has a start time in the digital programming file that corresponds to the start time of the sentence; and by a lesson generation engine processor, executing programming instructions that cause the lesson generation engine processor to generate a lesson comprising an exercise that includes: a prompt that uses one or more key words that are extracted from the sentence, and the digital media clip.

16. The method of claim 10 , wherein one or more of the key words comprise a named entity or event extracted from the sentence.

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4. The method of claim 1 , wherein: a. the host company further distributes print advertisements from the inventory listings generated.

1. A method for managing inventory sales advertisement information by a host company over a network, comprising the steps of: a. forming an inventory advertisement based information network having at least two tiers of access with at least one host server and at least one remote company user I/O device; b. configuring the host server with an interactive inventory listing builder for generating inventory listings; c. configuring the host server to manage remote company user access to the inventory listings; and d. enabling remote access to the at least one host server for the remote company user to create and manage inventory listings.

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14. The system of claim 13 wherein the web page analysis component groups the web pages into groups that are likely to be viewed as groups in each graph, given the random walk associated with each graph.

15. The system of claim 14 wherein the web page analysis component groups the web pages into groups that are likely to be viewed as a group given all graphs, and all random walks defined for the graphs.

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14. The system of claim 10 , wherein generating a second modified query comprises selecting the second limitation based on a property of the first limitation.

10. 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 an original query including a first limitation that constrains a search; modifying the original query to obtain a modified query in which the first limitation that constrains the search has been omitted; obtaining, from a search engine system, first search results responsive to the modified query, wherein the first search results have an associated ranking determined by the search engine system, and wherein each of the first search results refers to a respective resource; identifying one or more common characteristics shared by two or more resources, each of the two or more resources corresponding to a different highly-ranked result of the first search results; generating a second modified query comprising the original query and a second limitation representing the one or more common characteristics, the second limitation requiring results responsive to the second modified query to reference a resource having the one or more common characteristics; obtaining second search results responsive to the second modified query, wherein each of the second search results refers to a resource having the one or more common characteristics; and providing the second search results in a response to the original query.

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1. A computing system comprising: at least one processor; and at least one computer readable storage medium storing computer executable instructions which, when executed by the at least one processor, implement a method of providing entities the ability to create, manage, and/or store fine-grained metadata, artifacts, or other software related items of a domain by providing a relational model that stores these items in a way that allows rich querying using database routines and other tools, wherein the method includes: exposing, via an interface stored in memory of a computer system a set of schema guidelines that describe how software related items of a schematized model of a domain are to be categorized in query tables of a repository, the software related items including both executables and metadata that describes the executables, wherein the set of schema guidelines includes two or more of: naming guidelines; script file guidelines; table guidelines; indexing guidelines; viewing guidelines; procedure and function guidelines; foreign key guidelines; query guidelines; or cursor use guidelines; receiving from one or more entities software related items that are organized according to one or more schema guidelines in the set of schema guidelines, the software related items being received by the repository; and storing the organized software related items into a plurality of query tables of the repository, wherein the organized software related items are stored in rows of the query tables, wherein each row includes a container version ID field storing a container version ID that identifies a container to which the organized software related items stored in the row pertain, and wherein the containers are versioned based on when changes to the organized software related items the containers contain are made.

8. The computing system of claim 1 , wherein the procedure and function guidelines include one or more of: using updatable view instead of basic database functions; not using schema binding; avoiding cursors; not allowing for change of primary key columns; not altering identity or timestamp columns; or using column lists in insert statements.

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3. The method of claim 1 , wherein said expanding comprises expanding each of the tokens in the input linguistic item using plural reference sources.

1. A method, performed by computing functionality, for converting an input linguistic item into to a normalized linguistic item, the method executed by one or more processing devices of the computing functionality, the method comprising: receiving the input linguistic item; partitioning the input linguistic item into one or more tokens; expanding each of the tokens in the input linguistic item into a list of one or more candidate tokens, to provide an expanded linguistic item; creating a graph based on the expanded linguistic item; assigning weights to edges in the graph, using, at least in part, a statistical language model; and identifying a shortest path through the graph, to thereby identify a normalized linguistic item which represents a normalized counterpart of the input linguistic item.

