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10. The method of claim 1 , further comprising analyzing said information over the plurality of search events and over the plurality of index-type search engines to generate statistical data reflective of how search engine users locate the destination.

10. The method of claim 1 , further comprising analyzing said information over the plurality of search events and over the plurality of index-type search engines to generate statistical data reflective of how search engine users locate the destination. 11. The method of claim 10 , wherein analyzing said information comprises sorting said position information by search engine and by query.

1. A speech recognition method in which an entry corresponding to a speech input is selected from a list of entries, the method comprising: detecting the speech input; recognizing a phoneme sequence of the speech input; providing a list of fragments of entries in the list of entries, the fragments being based on a subword or phoneme level, each entry in the list of entries being formable by concatenating one or more fragments in the list of fragments; for each given fragment in the list of fragments, comparing one or more phonemes of the recognized phoneme sequence to the given fragment to generate a fragment score that represents how well the one or more phonemes of the recognized phoneme sequence fits to the given fragment on the subword or phoneme level; for each given entry in the list of entries, calculating an entry score based on the fragment scores of the one or more fragments in the list of fragments that form the given entry; and generating a candidate list of best matching entries based on the calculated entry scores.

1. A speech recognition method in which an entry corresponding to a speech input is selected from a list of entries, the method comprising: detecting the speech input; recognizing a phoneme sequence of the speech input; providing a list of fragments of entries in the list of entries, the fragments being based on a subword or phoneme level, each entry in the list of entries being formable by concatenating one or more fragments in the list of fragments; for each given fragment in the list of fragments, comparing one or more phonemes of the recognized phoneme sequence to the given fragment to generate a fragment score that represents how well the one or more phonemes of the recognized phoneme sequence fits to the given fragment on the subword or phoneme level; for each given entry in the list of entries, calculating an entry score based on the fragment scores of the one or more fragments in the list of fragments that form the given entry; and generating a candidate list of best matching entries based on the calculated entry scores. 19. The method of claim 1 further comprising storing the entries together with wildcards, where the wildcards are indicative that a user's utterance for selecting one entry from the list of entries contains more than the entry itself.

1. A method for detecting anatomic landmarks of a left ventricle (LV) in a magnetic resonance (MR) long axis image slice, comprising: detecting a plurality of apex candidates in the MR long axis image slice using a trained apex detector; detecting a plurality of base plane candidates in the MR long axis image slice using a trained base plane detector; generating a joint context for each apex-base plane candidate pair; and determining a best apex-base plane candidate pair based on the generated joint context using a trained joint context detector.

1. A method for detecting anatomic landmarks of a left ventricle (LV) in a magnetic resonance (MR) long axis image slice, comprising: detecting a plurality of apex candidates in the MR long axis image slice using a trained apex detector; detecting a plurality of base plane candidates in the MR long axis image slice using a trained base plane detector; generating a joint context for each apex-base plane candidate pair; and determining a best apex-base plane candidate pair based on the generated joint context using a trained joint context detector. 2. The method of claim 1 , wherein said step of detecting a plurality of base plane candidates in the MR long axis image slice using a trained base plane detector comprises: detecting a plurality of basal annulus point candidates in the MR long axis image slice using a trained annulus point detector; generating base plane hypotheses by generating a joint context for combinations of detected basal annulus point candidates; and detecting the plurality of base plane candidates from the base plane hypotheses using the trained base plane detector.

11. The method of claim 1 , wherein adjusting edge weights comprises: obtaining a feature vector representation of each document, each feature vector indicating an amount of occurrences of respective n-grams in the respective document with respective cardinal values; and for each of at least some of the feature vectors, selecting cardinal values of the respective feature vector correspond to n-grams in the adjustment n-gram set and adjusting the selected cardinal values.

11. The method of claim 1 , wherein adjusting edge weights comprises: obtaining a feature vector representation of each document, each feature vector indicating an amount of occurrences of respective n-grams in the respective document with respective cardinal values; and for each of at least some of the feature vectors, selecting cardinal values of the respective feature vector correspond to n-grams in the adjustment n-gram set and adjusting the selected cardinal values. 13. The method of claim 11 , wherein adjusting edge weights comprises: steps for determining similarity of feature vectors.

1. A method to be executed by a processor in an electronic environment, comprising: receiving an object in a network environment; receiving a search query that includes a first regular expression, wherein the first regular expression comprises a string according to one or more syntax rules; mapping the first regular expression to a first attribute, wherein the first attribute is included amongst a plurality of attributes provided in an attribute map, and wherein the plurality of attributes each represent respective regular expressions; and parsing only the regular expressions related to attributes that have not been found in the object, wherein if a parsing activity identifies a match for the first regular expression in the object, then other regular expressions that contain the first attribute are not searched for the search query.

1. A method to be executed by a processor in an electronic environment, comprising: receiving an object in a network environment; receiving a search query that includes a first regular expression, wherein the first regular expression comprises a string according to one or more syntax rules; mapping the first regular expression to a first attribute, wherein the first attribute is included amongst a plurality of attributes provided in an attribute map, and wherein the plurality of attributes each represent respective regular expressions; and parsing only the regular expressions related to attributes that have not been found in the object, wherein if a parsing activity identifies a match for the first regular expression in the object, then other regular expressions that contain the first attribute are not searched for the search query. 4. The method of claim 1 , wherein an attribute index is used to represent the plurality of attributes, and wherein the attribute index is implemented as a bit vector having one bit position associated with each of the attributes.

16. A computer-readable memory medium containing instructions that control a computer processor to search a corpus of documents, each document having at least one sentence, by performing a method comprising: receiving a relationship search query that designates a desired grammatical relationship between a first entity and at least one of a second entity or an action; transforming the search query into a Boolean expression; determining a set of data objects that match the Boolean expression using a keyword-style search of a data structure that indexes terms of the documents by including, for at least some of a plurality of the terms, grammatical relationship information that specifies that the corresponding term is a subject, object, or modifier of another term, and including for at least one of the plurality of terms having the included grammatical relationship information, semantic information that specifies an entity type that identifies the term as a type of person, location, or thing; when the received relationship search query designates a desired grammatical relationship between the first entity and any action, returning an indication of a plurality of matching objects in the corpus that encompasses the first entity along with an indication of the corresponding action encompassed by the matching objects; and otherwise, returning an indication of a plurality of matching objects in the corpus that encompass the desired relationship.

16. A computer-readable memory medium containing instructions that control a computer processor to search a corpus of documents, each document having at least one sentence, by performing a method comprising: receiving a relationship search query that designates a desired grammatical relationship between a first entity and at least one of a second entity or an action; transforming the search query into a Boolean expression; determining a set of data objects that match the Boolean expression using a keyword-style search of a data structure that indexes terms of the documents by including, for at least some of a plurality of the terms, grammatical relationship information that specifies that the corresponding term is a subject, object, or modifier of another term, and including for at least one of the plurality of terms having the included grammatical relationship information, semantic information that specifies an entity type that identifies the term as a type of person, location, or thing; when the received relationship search query designates a desired grammatical relationship between the first entity and any action, returning an indication of a plurality of matching objects in the corpus that encompasses the first entity along with an indication of the corresponding action encompassed by the matching objects; and otherwise, returning an indication of a plurality of matching objects in the corpus that encompass the desired relationship. 22. The memory medium of claim 16 wherein the designated desired grammatical relationship specifies at least one of a prepositional constraint, a document keyword constraint, or a document metadata constraint.

1. An image processing method comprising the acts: capturing imagery with a camera portion of a mobile system, the system also including a processor controlled by configuration instructions stored in a memory; selecting a first recognition process to apply to the captured imagery, from among several recognition processes that the system is equipped to apply, said selection of the first recognition process being based on context information rather than on express user instruction; launching the selected first recognition process, employing said processor, to discern information about a subject depicted in the captured imagery; receiving first data, after said selected first recognition process has been launched, said first data comprising (a) user input data indicating express or implied user encouragement or discouragement of said first recognition process by the user, and/or (b) a detection state metric, representing a quantified likelihood that a recognition goal sought by the first recognition process will be reached; and allocating increased or decreased resources to said first recognition process based on said received first data, said allocation of increased resources being apart from increased resource usage due to any increasing base complexity of successive stages of the selected first recognition process.

1. An image processing method comprising the acts: capturing imagery with a camera portion of a mobile system, the system also including a processor controlled by configuration instructions stored in a memory; selecting a first recognition process to apply to the captured imagery, from among several recognition processes that the system is equipped to apply, said selection of the first recognition process being based on context information rather than on express user instruction; launching the selected first recognition process, employing said processor, to discern information about a subject depicted in the captured imagery; receiving first data, after said selected first recognition process has been launched, said first data comprising (a) user input data indicating express or implied user encouragement or discouragement of said first recognition process by the user, and/or (b) a detection state metric, representing a quantified likelihood that a recognition goal sought by the first recognition process will be reached; and allocating increased or decreased resources to said first recognition process based on said received first data, said allocation of increased resources being apart from increased resource usage due to any increasing base complexity of successive stages of the selected first recognition process. 13. The method of claim 1 that includes: selecting both first and second recognition processes to apply to the captured imagery, from among said several recognition processes the system is equipped to apply, based on context information rather than on express user instruction; launching both the first and second recognition processes; receiving second data, after said second recognition process has been launched, said second data comprising (a) user input data indicating express or implied user encouragement or discouragement of said second recognition process by the user, and/or (b) a detection state metric, representing a quantified likelihood that a recognition goal sought by the second recognition process will be reached; and based on the received first and second data, allocating increased resources to the first recognition process, and allocating reduced resources to the second recognition process.

1. A method of tying a geospatial location to a PIDF-LO file, comprising: receiving, at a physical network server, a Presence Information Data Format-Location Object (PIDF-LO) file; determining, at said physical network server, that said PIDF-LO file lacks geospatial location information identifying a specific physical zone; identifying a universal resource locator (URL) of XML content relating to a location relevant to said PIDF-LO compliant file; requesting said geospatial location information, through said physical network server; associating, at said physical network server, said geospatial location information with said XML content; and inserting, at said physical network server, said URL of XML content into said PIDF-LO file.

1. A method of tying a geospatial location to a PIDF-LO file, comprising: receiving, at a physical network server, a Presence Information Data Format-Location Object (PIDF-LO) file; determining, at said physical network server, that said PIDF-LO file lacks geospatial location information identifying a specific physical zone; identifying a universal resource locator (URL) of XML content relating to a location relevant to said PIDF-LO compliant file; requesting said geospatial location information, through said physical network server; associating, at said physical network server, said geospatial location information with said XML content; and inserting, at said physical network server, said URL of XML content into said PIDF-LO file. 7. The method of tying a geospatial location to a PIDF-LO file according to claim 1 , wherein: said geospatial location information identifies a specific latitude/longitude.

26. The speech recognition system of claim 24 , further comprising: a speech recognizer communicatively coupled to receive the signals from the sound classifier and operable to distinguish sets of fragments containing speech from sets of fragments not containing speech based at least in part on the classifications indicated in the signals received from the sound classifier.

26. The speech recognition system of claim 24 , further comprising: a speech recognizer communicatively coupled to receive the signals from the sound classifier and operable to distinguish sets of fragments containing speech from sets of fragments not containing speech based at least in part on the classifications indicated in the signals received from the sound classifier. 31. The speech recognition system of claim 26 wherein a speech detector of the speech recognizer employs the classifications to detect at least one of a start or a stop of speech by based at least in part on the received classifications of each of the plurality of frames of audio.

1. A method performed by one or more computers, the method comprising: obtaining an input image; processing the input image using a deep convolutional neural network to generate an alternative representation for the input image, wherein: (i) the deep convolutional neural network includes a plurality of core neural network layers that are each defined by a respective set of parameters having current values that were determined by training a second neural network having the plurality of core neural network layers on a plurality of training images, and (ii) the second neural network was trained in part by processing, with an output layer of the second neural network and for each training image, an output of a last core neural network layer of the plurality of core neural network layers to generate, for each of a plurality of object categories, a respective score that represents a predicted likelihood that the training image contains an image of an object from the object category; and processing the alternative representation for the input image using a third neural network to generate a sequence of a plurality of words in a target natural language that describes the input image.

1. A method performed by one or more computers, the method comprising: obtaining an input image; processing the input image using a deep convolutional neural network to generate an alternative representation for the input image, wherein: (i) the deep convolutional neural network includes a plurality of core neural network layers that are each defined by a respective set of parameters having current values that were determined by training a second neural network having the plurality of core neural network layers on a plurality of training images, and (ii) the second neural network was trained in part by processing, with an output layer of the second neural network and for each training image, an output of a last core neural network layer of the plurality of core neural network layers to generate, for each of a plurality of object categories, a respective score that represents a predicted likelihood that the training image contains an image of an object from the object category; and processing the alternative representation for the input image using a third neural network to generate a sequence of a plurality of words in a target natural language that describes the input image. 2. The method of claim 1 , wherein processing the input image using the deep convolutional neural network comprises processing the input image through each of the core neural network layers, and wherein the alternative representation for the input image is an output generated by a last core neural network layer of the plurality of core neural network layers of the deep convolutional neural network.

10. A non-transitory computer-readable storage medium storing instructions that when executed by a computer cause the computer to perform a method for estimating a similarity level between documents, the method comprising: extracting, from a set of documents, a set of semantic entities, wherein a respective semantic entity includes a meaningful sequence of characters; determining, for the respective semantic entity, a predefined word group to which the respective semantic entity belongs, wherein the predefined word group indicates a content category associated with the respective semantic entity; computing an inverse document frequency (IDF) value for the respective semantic entity; assigning a weight to the computed IDF value based on the predefined word group to which the respective semantic entity belongs; calculating the similarity level sim(A,B) between a respective document A in the set of documents and a target document B, based on weighted IDF values associated with one or more of the extracted semantic entities, wherein calculating the similarity level involves calculating: sim ⁡ ( A , B ) = 2 * ∑ e ∈ A ⋂ B ⁢ ( idf e * w e ) ∑ e ∈ A ⁢ ( idf e * w e ) + ∑ e ∈ B ⁢ ( idf e * w e ) , wherein idf e indicates an IDF value for an entity e, and wherein w e indicates a weight for entity e; and producing a result indicating documents that are similar to the target document based on the calculated similarity level.

10. A non-transitory computer-readable storage medium storing instructions that when executed by a computer cause the computer to perform a method for estimating a similarity level between documents, the method comprising: extracting, from a set of documents, a set of semantic entities, wherein a respective semantic entity includes a meaningful sequence of characters; determining, for the respective semantic entity, a predefined word group to which the respective semantic entity belongs, wherein the predefined word group indicates a content category associated with the respective semantic entity; computing an inverse document frequency (IDF) value for the respective semantic entity; assigning a weight to the computed IDF value based on the predefined word group to which the respective semantic entity belongs; calculating the similarity level sim(A,B) between a respective document A in the set of documents and a target document B, based on weighted IDF values associated with one or more of the extracted semantic entities, wherein calculating the similarity level involves calculating: sim ⁡ ( A , B ) = 2 * ∑ e ∈ A ⋂ B ⁢ ( idf e * w e ) ∑ e ∈ A ⁢ ( idf e * w e ) + ∑ e ∈ B ⁢ ( idf e * w e ) , wherein idf e indicates an IDF value for an entity e, and wherein w e indicates a weight for entity e; and producing a result indicating documents that are similar to the target document based on the calculated similarity level. 23. The computer-readable storage medium of claim 10 , wherein calculating the similarity level between document A and document B involves calculating: sim ⁡ ( A , B ) = ∑ e ∈ A ⋂ B ⁢ ( idf e * w e ) 2 ∑ e ∈ A ⁢ ( idf e * w e ) 2 * ∑ e ∈ B ⁢ ( idf e * w e ) 2 .

42. A system comprising: a processor; a machine-readable medium coupled with the processor; a network interface coupled with the processor; and a server software product tangibly stored on the machine-readable medium, the server software product comprising instructions that cause a programmable processor to perform operations for reversing a format of a reversible electronic document comprising two formatting states, the operations comprising: in a first document conversion for reversing an electronic document in a binary format into a markup language format, transforming the electronic document from the binary format into the markup language format by extracting a subset of information from the electronic document in the binary format according to predefined extraction parameters, inserting the subset of the information into portions of the electronic document in the markup language format, and inserting a reduced-information version of the electronic document in the binary format into a storage location of the electronic document in the markup language format such that the version of the electronic document in the binary format is retrievable upon reversal of the electronic document in the markup language format in a second document conversion, wherein the reduced-information version comprises data that is unconvertible to the markup language format; and in the second document conversion for reversing an electronic document in the markup language format into the binary format, transforming the electronic document from the markup language format into the binary format by extracting a first subset of information, from the electronic document in the markup language format, that is recognized in the binary format, extracting a second subset of information, from the electronic document in the markup language format, that is not recognized in the binary format, inserting the first subset of information, from the electronic document in the markup language format, into portions of the electronic document in the binary format, and placing the second subset of information from the electronic document in the markup language format into a storage location of the electronic document in the binary format such that the unrecognized information is retrievable upon reversal of the electronic document in the binary format in the first document conversion, wherein the second subset of information comprises data that is unconvertible to the binary format.

42. A system comprising: a processor; a machine-readable medium coupled with the processor; a network interface coupled with the processor; and a server software product tangibly stored on the machine-readable medium, the server software product comprising instructions that cause a programmable processor to perform operations for reversing a format of a reversible electronic document comprising two formatting states, the operations comprising: in a first document conversion for reversing an electronic document in a binary format into a markup language format, transforming the electronic document from the binary format into the markup language format by extracting a subset of information from the electronic document in the binary format according to predefined extraction parameters, inserting the subset of the information into portions of the electronic document in the markup language format, and inserting a reduced-information version of the electronic document in the binary format into a storage location of the electronic document in the markup language format such that the version of the electronic document in the binary format is retrievable upon reversal of the electronic document in the markup language format in a second document conversion, wherein the reduced-information version comprises data that is unconvertible to the markup language format; and in the second document conversion for reversing an electronic document in the markup language format into the binary format, transforming the electronic document from the markup language format into the binary format by extracting a first subset of information, from the electronic document in the markup language format, that is recognized in the binary format, extracting a second subset of information, from the electronic document in the markup language format, that is not recognized in the binary format, inserting the first subset of information, from the electronic document in the markup language format, into portions of the electronic document in the binary format, and placing the second subset of information from the electronic document in the markup language format into a storage location of the electronic document in the binary format such that the unrecognized information is retrievable upon reversal of the electronic document in the binary format in the first document conversion, wherein the second subset of information comprises data that is unconvertible to the binary format. 44. The system of claim 42 , wherein the electronic document comprises rules governing information capture, and at least one of the subsets of information comprises at least a portion of the rules governing information capture.