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16. The apparatus as described in claim 15 , wherein the feature extractor is to identify a third value of the first feature of second media evoking the first emotion, and further including a mood model validator to validate the mood model by confirming that the mood model indicates that the first value of the first feature is within a threshold percentage of the third value of the first feature.

15. An apparatus to identify an emotion evoked by media, the apparatus comprising: a musical instrument digital interface notator to create a musical instrument digital interface representation of a pre-verbal utterance associated with a first emotion; a synthesizer to generate, using a digital musical instrument, a first synthesized sample based on the musical instrumented digital interface representation of the pre-verbal utterance; a feature extractor to identify a first value of a first feature of the first synthesized sample, the feature extractor to identify a second value of the first feature of first media evoking an unknown emotion; and a classification engine to create a mood model based on the first feature, the mood model to establish a relationship between the first value of the first feature and the first emotion, the classification engine to identify the first media as evoking the first emotion when the mood model indicates that the second value corresponds to the first value.

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1. A computer-implemented method for managing a conversational system on a server, the method comprising: presenting on a user system of a first party, each of at least one chatbot as part of at least one messaging window forming a chat window hosted by a third party, the chatbot adapted to act as a customer service agent for at least one of a good and a service; and a web page document hosted by a second party, the web page document including code to launch the chat window hosted by the third party, and the second party and the third party being independent business entities; displaying a message to the first party through the messaging window; and reviewing a response from the first party using a combination of keyword/response pair scripting and artificial intelligence; and wherein the keyword/response pair scripting, the messaging window and the artificial intelligence are all managed via a web-based management console hosted by the third party, the web-based management console includes components selectable by the second party for managing the messaging window of the chatbot, separate from a window of the web page document hosted by the second party for display on the user system of the first party, the components including at least one event to launch the messaging window of the chatbot in response to code scripting at the web page document hosted by the second party.

21. The method of claim 1 , wherein the event to launch the chatbot includes selecting specific links.

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9. The method of claim 1 , wherein the elements of textual data comprise user reviews of a product such that the first dimension includes concepts that represent attributes of the product and the second dimension includes concepts that represent possible classifications of users.

1. A method, implemented by one or more processors in a computing system, for generating a multidimensional synopsis of a stream of textual data, the method comprising: accessing, by the one or more processors, a stream of textual data that includes a number of elements of textual data, each element of textual data comprising plain text content that is associated with an author and is directed to a particular subject; identifying, by the one or more processors, a first dimension and a second dimension for the stream of textual data, the first dimension including a number of concepts that each represent a subject attribute of the particular subject, the second dimension including a number of concepts that each represent an author attribute; processing, by the one or more processors, each of the number of elements of textual data to identify which of the concepts of the first and second dimension appear in the plain text content included in the element, and for each concept within the first dimension that appears in the plain text content included in the element, generating a quantitative value; and generating, by the one or more processors, the multidimensional synopsis of the stream of textual data by generating a score for each intersecting set of concepts from the corresponding quantitative values, each score representing a prevalence of the intersecting set of concepts within the stream of textual data.

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8. The computer-implemented method of claim 7 , further comprising determining that the first signal comprises the first speech by: obtaining an endpointed signal, wherein the endpointed signal comprises one or more sounds and wherein the endpointed signal corresponds to a portion of the first signal; and using a classifier to determine that the one or more sounds comprise the speech.

7. A computer-implemented method comprising: under control of a computing device comprising one or more computer processors implemented in hardware and configured to execute specific computer-executable instructions, receiving a first signal; generating first speech recognition results from the first signal using and an acoustic model, wherein the first speech recognition results comprise a textual transcription of first speech; and subsequent to generating the first speech recognition results: determining a first confidence value associated with the first speech recognition results; generating a first updated acoustic model based on the first confidence value associated with the first speech recognition results; receiving a second signal; generating second speech recognition results from the second signal using and the first updated acoustic model, wherein the second speech recognition results comprise a textual transcription of second speech; determining a second confidence value associated with the second speech recognition results; determining that the first speech recognition results were generated outside a window; and generating a second updated acoustic model based at least in part on the second confidence value.