40. The method for presenting an electronic document of claim 39 wherein the knowledge database comprises at least one of a cognitive cluster database, a rarity database, a geographical similarity database, a part of speech database, and a context database.

40. The method for presenting an electronic document of claim 39 wherein the knowledge database comprises at least one of a cognitive cluster database, a rarity database, a geographical similarity database, a part of speech database, and a context database. 41. The method for presenting an electronic document of claim 40 further comprising the step of comparing each word with the rarity database to assign a rarity value to each word.

9. A computer system, comprising: at least one processor; and at least one memory, communicatively coupled to the at least one processor and containing computer-readable instructions that, when executed by the at least one processor, perform a method of managing a graphical user interface (GUI), the method comprising: providing a data model that comprises a model object, wherein the model object comprises at least a first task mapped to a first command for performing the first task; providing a user interface (UI) conceptual model that describes at least one UI element for managing the GUI, wherein the at least one UI element references the model object; adding a first reference to the at least one UI element to a first page definition of the UI conceptual model; adding a second reference to the at least one UI element to a second page definition of the UI conceptual model; rendering the at least one UI element on a first corresponding page and a second corresponding page of the GUI, wherein functionality associated with the first corresponding page and the second corresponding page of the GUI is modified upon rendering the at least one UI element; modifying the at least one UI element in the UI conceptual model; automatically modifying the rendering of the at least one UI element on the first corresponding page and the second corresponding page of the GUI without recoding the first page definition or the second page definition; and activating the at least one rendered UI element, wherein activating the at least one rendered UI element includes executing the first task.

9. A computer system, comprising: at least one processor; and at least one memory, communicatively coupled to the at least one processor and containing computer-readable instructions that, when executed by the at least one processor, perform a method of managing a graphical user interface (GUI), the method comprising: providing a data model that comprises a model object, wherein the model object comprises at least a first task mapped to a first command for performing the first task; providing a user interface (UI) conceptual model that describes at least one UI element for managing the GUI, wherein the at least one UI element references the model object; adding a first reference to the at least one UI element to a first page definition of the UI conceptual model; adding a second reference to the at least one UI element to a second page definition of the UI conceptual model; rendering the at least one UI element on a first corresponding page and a second corresponding page of the GUI, wherein functionality associated with the first corresponding page and the second corresponding page of the GUI is modified upon rendering the at least one UI element; modifying the at least one UI element in the UI conceptual model; automatically modifying the rendering of the at least one UI element on the first corresponding page and the second corresponding page of the GUI without recoding the first page definition or the second page definition; and activating the at least one rendered UI element, wherein activating the at least one rendered UI element includes executing the first task. 12. The computer system of claim 9 , wherein the model object further comprises a data item having rules and conditions for transforming data.

1. A method for encoding speech comprising: processing an input speech signal using an encoder, resulting in a compressed encoder representation of the input speech signal, if a speech recognizer identifies a corresponding dictionary speech element, which approximates the input speech signal, determining a compressed recognizer representation of the corresponding dictionary speech element, calculating one or more differences between the compressed encoder representation and the compressed recognizer representation, compiling compressed speech information that includes representations of the one or more differences; and the method further comprising, if the speech recognizer does not identify a corresponding dictionary speech element, compiling the compressed speech information to include the compressed encoder representation of the input speech signal, and not to include the one or more differences.

1. A method for encoding speech comprising: processing an input speech signal using an encoder, resulting in a compressed encoder representation of the input speech signal, if a speech recognizer identifies a corresponding dictionary speech element, which approximates the input speech signal, determining a compressed recognizer representation of the corresponding dictionary speech element, calculating one or more differences between the compressed encoder representation and the compressed recognizer representation, compiling compressed speech information that includes representations of the one or more differences; and the method further comprising, if the speech recognizer does not identify a corresponding dictionary speech element, compiling the compressed speech information to include the compressed encoder representation of the input speech signal, and not to include the one or more differences. 12. The method of claim 1 , further comprising storing the compressed speech information.

19. The system of claim 14 , wherein the index includes an identification of one or more terms included within a received document.

19. The system of claim 14 , wherein the index includes an identification of one or more terms included within a received document. 20. The system of claim 19 , wherein the index further includes an identification of a position of each of the one or more terms included within the received document.

2. The method of claim 1 wherein the graphical context menu further comprises a menu item for displaying a full menu.

2. The method of claim 1 wherein the graphical context menu further comprises a menu item for displaying a full menu. 3. The method of claim 2 further comprising rendering, in response to actuating the menu item for displaying the full menu, a graphical list of menu items which includes menu items not displayed in the graphical context menu.

6. A computer program product comprising: a computer readable device storing computer usable program code for magnifying a portion of a document in a browser, the computer program product comprising: computer usable program code for presenting a first document in a first display area in the browser, wherein the first document is displayed with an original font size; computer usable program code for receiving a selection of a portion of the first document for magnified display; computer usable program code for generating a magnified display of the portion of the first document to form a magnified portion, wherein the magnified display of the portion comprises text in a second font size that is larger than the original font size, wherein generating the magnified display further comprises: presenting the magnified portion in a magnifier window in a second display area within the first display area, wherein the second display area is presented on top of at least a portion of the first display area and obstructs from view the portion of the first display area; analyzing a document object model for the first document; and identifying a portion of the document object model that corresponds to the portion of the first document, wherein the magnified display of the portion of the first document is generated based on the portion of the document object model; computer usable program code for receiving a request for an action, wherein the action is to be applied within the magnified display in the magnifier window; and computer usable program code for performing the action with respect to the magnified display, wherein the magnified portion presents the portion of the document object model and retains full browser functionality within the magnifier window.

6. A computer program product comprising: a computer readable device storing computer usable program code for magnifying a portion of a document in a browser, the computer program product comprising: computer usable program code for presenting a first document in a first display area in the browser, wherein the first document is displayed with an original font size; computer usable program code for receiving a selection of a portion of the first document for magnified display; computer usable program code for generating a magnified display of the portion of the first document to form a magnified portion, wherein the magnified display of the portion comprises text in a second font size that is larger than the original font size, wherein generating the magnified display further comprises: presenting the magnified portion in a magnifier window in a second display area within the first display area, wherein the second display area is presented on top of at least a portion of the first display area and obstructs from view the portion of the first display area; analyzing a document object model for the first document; and identifying a portion of the document object model that corresponds to the portion of the first document, wherein the magnified display of the portion of the first document is generated based on the portion of the document object model; computer usable program code for receiving a request for an action, wherein the action is to be applied within the magnified display in the magnifier window; and computer usable program code for performing the action with respect to the magnified display, wherein the magnified portion presents the portion of the document object model and retains full browser functionality within the magnifier window. 9. The computer program product of claim 6 , wherein the action comprises a selection of a portion of text within the portion of the magnified display to be copied and pasted into a different application and the step of performing the action comprises copying the portion of the text selected within the portion of the magnified display and pasting the portion of the text into a second document corresponding to the different application.

1. A method comprising: configuring, by a computer system comprising computer hardware, a human-capital-management (HCM) master taxonomy and a HCM language library; wherein the HCM master taxonomy comprises a plurality of levels that range from more general to more specific, each level of the plurality of levels comprising a plurality of nodes; wherein the plurality of levels comprises a job-species level and a job-family level, the job-species level comprising a level of greatest specificity in the plurality of levels, the job-family level comprising a level of specificity immediately above the job-species level; transforming, by the computer system, human-capital information via the HCM language library; classifying, by the computer system the transformed human-capital information into a job-family node selected from the plurality of nodes at the job-family level; analyzing, by the computer system, selected attributes of a plurality of job-species nodes, the plurality of job species comprising ones of the plurality of nodes at the job-species level that are positioned beneath the job-family node; and wherein the analyzing comprises: identifying differences between node attributes of the plurality of job species; for each identified difference of the identified differences, analyzing an impact of the identified difference on a spotlight attribute; and determining one or more of the node attributes to be key performance indicators (KPIs) for the spotlight attribute.