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20. The apparatus of claim 1 , wherein the sorting knowledge base is structured as a set of models, each model uniquely corresponding to one of a set of folders.

1. An electronic mail apparatus, comprising: a computing device capable of being connected to a network; and an email productivity module, executed by the computing device, and configured to interact with an existing email application executed by the computing device that sends and receives email messages over the network, wherein the email productivity module includes: a content analysis engine, executed by the computing device, configured to analyze a received email message to generate content information representative of a content of the received email message; a prioritization module, executed by the computing device, having at least one prioritization knowledge base implemented on the computing device, the prioritization module being configured to apply the content information to the at least one prioritization knowledge base to determine at least one priority score for the received email message that reflects a relative priority of the received email message as a legitimate email message and to assign at least one priority level to the received email message based on the priority score that reflects a range of priority scores; a message sorting module, executed by the computing device, having at least one sorting knowledge base implemented on the computing device, the message sorting module being configured to apply the content information to the at least one sorting knowledge base to determine a set of suggested folders for the received email message that represent one or more folders in which are stored other emails having similar content and in which a user would be most likely to store the received email message; and a junkmail module, executed by the computing device, having at least one junkmail knowledge base implemented on the computing device, the junkmail module being configured to apply the content information to the at least one junkmail knowledge base to determine a junkmail score for the received email message that represents a probability that the received email message is junkmail, and the junkmail module being configured to cause a user interface of the existing email application to modify a presentation of the received email message in accordance with the junkmail score; the email productivity module being configured to attach fields for the priority score, priority level, set of suggested folders, and junkmail score to the received email message for display by the existing email application; the email productivity module being configured to receive user feedback to the existing email application indicative of a user action taken with respect to the received email message, and to cause the computing device to adapt the at least one prioritization knowledge base, the at least one sorting database, or the at least one junkmail database, in accordance with the user feedback; wherein the at least one prioritization knowledge base is adapted by the computing device in accordance with explicit user feedback in an event that the user modifies the at least one priority level or the at least one priority score produced by the prioritization module and attached to the received email message for display; and wherein the at least one prioritization knowledge base is adapted by the computing device in accordance with implicit user feedback in an event that the user does not modify the at least one priority level or the at least one priority score produced by the prioritization module and attached to the received email message for display.

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10. The method of claim 8 , wherein the one or more range of invariants are defined in the defining of the query graph and comprises the range of invariants defining a particular search volume, and wherein at least some of the anticipated invariant queries include the range of invariants defining the particular search volume.

8. A method for analyzing ballistic trajectories comprising: determining, using a preprocessor, invariants for known ballistic objects; defining, using the preprocessor, a reference graph having nodes corresponding to the invariants for the known ballistic objects; defining, using the preprocessor, a query graph having nodes connected to nodes of the reference graph corresponding to anticipated invariant queries to be made using the query graph; inputting into the reference graph, using a runtime processor, one or more sets of invariants corresponding to trajectories of one or more observed objects, each of the one or more sets of invariants traversing through the nodes of the reference graph corresponding to the each of the one or more sets of invariants and leaving a record in the nodes traversed; selecting, using an interface, a query for the query graph corresponding to one or more range of invariants, said query generating a query result identifying the nodes of the reference graph that satisfy the query; and identifying, using the runtime processor, each of the one or more observed objects identified by a record in the identified nodes, thereby determining which of the one or more observed objects satisfy the query.

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16. A non-transitory computer-readable storage medium, storing program instructions computer-executable on a computer to: for each item in a subset of items from a larger group of items, evaluate item description information about that item to identify a respective set of candidate phrases to be evaluated for feature phrase identification, wherein the subset of items are related to one another and include a plurality of items that are less than all of the larger group of items; for each phrase in the sets of candidate phrases, generate multiple component scores comprising: a document frequency component score indicating the frequency with which that phrase occurs in the item description information for the subset of items, and an inverse document frequency component score indicating the frequency with which that phrase occurs in a corpus of item description information for the larger group of items; for each phrase in the sets of candidate phrases, combine the multiple component scores generated for that phrase to generate a respective phrase score for that phrase, wherein the program instructions are configured to generate the respective phrase scores for at least some of the candidate phrases based on a measure of brand entropy for a respective candidate phrase, wherein a given measure of brand entropy is penalized for candidate phrases that have a requisite term frequency—inverse document frequency (TFIDF) but only occur in item description information of a specified quantity of different item brands; and based on the phrase scores, select a subset of phrases from the sets of candidate phrases as being feature phrases for the subset of items.