1. A method comprising: configuring, by a computer system comprising computer hardware, a human-capital-management (HCM) master taxonomy and a HCM language library; wherein the HCM master taxonomy comprises a plurality of levels that range from more general to more specific, each level of the plurality of levels comprising a plurality of nodes; wherein the plurality of levels comprises a job-species level and a job-family level, the job-species level comprising a level of greatest specificity in the plurality of levels, the job-family level comprising a level of specificity immediately above the job-species level; transforming, by the computer system, human-capital information via the HCM language library; classifying, by the computer system the transformed human-capital information into a job-family node selected from the plurality of nodes at the job-family level; analyzing, by the computer system, selected attributes of a plurality of job-species nodes, the plurality of job species comprising ones of the plurality of nodes at the job-species level that are positioned beneath the job-family node; and wherein the analyzing comprises: identifying differences between node attributes of the plurality of job species; for each identified difference of the identified differences, analyzing an impact of the identified difference on a spotlight attribute; and determining one or more of the node attributes to be key performance indicators (KPIs) for the spotlight attribute. 18. The method of claim 1 , the method comprising determining, via the determined KPIs, that a new job-species node should be created.

32. A document data processing method for document retrieval according to claim 30, wherein said concatenated component character table is prepared by mapping sets of character codes to the bit list having a number of entries which is smaller than the number of combinations of the characters used actually by using a hash function.

32. A document data processing method for document retrieval according to claim 30, wherein said concatenated component character table is prepared by mapping sets of character codes to the bit list having a number of entries which is smaller than the number of combinations of the characters used actually by using a hash function. 35. A document data processing method for document retrieval according to claim 32, wherein the character codes are mapped to a number of codes of entries which is smaller than that of the characters used actually by using said hash function, whereon sets of the hashed character codes are mapped to the bit list having a number of entries smaller than the number of the actually used character strings by using another hash function.

15. A computer program product, the computer program product comprising: a computer readable storage medium having computer readable program embodied therewith, the computer readable program comprising: computer readable program configured to model a software system having pairs of coupled software components to yield a platform-independent model of pairs of respective platform-independent software component models, such that each software component model being a placeholder associated with a respective variable set of concrete platform-specific software components; computer readable program configured to apply a materialization process to the platform-independent model to yield a platform-specific model by selecting respective concrete platform-specific software components for at least some of the software component models; computer readable program configured to analyze the platform-specific model to identify automatically mismatched pairs of concrete platform-specific software components; computer readable program configured to re-model the platform-specific model such that each identified mismatched pair becomes coupled together via at least one configurable glue component model which comprises interface maps usable to eliminate the mismatch; computer readable program configured to configure the glue component models by determining, in response to a feedback from a user, at least: the interface maps, method maps associated with the determined interface maps, parameter maps associated with the determined method maps, and code snippets associated with at least one of the determined interface maps, the determined method maps, and the determined parameter maps; and computer readable program configured to transform the configured glue component model into a computer code in the platform-specific language to eliminate the mismatch, said glue component model is transformed by assembling all determined code snippets into a single piece of code.

15. A computer program product, the computer program product comprising: a computer readable storage medium having computer readable program embodied therewith, the computer readable program comprising: computer readable program configured to model a software system having pairs of coupled software components to yield a platform-independent model of pairs of respective platform-independent software component models, such that each software component model being a placeholder associated with a respective variable set of concrete platform-specific software components; computer readable program configured to apply a materialization process to the platform-independent model to yield a platform-specific model by selecting respective concrete platform-specific software components for at least some of the software component models; computer readable program configured to analyze the platform-specific model to identify automatically mismatched pairs of concrete platform-specific software components; computer readable program configured to re-model the platform-specific model such that each identified mismatched pair becomes coupled together via at least one configurable glue component model which comprises interface maps usable to eliminate the mismatch; computer readable program configured to configure the glue component models by determining, in response to a feedback from a user, at least: the interface maps, method maps associated with the determined interface maps, parameter maps associated with the determined method maps, and code snippets associated with at least one of the determined interface maps, the determined method maps, and the determined parameter maps; and computer readable program configured to transform the configured glue component model into a computer code in the platform-specific language to eliminate the mismatch, said glue component model is transformed by assembling all determined code snippets into a single piece of code. 18. The computer program product according to claim 15 , further comprising computer readable program configured to wrap, for each pair having software components implemented in different technologies, one of the components of the pair in a wrap usable for integrating, on technology level, between the components, to yield a corresponding wrapper code usable for the assembling.

7. An event analysis method by a management computer which includes storage resources and manages a plurality of computers to be managed, the method comprising: storing, in the storage resources: (1) topologies indicating relationships among a plurality of managed objects, which are the plurality of managed computers or a plurality of components included in the plurality of managed computers; (2) event propagation models, each including a set of information on one or more events and a causal event to cause the one or more events defined with types of events and types of managed objects where events occur, one of the event propagation models indicating that events of type A which occur to managed objects of type 1 cause events of type B to occur to management objects of type 2; and (3) causality information including one or more causal relations, one of the causal relations indicating that a first event of type A which occurs to a first managed object of type 1 causes a second event of type B to occur to a second managed object of type 2; (A) detecting an event relating to a problem that has occurred to a managed object; (B) determining whether a first causal relation to be used in analysis of the detected event has been created in the causality information; (C) performing, in a case where the processor determines that the first causal relation has not been created in (B), an on-demand expansion based on one of the topologies and one of the event propagation models to create the first causal relation in the causality information; performing, in a case where the processor determines that the first causal relation is being created in (B), an on-demand expansion based on one of the topologies and one of the event propagation models to create the first causal relation in the causality information; and (D) analyzing the detected event using the first causal relation.

7. An event analysis method by a management computer which includes storage resources and manages a plurality of computers to be managed, the method comprising: storing, in the storage resources: (1) topologies indicating relationships among a plurality of managed objects, which are the plurality of managed computers or a plurality of components included in the plurality of managed computers; (2) event propagation models, each including a set of information on one or more events and a causal event to cause the one or more events defined with types of events and types of managed objects where events occur, one of the event propagation models indicating that events of type A which occur to managed objects of type 1 cause events of type B to occur to management objects of type 2; and (3) causality information including one or more causal relations, one of the causal relations indicating that a first event of type A which occurs to a first managed object of type 1 causes a second event of type B to occur to a second managed object of type 2; (A) detecting an event relating to a problem that has occurred to a managed object; (B) determining whether a first causal relation to be used in analysis of the detected event has been created in the causality information; (C) performing, in a case where the processor determines that the first causal relation has not been created in (B), an on-demand expansion based on one of the topologies and one of the event propagation models to create the first causal relation in the causality information; performing, in a case where the processor determines that the first causal relation is being created in (B), an on-demand expansion based on one of the topologies and one of the event propagation models to create the first causal relation in the causality information; and (D) analyzing the detected event using the first causal relation. 11. An event analysis method according to claim 7 , further comprising (H) suspending on-demand expansion of other causal relations having the same cause as a causal event indicated by the first causal relation during the on-demand expansion relating to the first causal relation.

17. At least one tangible non-transitory machine-readable medium having instructions store thereon configured, upon execution by one or more machines, to enable interactive editing of software source code by performing operations comprising: maintaining a structured tree representation of the source code, the structured tree representation comprising a plurality of nodes, including nodes associated with respective identifiers, operands, operators, variables, and flow control elements in the source code, and nodes associated with non-syntax aspects of a text-based representation of the source code; and enabling the source code to be interactively edited by performing a continuous loop of operations including, unparsing a localized portion of the structured tree representation corresponding to a portion of source code displayed via an editor and being edited and deriving tokens corresponding to a localized portion of source code being edited; processing the tokens to generate a text-based representation of the localized portion of the source code; enabling the text-based representation of the source code to be edited by a user via the editor; in response to an edit to the text-based representation of the source code, at least one of, i) generating one or more new tokens; ii) deleting one or more tokens that existed before the edit; and iii) changing text content associated with an existing token; parsing tokens including tokens corresponding to the portion of the source that was edited; and processing the parsed tokens to update the localized portion of the structured tree representation to reflect the edit to the source code.

17. At least one tangible non-transitory machine-readable medium having instructions store thereon configured, upon execution by one or more machines, to enable interactive editing of software source code by performing operations comprising: maintaining a structured tree representation of the source code, the structured tree representation comprising a plurality of nodes, including nodes associated with respective identifiers, operands, operators, variables, and flow control elements in the source code, and nodes associated with non-syntax aspects of a text-based representation of the source code; and enabling the source code to be interactively edited by performing a continuous loop of operations including, unparsing a localized portion of the structured tree representation corresponding to a portion of source code displayed via an editor and being edited and deriving tokens corresponding to a localized portion of source code being edited; processing the tokens to generate a text-based representation of the localized portion of the source code; enabling the text-based representation of the source code to be edited by a user via the editor; in response to an edit to the text-based representation of the source code, at least one of, i) generating one or more new tokens; ii) deleting one or more tokens that existed before the edit; and iii) changing text content associated with an existing token; parsing tokens including tokens corresponding to the portion of the source that was edited; and processing the parsed tokens to update the localized portion of the structured tree representation to reflect the edit to the source code. 32. The at least one tangible non-transitory machine-readable medium of claim 17 , wherein the instructions are configured to be executed on a plurality of servers to implement a web service that enables interactive editing of software source code.

9. A reconstruction method, comprising: acquiring a set of projection data from a plurality of views around an imaged volume; performing an iterative reconstruction of the set of projection data by solving an objective function comprising at least a dictionary-based term, wherein the dictionary-based term employs dictionary learning that employs at least one dictionary comprising two-dimensional image patches oriented in different directions; and generating a reconstructed image upon completion of the iterative reconstruction.

9. A reconstruction method, comprising: acquiring a set of projection data from a plurality of views around an imaged volume; performing an iterative reconstruction of the set of projection data by solving an objective function comprising at least a dictionary-based term, wherein the dictionary-based term employs dictionary learning that employs at least one dictionary comprising two-dimensional image patches oriented in different directions; and generating a reconstructed image upon completion of the iterative reconstruction. 15. The method of claim 9 , further comprising: performing an initial analytic image reconstruction on all or part of the set of projection data to generate an initial image as an input to the iterative reconstruction.

1. A text subtitle decoder for decoding text subtitle streams recorded on a recording medium, comprising: a text subtitle processor configured to parse the text subtitle stream into text data to be displayed in the subtitle region, region style information indicating a region style to be applied to an overall region including the text data, and inline style information indicating at least one font related style to be applied to the text data, the parsed text data and inline style information being transferred to a different area of the text subtitle decoder than the parsed region style information; a text renderer configured to receive the text data and the inline style information; and a controller configured to input the region style information into the text renderer, wherein the text renderer is controlled by the controller, and converts the text data into bitmap data using the region style information and the inline style information.

1. A text subtitle decoder for decoding text subtitle streams recorded on a recording medium, comprising: a text subtitle processor configured to parse the text subtitle stream into text data to be displayed in the subtitle region, region style information indicating a region style to be applied to an overall region including the text data, and inline style information indicating at least one font related style to be applied to the text data, the parsed text data and inline style information being transferred to a different area of the text subtitle decoder than the parsed region style information; a text renderer configured to receive the text data and the inline style information; and a controller configured to input the region style information into the text renderer, wherein the text renderer is controlled by the controller, and converts the text data into bitmap data using the region style information and the inline style information. 3. The text subtitle decoder of claim 1 , further comprising: a subtitle preloading buffer configured to preload an entire portion of the text subtitle stream from the recording medium, and to provide the preloaded text subtitle stream to the text subtitle processor.

19. The method of claim 17 , wherein the identifying the opening comprises identifying a transitional phrase preceding the at least one first substantive claim limitation within the text.

19. The method of claim 17 , wherein the identifying the opening comprises identifying a transitional phrase preceding the at least one first substantive claim limitation within the text. 20. The method of claim 19 , wherein identifying the closing comprises identifying a punctuation mark following the at least one first substantive claim limitation within the text.

1. A data recording and reproducing apparatus comprising: a memory; a signal processor which captures images, processes the captured images to generate image data, and generates an image file comprising the image data; a speech recognition unit which recognizes first speech, which is related to the image data, and converts the first speech into first text data; a controller which generates first metadata using the first text data and adds the generated first metadata to the image file, wherein the controller generates a folder having a name based on the first text data and wherein the controller stores the image file in the folder having the name based on the first text data; and a storage unit which stores the generated image file a display unit which displays an image from the captured images represented by the image data included in the image file or displays the first metadata along with the image, wherein the first metadata comprises a description tag which is generated using the first text data and a keyword tag which is generated by extracting keywords from the first text data, and wherein, when newly input second speech is converted into second text data by the speech recognition unit while the images stored in the image file are displayed on the display unit, the controller generates second metadata from the second text data and compares if the second metadata is different with the first metadata in the image file, and if the second metadata is different from the first metadata in the image file, the controller adds the second metadata to the first metadata in the image file.

1. A data recording and reproducing apparatus comprising: a memory; a signal processor which captures images, processes the captured images to generate image data, and generates an image file comprising the image data; a speech recognition unit which recognizes first speech, which is related to the image data, and converts the first speech into first text data; a controller which generates first metadata using the first text data and adds the generated first metadata to the image file, wherein the controller generates a folder having a name based on the first text data and wherein the controller stores the image file in the folder having the name based on the first text data; and a storage unit which stores the generated image file a display unit which displays an image from the captured images represented by the image data included in the image file or displays the first metadata along with the image, wherein the first metadata comprises a description tag which is generated using the first text data and a keyword tag which is generated by extracting keywords from the first text data, and wherein, when newly input second speech is converted into second text data by the speech recognition unit while the images stored in the image file are displayed on the display unit, the controller generates second metadata from the second text data and compares if the second metadata is different with the first metadata in the image file, and if the second metadata is different from the first metadata in the image file, the controller adds the second metadata to the first metadata in the image file. 3. The apparatus of claim 1 , further comprising an audio output unit which outputs an audio signal, and wherein the controller converts the first metadata in the image file, the image data of which is currently displayed, into an audio signal to output the audio signal to the audio output unit while the image data of the image file is displayed on the display unit.

1. A method for making document changes using electronic messages, comprising: creating an electronic message that includes a change made to a document that is viewable within a body of the electronic message, wherein the document is collaborated on by reviewers; sending the electronic message to at least a portion of the reviewers that includes the change made to the document within the body of the electronic message; receiving a reply to the electronic message that includes a received change made directly from within the electronic message without editing the document that is to be incorporated into the document; and after receiving the reply, automatically incorporating the received change into the document.

1. A method for making document changes using electronic messages, comprising: creating an electronic message that includes a change made to a document that is viewable within a body of the electronic message, wherein the document is collaborated on by reviewers; sending the electronic message to at least a portion of the reviewers that includes the change made to the document within the body of the electronic message; receiving a reply to the electronic message that includes a received change made directly from within the electronic message without editing the document that is to be incorporated into the document; and after receiving the reply, automatically incorporating the received change into the document. 8. The method of claim 1 , wherein receiving the reply to the electronic message that includes the received change that is to be incorporated into the document comprises determining when the received change is at least one of: a comment to the change and a revised change to the change included within the electronic message.

11. A non-transitory computer storage medium storing one or more sequences of instructions which, when executed by one or more processors, cause performance of: capturing data definition language (DDL) expression text corresponding to a DDL instruction executed on a source database; determining a component set comprising at least one component of text in the DDL expression text; generating an annotation set comprising at least one annotation for at least one component of the component set, the at least one annotation comprising hierarchical data describing at least one hierarchical relationship in the component set; adding, to replication data, the annotation set and a change record corresponding to the DDL instruction executed on the source database.

11. A non-transitory computer storage medium storing one or more sequences of instructions which, when executed by one or more processors, cause performance of: capturing data definition language (DDL) expression text corresponding to a DDL instruction executed on a source database; determining a component set comprising at least one component of text in the DDL expression text; generating an annotation set comprising at least one annotation for at least one component of the component set, the at least one annotation comprising hierarchical data describing at least one hierarchical relationship in the component set; adding, to replication data, the annotation set and a change record corresponding to the DDL instruction executed on the source database. 12. The non-transitory computer storage medium of claim 11 , wherein the one or more sequences of instructions include instructions that, when executed by the one or more processors, cause performance of transmitting the annotation set and the change record to a replication client configured to perform annotation-based mapping of a source database object to a target database object based on the annotation set.

1. A method for activating a cellular phone account utilizing automated speech recognition, comprising: receiving a plurality of user supplied information over a network utilizing automated speech recognition; storing the information in a memory database; and determining if the stored information is sufficient for cellular phone activation, wherein if sufficient, automatically activating the cellular phone account based on the information received utilizing the automated speech recognition, and wherein, if not sufficient, continuing to store the information in the memory database without activating the cellular phone and allowing the user to resume an interrupted activation session without repeating the previously stored information.

1. A method for activating a cellular phone account utilizing automated speech recognition, comprising: receiving a plurality of user supplied information over a network utilizing automated speech recognition; storing the information in a memory database; and determining if the stored information is sufficient for cellular phone activation, wherein if sufficient, automatically activating the cellular phone account based on the information received utilizing the automated speech recognition, and wherein, if not sufficient, continuing to store the information in the memory database without activating the cellular phone and allowing the user to resume an interrupted activation session without repeating the previously stored information. 15. The method as recited in claim 1 , wherein instructional information is audibly transmitted to the user over the network for facilitating the programming of the cellular phone by the user.

10. A method of automatically generating encryption rules, the method comprising: by a rules generation system comprising one or more hardware processors, determining a set of file portions from a plurality of training files, at least some of the set of file portions comprising content designated as sensitive information, each of the file portions comprising a subset of content of at least one file from the plurality of training files; generating a prospective encryption rule for addition to a set of available encryption rules based at least in part on an aggregated set of the file portions, the aggregated set of the file portions including at least one file portion that appears in more than one file from the plurality of training files; determining that a number of files from the plurality of training files identified for encryption by performance of the prospective encryption rule does not satisfy a threshold number of files; and in response to said determining, iteratively modifying the prospective encryption rule until the threshold number of files of the plurality of training files are identified for encryption by performance of the modified prospective encryption rule, and storing the modified prospective encryption rule at a non-volatile repository.