17. The non-transitory medium of claim 16 , wherein to identify a subset of items to evaluate for feature phrase identification, the program instructions are configured to: receive information specifying a hierarchy of nodes, wherein each node corresponds to a respective class of items; and based on the hierarchy of nodes, select items of a particular node for feature phrase identification.

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15. The mobile device of claim 14 wherein the navigation processor is further coupled with a navigation database operative to relate one or more navigation oriented conversational elements to one or more navigation data elements.

14. A mobile device comprising: an input operative to receive at least a portion of a conversational narrative comprising at least one non-verbal physical movement of a portion of a human body of a provider and descriptive of a route to a destination expressed by the provider thereof to a receiver not connected to the mobile device, wherein the conversational narrative includes a plurality elements, the plurality of elements including a plurality of navigation oriented elements and at least one descriptive element characterizing another of the plurality of elements; a converter coupled with the input and operative to convert the received portion of the expressed conversational narrative to data representative thereof; a parser coupled with the converter and operative to identify the plurality of navigation oriented conversational elements represented within the data, as well as any descriptive elements associated therewith; a navigation processor coupled with the parser and operative to convert each of the plurality of navigation oriented conversational elements into an associated navigation data element representative thereof based on the identified descriptive elements associated therewith, if any; a route generator coupled with the navigation processor and operative to compile the navigation elements into a navigation route; and an output coupled with the route generator and operative to present the navigation route.

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11. The method of claim 9 wherein the histogram-mapping operation comprises: generating probability mass functions for confidence scores from the first and second confidence classifiers, generating a cumulative mass functions corresponding to the probability mass functions for confidence scores from the first and second confidence classifiers, respectively, and generating an acceptance criteria map in which the cumulative mass function for the second classifier for each confidence score in the acceptance criteria map equals the cumulative mass function for the first classifier for each confidence score within a preset resolution.

9. A method of transforming acoustic utterances into text in a speech recognition device, the method comprising: receiving one or more acoustic utterances via an acoustic sensor configured of the speech recognition device; storing a set of one or more acoustic models having trained one or more confidence classifiers and one or more acceptance metrics defining at least one recognition acceptance condition; generating a first speech recognition confidence classifier score corresponding to the one or more received acoustic utterances and recognized text based on a first confidence classifier; generating a second speech recognition confidence classifier score corresponding to the one or more received acoustic utterances and the recognized text based on a second confidence classifier; mapping a distribution within an output range of the second confidence classifier to a distribution within an output range of the first confidence classifier, the mapped distribution including a mapped speech recognition confidence classifier score for the second confidence classifier that more accurately satisfies the recognition acceptance condition than a corresponding score from the first confidence classifier; and outputting a signal representing new recognized text for a newly-received acoustic utterance as accepted text responsive to a determination that a mapped speech recognition confidence classifier score of the second confidence classifier for the newly-received acoustic utterance satisfies the recognition acceptance condition.

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8. A computer implemented document processing system for processing a document comprising: a user-input function for receiving a user input sample document and storing the sample document in a data storage wherein the sample document having sample document attributes including a computer file type or file format and sample structural characteristics wherein said document processing system processing said document to generate an output document with the same computer file type or file format as said user input sample document.

10. The document processing system of claim 8 wherein: said document processing system processing said document and generating an output document with a same computer file type or file format as the sample structural characteristics of the user input sample document.

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10. A non-transitory computer-readable medium having stored thereon instructions that, when executed by a computerized device, cause the computerized device to execute a computer-implemented method comprising: defining a tree pattern based on the text file, and defining a plurality of character string patterns to identify the desired data; loading the text file into an array, wherein each line of the text file is an element of the array; locating one or more text blocks of the text file to determine a tree structure corresponding to the text file according to the tree pattern, and retrieving the desired data from the text file according to the tree structure corresponding to the text file and the character string patterns; and outputting the retrieved desired data into a storage system.