10. A method of automatically generating encryption rules, the method comprising: by a rules generation system comprising one or more hardware processors, determining a set of file portions from a plurality of training files, at least some of the set of file portions comprising content designated as sensitive information, each of the file portions comprising a subset of content of at least one file from the plurality of training files; generating a prospective encryption rule for addition to a set of available encryption rules based at least in part on an aggregated set of the file portions, the aggregated set of the file portions including at least one file portion that appears in more than one file from the plurality of training files; determining that a number of files from the plurality of training files identified for encryption by performance of the prospective encryption rule does not satisfy a threshold number of files; and in response to said determining, iteratively modifying the prospective encryption rule until the threshold number of files of the plurality of training files are identified for encryption by performance of the modified prospective encryption rule, and storing the modified prospective encryption rule at a non-volatile repository. 17. The method of claim 10 , further comprising removing a file portion from the set of file portions based at least in part on an identified set of non-sensitive file portions.

14. A processor-readable storage medium having instructions encoded thereon that when executed by a processor cause actions to be performed, the actions comprising: receiving content and a plurality of target object lists from a content author, wherein each target object list identifies a unique subset of text within the received content as target objects and each unique subject differs by at least one target object; determining whether a user at the client device is validated by an online social network as having a relationship status with the content author; associating at least one of the plurality of target object lists with the validated user of the a client device based in part on the subject matter of the target objects, wherein the associating of the at least one target object list with the validated user of the client device is based on a social network relationship definable by the content author, and wherein the associating of the at least one target object list with the validated user of the client device is performed by a third party; displaying the received content at the client device, wherein the target objects in each associated target object list are identifiable within the displayed content as being available for associating comments based on the client device association; receiving from the client device a comment about one of the identifiable target objects; in response to detecting an action from the client device, the action being detected as associated with the identifiable target object, selectively displaying the received comment at the client device; receiving another input from the client device; and providing an expanded view of the comment with at least one other comment, and information about users having entered the comments.

14. A processor-readable storage medium having instructions encoded thereon that when executed by a processor cause actions to be performed, the actions comprising: receiving content and a plurality of target object lists from a content author, wherein each target object list identifies a unique subset of text within the received content as target objects and each unique subject differs by at least one target object; determining whether a user at the client device is validated by an online social network as having a relationship status with the content author; associating at least one of the plurality of target object lists with the validated user of the a client device based in part on the subject matter of the target objects, wherein the associating of the at least one target object list with the validated user of the client device is based on a social network relationship definable by the content author, and wherein the associating of the at least one target object list with the validated user of the client device is performed by a third party; displaying the received content at the client device, wherein the target objects in each associated target object list are identifiable within the displayed content as being available for associating comments based on the client device association; receiving from the client device a comment about one of the identifiable target objects; in response to detecting an action from the client device, the action being detected as associated with the identifiable target object, selectively displaying the received comment at the client device; receiving another input from the client device; and providing an expanded view of the comment with at least one other comment, and information about users having entered the comments. 15. The processor readable storage medium of claim 14 , wherein the third party is a blog service.

16. The electronic device of claim 9 , further comprising: an antenna; and a receiver coupled to the antenna and configured to receive a signal corresponding to a particular input sound.

16. The electronic device of claim 9 , further comprising: an antenna; and a receiver coupled to the antenna and configured to receive a signal corresponding to a particular input sound. 18. The electronic device of claim 16 , wherein the speech detector, the frequency analyzer, the speech direction determiner, the circuitry, the receiver, and the antenna are integrated into a fixed location communication device.

1. A method comprising: monitoring network traffic from a plurality of users including a first user and a second user; extracting words from the network traffic; building a personal vocabulary for at least the second user from the words; identifying a connection between the first user and the second user, wherein the connection is created from a trigger that includes an email including one or more subject matter keywords and the first and the second user as one or more of a recipient of the email, a sender of the email, or a part of text in the email; receiving audio of the first user originating from audio content that does not involve the second user; and converting the audio of the first user into text using a language model based at least partially on the personal vocabulary of the second user and the connection between the first user and the second user, where the audio of the first user includes at least part of the one or more subject matter keywords.

1. A method comprising: monitoring network traffic from a plurality of users including a first user and a second user; extracting words from the network traffic; building a personal vocabulary for at least the second user from the words; identifying a connection between the first user and the second user, wherein the connection is created from a trigger that includes an email including one or more subject matter keywords and the first and the second user as one or more of a recipient of the email, a sender of the email, or a part of text in the email; receiving audio of the first user originating from audio content that does not involve the second user; and converting the audio of the first user into text using a language model based at least partially on the personal vocabulary of the second user and the connection between the first user and the second user, where the audio of the first user includes at least part of the one or more subject matter keywords. 8. The method of claim 1 , where the audio content includes audio from a teleconference or a videoconference.

1. A computer-implemented method of providing a media presentation associated with a rendered document, the method comprising: optically or acoustically capturing a portion of the rendered document containing human-readable text using a portable data capture device; generating a digest of the captured portion based at least in part on content of the text of the captured portion using the portable data capture device; locating a document identifier associated with an electronic counterpart to the rendered document based at least in part on the digest of the captured portion; sending an enhancement package request including the document identifier to a media server; receiving from the media server an enhancement package associated with the document identifier, wherein the enhancement package includes multiple media presentations associated with multiple words of the rendered document, wherein each word of the multiple words is associated with a respective media presentation of the multiple media presentations; optically or acoustically capturing another portion of the rendered document containing human-readable text using the portable data capture device; locating within the enhancement package a media presentation associated with one or more identified words within the another captured portion; and presenting the associated media presentation using a display or speaker of the portable data capture device.

1. A computer-implemented method of providing a media presentation associated with a rendered document, the method comprising: optically or acoustically capturing a portion of the rendered document containing human-readable text using a portable data capture device; generating a digest of the captured portion based at least in part on content of the text of the captured portion using the portable data capture device; locating a document identifier associated with an electronic counterpart to the rendered document based at least in part on the digest of the captured portion; sending an enhancement package request including the document identifier to a media server; receiving from the media server an enhancement package associated with the document identifier, wherein the enhancement package includes multiple media presentations associated with multiple words of the rendered document, wherein each word of the multiple words is associated with a respective media presentation of the multiple media presentations; optically or acoustically capturing another portion of the rendered document containing human-readable text using the portable data capture device; locating within the enhancement package a media presentation associated with one or more identified words within the another captured portion; and presenting the associated media presentation using a display or speaker of the portable data capture device. 2. The method of claim 1 , wherein the media presentation comprises an audio presentation.

2. The apparatus of claim 1 , the translatable content component operative to extract the translatable content from the original document, the translatable content component to identify one or more paragraphs in the original document, extract text from the one or more paragraphs, generate one or more style identifiers for the extracted text, identify one or more runs of text.

2. The apparatus of claim 1 , the translatable content component operative to extract the translatable content from the original document, the translatable content component to identify one or more paragraphs in the original document, extract text from the one or more paragraphs, generate one or more style identifiers for the extracted text, identify one or more runs of text. 3. The apparatus of claim 2 , the intermediate component operative to create the plurality of intermediate documents from the extracted translatable content, the intermediate component to create paragraphs tags for each identified paragraph, identify a predominant style identifier for each paragraph, associate each paragraph with its predominant style identifier, identify off-style runs in each paragraph, create style tags for each off-style run, and create annotation tags from the annotation identifiers.

1. A system comprising: a processor; a memory; one or more designers and a visualizer, the one or more designers and the visualizer configured to visually formulate application package designs that customize the appearance and layout of corresponding application packages; a package generator for generating application packages from application package designs, generated application packages being in a format expected by product deployment software; and one or more computer readable storage devices having stored thereon computer executable instructions that, when executed by the processor, cause the system to: customize an application package design, including: present the structure of an application package through an arrangement of user-interface elements in a view of the visualizer, the user-interface elements corresponding to application package element references, the application package element references referencing application package elements; receive user input within the visualizer view; in response to the user input: visually alter one or more user-interface elements in the arrangement of user-interface elements; and alter the structure of the application package by the one or more designers altering application package element references corresponding the visually altered one or more user-interface elements; and generate a customized application package from the customized application design, generation of the customized application package including: create an application package element manifest by traversing the altered structure of application package in accordance with the application package element references to identify application package elements that are to be included in the customized application package; transform the application package element manifest into one or more manifest files in a format that is compatible with a packaging schema for the product deployment software by mapping between types and properties in an object model and elements and attributes in the packaging schema; preview the customized application package on disk by creating, from the one or more manifest files, a directory hierarchy comprising the identified application package elements and placing the identified application package elements in locations relative to the directory hierarchy on at least one server computer of the plurality of server computers; and use the directory hierarchy to bundle the identified application package elements into one file for deploying the collaborative server application across the plurality of server computers, the one file in a format expected by the product deployment software.

1. A system comprising: a processor; a memory; one or more designers and a visualizer, the one or more designers and the visualizer configured to visually formulate application package designs that customize the appearance and layout of corresponding application packages; a package generator for generating application packages from application package designs, generated application packages being in a format expected by product deployment software; and one or more computer readable storage devices having stored thereon computer executable instructions that, when executed by the processor, cause the system to: customize an application package design, including: present the structure of an application package through an arrangement of user-interface elements in a view of the visualizer, the user-interface elements corresponding to application package element references, the application package element references referencing application package elements; receive user input within the visualizer view; in response to the user input: visually alter one or more user-interface elements in the arrangement of user-interface elements; and alter the structure of the application package by the one or more designers altering application package element references corresponding the visually altered one or more user-interface elements; and generate a customized application package from the customized application design, generation of the customized application package including: create an application package element manifest by traversing the altered structure of application package in accordance with the application package element references to identify application package elements that are to be included in the customized application package; transform the application package element manifest into one or more manifest files in a format that is compatible with a packaging schema for the product deployment software by mapping between types and properties in an object model and elements and attributes in the packaging schema; preview the customized application package on disk by creating, from the one or more manifest files, a directory hierarchy comprising the identified application package elements and placing the identified application package elements in locations relative to the directory hierarchy on at least one server computer of the plurality of server computers; and use the directory hierarchy to bundle the identified application package elements into one file for deploying the collaborative server application across the plurality of server computers, the one file in a format expected by the product deployment software. 4. The system of claim 1 , wherein the customized application package comprises a collaborative server application comprising a Web-based collaboration function, a process management module, a search module and a content management platform.

14. The method of claim 11 further comprising: generating interactive content corresponding to the one or more language tags; and providing the interactive content to the user device.

14. The method of claim 11 further comprising: generating interactive content corresponding to the one or more language tags; and providing the interactive content to the user device. 15. The method of claim 14 , wherein the interactive content is generated based on a user language skill level.

8. The medium of claim 6 , wherein the information, when read by the computer, causes the computer to further perform the following: scheduling for human post-editing one or more of the machine translated translatable components.

8. The medium of claim 6 , wherein the information, when read by the computer, causes the computer to further perform the following: scheduling for human post-editing one or more of the machine translated translatable components. 9. The medium of claim 8 , wherein the information, when read by the computer, causes the computer to further perform the following: storing a corresponding human post-edited translated component in the second language for each translatable component that has been human post-edited.

11. An apparatus comprising a memory and a processor that executes computer executable instructions stored in the memory to cause the processor to: extract description information of multiple products; cluster the description information of the multiple products belonging to a particular model into a first text; process the first text by segmenting the first text to one of remove from the first text one or more terms whose term frequencies are higher than a first set threshold, and remove from the first text one or more terms whose term frequencies are lower than a second set threshold; cluster, after the first text is processed, first texts of products belonging to different models into a second text; apply a subject analysis to the second text to obtain one or more subjects and defines one or more names for the one or more subjects, respectively; and assign a respective name of a respective subject correlated to description information of a respective product as an identifier of the respective product and label the respective product by using the identifier, wherein the computer executable instructions stored in the memory cause the processor to: set a number of subjects in one or more subject models; apply the subject analysis to the second text by using a text analysis method based on the one or more subject models; obtain a number of subsets corresponding to the number of subjects from a set of terms included in the second text, the number of subsets being equal to the number of subjects, a respective subset corresponding to a respective subject; and according to the respective subset that one or more terms in the description information of the products locate, correlate the description information of the products to the respective subject corresponding to the respective subset.

11. An apparatus comprising a memory and a processor that executes computer executable instructions stored in the memory to cause the processor to: extract description information of multiple products; cluster the description information of the multiple products belonging to a particular model into a first text; process the first text by segmenting the first text to one of remove from the first text one or more terms whose term frequencies are higher than a first set threshold, and remove from the first text one or more terms whose term frequencies are lower than a second set threshold; cluster, after the first text is processed, first texts of products belonging to different models into a second text; apply a subject analysis to the second text to obtain one or more subjects and defines one or more names for the one or more subjects, respectively; and assign a respective name of a respective subject correlated to description information of a respective product as an identifier of the respective product and label the respective product by using the identifier, wherein the computer executable instructions stored in the memory cause the processor to: set a number of subjects in one or more subject models; apply the subject analysis to the second text by using a text analysis method based on the one or more subject models; obtain a number of subsets corresponding to the number of subjects from a set of terms included in the second text, the number of subsets being equal to the number of subjects, a respective subset corresponding to a respective subject; and according to the respective subset that one or more terms in the description information of the products locate, correlate the description information of the products to the respective subject corresponding to the respective subset. 12. The apparatus as recited in claim 11 , wherein the description information of the multiple products comprises title information, and the computer executable instructions stored in the memory cause the processor to: determine whether the title information of the respective product includes an additional identifier in an additional identifier database pre-established for a particular category that the respective product belongs; and in response to a result of determining that is positive, obtain the additional identifier and uses the additional identifier in addition to the identifier to label the respective product.

17. An electronic system for identifying and resolving ambiguities in the selection of single character or two-character symbolic language words, comprising: a keyboard having a plurality of key indicia corresponding to selected features of graphic characters and adapted to produce an identifier representing a character to be typed; file means containing a first list of characters and a second list of permitted character pairings, said characters and pairings being listed in said file by index codes selectable by specified identifiers, whereby a selected identifier will call up the index codes and pairings of all characters having that identifier; first storage means for receiving the index codes and permitted pairings for the identifier of a first character to be typed; second storage means for receiving the index codes for the identifier of a second character in a two-character word to be typed; a matching network for matching the index codes stored in said first storage means with the index codes in said second storage means to produce a list of possible character pairs for a two-character word; selection storage means; means for connecting said selection storage means either to said first storage means to receive and store only the index codes in said first storage means for a single character or to said matching network to receive said list of possible pairs for a two-character word; a comparator connected to said selection storage means for comparing said list of permitted character pairings with said list of possible pairs; significant pair storage means to receive and store character pairs appearing in both said list of permitted pairings and said list of possible pairs; and selector means connected either to said selection storage means to resolve single character ambiguities or to said significant pair storage means to resolve two-character word ambiguities.

17. An electronic system for identifying and resolving ambiguities in the selection of single character or two-character symbolic language words, comprising: a keyboard having a plurality of key indicia corresponding to selected features of graphic characters and adapted to produce an identifier representing a character to be typed; file means containing a first list of characters and a second list of permitted character pairings, said characters and pairings being listed in said file by index codes selectable by specified identifiers, whereby a selected identifier will call up the index codes and pairings of all characters having that identifier; first storage means for receiving the index codes and permitted pairings for the identifier of a first character to be typed; second storage means for receiving the index codes for the identifier of a second character in a two-character word to be typed; a matching network for matching the index codes stored in said first storage means with the index codes in said second storage means to produce a list of possible character pairs for a two-character word; selection storage means; means for connecting said selection storage means either to said first storage means to receive and store only the index codes in said first storage means for a single character or to said matching network to receive said list of possible pairs for a two-character word; a comparator connected to said selection storage means for comparing said list of permitted character pairings with said list of possible pairs; significant pair storage means to receive and store character pairs appearing in both said list of permitted pairings and said list of possible pairs; and selector means connected either to said selection storage means to resolve single character ambiguities or to said significant pair storage means to resolve two-character word ambiguities. 23. The apparatus of claim 17, wherein said selected features of graphic characters are peripheral stroke configurations thereof.

5. The method of claim 1 , further comprising generating optimized evaluation code, using a matching evaluation template, for the expression of the particular type.

5. The method of claim 1 , further comprising generating optimized evaluation code, using a matching evaluation template, for the expression of the particular type. 12. The system of claim 5 , wherein the optimized evaluation code is generated by inserting a parameter value, from the expression and identified by the parsing, into the matching evaluation template.

1. A method comprising: instantiating a gesture object for an application to handle gesture recognition for the application through native gesture functionality provided by a computing device; associating the gesture object with interaction inputs and a target element specified by the application such that the interaction inputs directed to the target element are offloaded to the gesture object configured for the application, the target element representing a selectable element rendered by the computing device; creating a recognizer on behalf of the application to facilitate gesture recognition through the native gesture functionality provided by the computing device; feeding interaction input data for the interaction inputs to the recognizer to enable recognition of gestures corresponding to the application based on the interaction input data; obtaining gesture event messages from the recognizer that are indicative of recognized gestures for the application; processing raw gesture data described by the gesture event messages on behalf of the application using the gesture object; and firing gesture events having processed gesture data to the associated target element in accordance with a content model for the application such that the recognized gestures conveyed via the gesture event messages are applied to the target element.

1. A method comprising: instantiating a gesture object for an application to handle gesture recognition for the application through native gesture functionality provided by a computing device; associating the gesture object with interaction inputs and a target element specified by the application such that the interaction inputs directed to the target element are offloaded to the gesture object configured for the application, the target element representing a selectable element rendered by the computing device; creating a recognizer on behalf of the application to facilitate gesture recognition through the native gesture functionality provided by the computing device; feeding interaction input data for the interaction inputs to the recognizer to enable recognition of gestures corresponding to the application based on the interaction input data; obtaining gesture event messages from the recognizer that are indicative of recognized gestures for the application; processing raw gesture data described by the gesture event messages on behalf of the application using the gesture object; and firing gesture events having processed gesture data to the associated target element in accordance with a content model for the application such that the recognized gestures conveyed via the gesture event messages are applied to the target element. 5. A method as described in claim 1 , wherein the interaction inputs comprise touch contacts applied to a touchscreen associated with the computing device.

12. The method of claim 11, wherein said third display means further comprises means for displaying each icon for said plurality of parameters in a tree structure, wherein each icon in said tree structure has a graphic connection to said icon representing said object.