11. The non-transitory computer-readable medium of claim 10 , wherein the tree pattern is defined by using extensible markup language (XML), and the character string patterns are defined by using regular expressions.

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1. A system comprising: a memory storing a plurality of instructions; and one or more processors configured to access the memory, wherein the one or more processors are further configured to execute the plurality of instructions to at least: receive, via a first user interface, a selection of a business logic template from a plurality of business logic templates from a user, the selected business logic template indicating at least a type of template for generating a corresponding type of continuous query for the user, the selected business logic template comprising a Key Performance Indicator (KPI) alert template; generate the selected business logic template by: identifying one or more query parameters related to the selected business logic template, the set of one or more query parameters comprising at least one KPI measure that identifies a critical metric of an organization, and the at least one KPI measure specifying an aggregation on a column of a data object in the KPI alert template; determining a set of one or more threshold parameters specifying one or more threshold conditions for the set of one or more query parameters; and defining an alert event to be transmitted to the user when a value associated with a query parameter exceeds a threshold condition specified by the set of one or more threshold parameters associated with the at least one query parameter; upon the generation of the selected business logic template, determine, from a set of input data source types, one or more compatible input data source types usable with the selected business logic template based at least in part on actions associated with the selected business logic template, the set of input data source types including a stream type data and a relation type data; provide, via a second user interface, the set of one or more query parameters associated with the selected business logic template; receive via the second user interface, a value associated with the at least one KPI measure from the set of one or more query parameters from the user; receive via the second user interface, a user-selected input data source type of the one or more compatible input data source types; provide, via a third user interface, the set of one or more threshold parameters for the at least one KPI measure, the set of one or more threshold parameters specifying the one or more threshold conditions for the at least one KPI measure; receive, via the third user interface, one or more values associated with the set of one or more threshold parameters for the at least one KPI measure from the user; generate a continuous query for retrieving business event data of the user based at least in part on the selected business logic template, the set of one or more query parameters associated with the selected business logic template, and the set of one or more threshold parameters, the continuous query configured to provide the alert event when the value associated with the at least one KPI measure exceeds the threshold condition specified by the set of one or more threshold parameters associated with the at least one KPI measure; receive a request to save the generated continuous query; generate an extensible markup language (XML) file configured to enable subsequent generation of the generated continuous query; store the extensible markup language file; and execute the continuous query on an input data source of the user-selected input data source type to retrieve the business event data associated with the user.

6. The system of claim 1 , wherein the input data source is an archived relation data object containing an unordered, time-varying set of tuples associated with the business event data of the user.

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12. The electronic dictionary of claim 10 wherein said first retrieval means retrieves said end of search direction signal as a first address number datum for one of said word data items, said first retrieval means then retrieves said second address number datum of that said word data item and searches in the opposite direction.

10. An electronic dictionary to retrieve and display word data related to a word input into the dictionary comprising an input means to insert the letters of a word into the electronic dictionary to retrieve related word data including a plurality of character keys to insert character data representing a word, and a call key means to signal proceeding to a next designated word; a vocabulary storage section having a plurality of word data items; related groups of word data items in said vocabulary storage section with each of said related groups of word data items forming a closed loop of related words including the character data; a word data memory location in said vocabulary storage section for storing each of said word data items; an identifying address appendixed to each of said word data items; first and second address number data also appendixed to each of said word data items; an address number memory location for storing each of said address number datam; a working memory means for storing character data entered through said character keys; search means for searching, upon activation of said call key means, said word data memory locations having said word data items represented by said character data entered by said character keys; first retrieval means for retrieving said first and second address number data stored in said address number memory location corresponding to said word data memory location of said word data item to which said address number data is appendixed and corresponding to said word data memory location searched by said search means; second retrieval means for retrieving said word data item stored in said word data memory location designated by said identifying address corresponding to said address number data retrieved by said first retrieval means; display means for indicating said word data item retrieved by said second retrieval means as related to said character data; said first retrieval means operating upon each signal from said call key means to retrieve said address number data stored in said address number memory location corresponding to said word data memory location of said word data item to which said address number data is appendixed and corresponding to said word data item last retrieved by said second retrieval means; said second retrieval means, upon each operation of said first retrieval means, retrieving said word data item stored in said word data memory location designated by said identifying address corresponding to said address number datum retrieved by said first retrieval means; said first and second retrieval means operating in a loop of one of each of said related groups of word data items determined by said character data entered by said character keys; each of said related groups of word data items having one word data item with an end of search direction signal as a first address number datum and another word data item with an end of search direction signal as a second address number datum with searching made in an opposite direction after reaching said end of search direction signal as a first address number datum; and means to exit from the loop of a related group with the two end of serach direction signals with clearing of the display means.

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18. The media of claim 17 , further comprising determining and separating the one or more event clusters by one or more values based on one or more of time, frequency or spatial coordinates.

17. A processor readable non-transitory storage media that includes instructions for managing operations for organizations over a network, wherein execution of the instructions by one or more processors performs actions, comprising: employing a plurality of provided Operations events to perform further actions, including: providing, by the one or more processors, one or more event clusters that are associated with one or more Operations events; associating, by the one or more processors, one or more resolution metrics with the one or more event clusters; employing, by the one or more processors, a modeling engine to train one or more models based on the one or more Operations events, the one or more event clusters, and the one or more resolution metrics, wherein the one or more trained models are stored in a datastore; and retrieving, by the one or more processors, the one or more trained models, from the datastore, that are used to identify the one or more resolution metrics that are associated with one or more real-time Operations events.

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8. The method of claim 7 , wherein the plurality of second labels are associated with the at least one task of determining the nature of the conversation.

7. The method of claim 6 , wherein the plurality of second labels comprises at least one of a ‘Complaint’, ‘Apology’, ‘Answer’, ‘Receipt’, ‘Compliment’, ‘Response to positivity’, ‘Request’, ‘Greeting’, ‘Thank’, ‘Announcement’, ‘Solved’, and ‘Other’.

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21. The system of claim 8 , further comprising, a setup component that configures and saves discovered RFID readers based at least upon RFID reader settings.

22. The system of claim 21 , wherein, the setup component creates a logical reader collection, adds a physical RFID reader to the logical reader collection and specifies properties associated thereto.

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3. The method of claim 1 , wherein the character-based n-grams are bigrams, trigrams, or four-grams.

1. A method of classifying a domain name, comprising: identifying a dictionary set of character-based n-grams, wherein each character-based n-gram in the dictionary set of character-based n-grams is associated with a pre-established first classification probability value and a pre-established second classification probability value, wherein the pre-established first classification probability value is indicative of whether a corresponding character-based n-gram of the dictionary set is likely to be in a first domain name category, wherein the pre-established second classification probability value is indicative of whether the corresponding character-based n-gram of the dictionary set is likely to be in a second domain name category; identifying a domain set of character-based n-grams corresponding to a domain name string, the domain name string being associated with a domain name; classifying the domain name in the first domain name category in response to at least one of the pre-established first classification probability value of a first character-based n-gram in the dictionary set corresponding to a first selected character-based n-gram in the domain set or the pre-established first classification probability value of a second character-based n-gram in the dictionary set corresponding to a second selected character-based n-gram in the domain set being higher than a first classification predetermined threshold; and classifying the domain name in the second domain name category in response to at least one of the pre-established second classification probability value of the first character-based n-gram in the dictionary set corresponding to the first selected character-based n-gram in the domain set or the pre-established second classification probability value of the second character-based n-gram in the dictionary set corresponding to the second selected character-based n-gram in the domain set being higher than a second classification predetermined threshold.

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2. The printer processing method described in claim 1 , wherein: the binary data conversion step includes: a first conversion step for converting the first mark-up language document to a converted mark-up language document that conforms to output format information relating to a document format for documents output to the printer, and a second conversion step for converting the converted mark-up language document to the binary data, based on the model-dependent information.

3. The printer processing method described in claim 2 , wherein: the output format information is user-defined; and the first conversion step includes converting the first mark-up language document to an output format based on the output format information.