12. The method of claim 11, wherein said third display means further comprises means for displaying each icon for said plurality of parameters in a tree structure, wherein each icon in said tree structure has a graphic connection to said icon representing said object. 16. The method of claim 12, wherein said third display means comprises means for displaying a constant as a selection for said parameter within said list of selections, wherein selection of said constant by a user allows said user to enter a number for said parameter.

1. A method comprising: receiving data, wherein the data is organized as a plurality of named fields, wherein each named field includes a set of values associated with the named field, wherein each named field is assigned to a category from a plurality of categories and wherein each set of values includes two or more entries; determining, for at least one category, whether there is at least one identifier field for that category, wherein each identifier field is a named field that acts as an identifier for that category; and selecting a concept, wherein selecting the concept includes: determining whether one of the categories includes an identifier field that has a unique value for each entry in the identifier field set of values and, if so, selecting the identifier field as the concept; if none of the categories include an identifier field that has a unique value for each entry in the identifier field set of values, determining whether one of the categories includes two or more identifier fields that, when combined, have a unique value for each entry in the combined identifier field set of values and, if so, selecting the combined identifier fields as the concept; and if none of the categories include an identifier field that has a unique value for each entry in the identifier field set of values and if none of the categories include two or more identifier fields that, when combined, have a unique value for each entry in the combined identifier field set of values, adding a new identifier field, wherein adding the new identifier field includes providing a unique value for each entry in set of values included in the new identifier field, associating the new identifier field with one of the categories, and selecting the new identifier field as the concept.

1. A method comprising: receiving data, wherein the data is organized as a plurality of named fields, wherein each named field includes a set of values associated with the named field, wherein each named field is assigned to a category from a plurality of categories and wherein each set of values includes two or more entries; determining, for at least one category, whether there is at least one identifier field for that category, wherein each identifier field is a named field that acts as an identifier for that category; and selecting a concept, wherein selecting the concept includes: determining whether one of the categories includes an identifier field that has a unique value for each entry in the identifier field set of values and, if so, selecting the identifier field as the concept; if none of the categories include an identifier field that has a unique value for each entry in the identifier field set of values, determining whether one of the categories includes two or more identifier fields that, when combined, have a unique value for each entry in the combined identifier field set of values and, if so, selecting the combined identifier fields as the concept; and if none of the categories include an identifier field that has a unique value for each entry in the identifier field set of values and if none of the categories include two or more identifier fields that, when combined, have a unique value for each entry in the combined identifier field set of values, adding a new identifier field, wherein adding the new identifier field includes providing a unique value for each entry in set of values included in the new identifier field, associating the new identifier field with one of the categories, and selecting the new identifier field as the concept. 5. The method of claim 1 , wherein at least one of the categories includes a category that includes two or more identifier fields that, when combined, have a unique value across the two or more identifier fields for each entry in the set of values.

15. The search engine of claim 1 , wherein the index formed by the Pre-Search component is located remote from the RealTime Search component, and wherein the RunTime Search component comprises: a Cacher module that operates to locally store a copy of the remote index for use by the RealTime Search component, wherein the Cacher module is local with the RealTime Search component.

15. The search engine of claim 1 , wherein the index formed by the Pre-Search component is located remote from the RealTime Search component, and wherein the RunTime Search component comprises: a Cacher module that operates to locally store a copy of the remote index for use by the RealTime Search component, wherein the Cacher module is local with the RealTime Search component. 16. The search engine of claim 15 , wherein the Cacher module operates to download a copy of the index to a memory that is local to the RealTime Search component when invoked by the RealTime Search component.

4. A computer-implemented method, the method comprising: obtaining, in a server device, a plurality of first search results responsive to a first search query, the plurality of first search results being ranked in an order; generating a second search query by adding a term to the first search query, the second search query being different from the first search query; obtaining, in the server device, a plurality of second search results responsive to the second search query; determining that a particular first search result responsive to the first search query appears within a threshold number of highest-ranked second search results responsive to the second search query; determining that the particular first search result in the first search results responsive to the first search query is not within a threshold number of highest-ranked first search results of the first search results responsive to the first search query; in response to determining that the particular first search result responsive to the first search query appears within the threshold number of the highest-ranked second search results responsive to the second search query and that the particular first search result in the first search results responsive to the first search query is not within a threshold number of highest-ranked first search results of the first search results responsive to the first search query, modifying the order of the plurality of first search results by moving the particular first search result within the plurality of first search results from an original position in the order to a second, different position in the order, wherein the second position is higher than the original position; and providing the modified plurality of first search results in response to the first search query.

4. A computer-implemented method, the method comprising: obtaining, in a server device, a plurality of first search results responsive to a first search query, the plurality of first search results being ranked in an order; generating a second search query by adding a term to the first search query, the second search query being different from the first search query; obtaining, in the server device, a plurality of second search results responsive to the second search query; determining that a particular first search result responsive to the first search query appears within a threshold number of highest-ranked second search results responsive to the second search query; determining that the particular first search result in the first search results responsive to the first search query is not within a threshold number of highest-ranked first search results of the first search results responsive to the first search query; in response to determining that the particular first search result responsive to the first search query appears within the threshold number of the highest-ranked second search results responsive to the second search query and that the particular first search result in the first search results responsive to the first search query is not within a threshold number of highest-ranked first search results of the first search results responsive to the first search query, modifying the order of the plurality of first search results by moving the particular first search result within the plurality of first search results from an original position in the order to a second, different position in the order, wherein the second position is higher than the original position; and providing the modified plurality of first search results in response to the first search query. 7. The method of claim 4 , wherein the second search query is related temporally to the first search query.

1. A method performed by one or more computers, the method comprising: receiving, by the one or more computers, data indicating a candidate transcription for an utterance and a particular context for the utterance; obtaining, by the one or more computers, a maximum entropy language model that includes (i) scores for one or more n-gram features that each correspond to a respective n-gram and (ii) scores for one or more backoff features that each correspond to a set of n-grams for which there are no corresponding n-gram features in the maximum entropy language model; determining, by the one or more computers, based on the candidate transcription and the particular context, a feature value for (i) each of the one or more n-gram features of the maximum entropy language model and (ii) each of the one or more backoff features of the maximum entropy language model; inputting, by the one or more computers, the feature values for the n-gram features and the feature values for the backoff features to the maximum entropy language model; and receiving, by the one or more computers, from the maximum entropy language model, an output indicative of a likelihood of occurrence of the candidate transcription; selecting, by the one or more computers, based on the output of the maximum entropy language model, a transcription for the utterance from among a plurality of candidate transcriptions; and providing, by the one or more computers, the selected transcription to a client device.

1. A method performed by one or more computers, the method comprising: receiving, by the one or more computers, data indicating a candidate transcription for an utterance and a particular context for the utterance; obtaining, by the one or more computers, a maximum entropy language model that includes (i) scores for one or more n-gram features that each correspond to a respective n-gram and (ii) scores for one or more backoff features that each correspond to a set of n-grams for which there are no corresponding n-gram features in the maximum entropy language model; determining, by the one or more computers, based on the candidate transcription and the particular context, a feature value for (i) each of the one or more n-gram features of the maximum entropy language model and (ii) each of the one or more backoff features of the maximum entropy language model; inputting, by the one or more computers, the feature values for the n-gram features and the feature values for the backoff features to the maximum entropy language model; and receiving, by the one or more computers, from the maximum entropy language model, an output indicative of a likelihood of occurrence of the candidate transcription; selecting, by the one or more computers, based on the output of the maximum entropy language model, a transcription for the utterance from among a plurality of candidate transcriptions; and providing, by the one or more computers, the selected transcription to a client device. 4. The method of claim 1 , wherein the scores of the maximum entropy language model comprise, for each context in a set of different contexts that each comprise a different sequence of one or more words, scores for: multiple different n-gram features that each correspond to the occurrence of a respective word with the context, the respective words forming a set of words, and a backoff feature that corresponds to the occurrence, with the context, of any word that is not in the set of words.

1. A device configured to: receive a human-perceptible input of a nearby circumstance, wherein the human-perceptible input includes a non-text visual input; configure a wireless signal of the human-perceptible input; transmit the wireless signal to a distal computing service for a distal computed translation of the nearby circumstance and output of the distal translation; receive the translation wirelessly; express the translation in a human-perceptible output, wherein the human-perceptible output includes an audible output translation of the non-text visual input; wherein the device is configured to express the translation through another device that is intermittently local to the device.

1. A device configured to: receive a human-perceptible input of a nearby circumstance, wherein the human-perceptible input includes a non-text visual input; configure a wireless signal of the human-perceptible input; transmit the wireless signal to a distal computing service for a distal computed translation of the nearby circumstance and output of the distal translation; receive the translation wirelessly; express the translation in a human-perceptible output, wherein the human-perceptible output includes an audible output translation of the non-text visual input; wherein the device is configured to express the translation through another device that is intermittently local to the device. 11. The device of claim 1 , wherein the device is configured to transmit the wireless signal directly to the distal computing service.

1. A computer-implemented method comprising: classifying a received phrase as an incomplete phrase for performing a voice action based at least on determining that (i) the voice action requires a parameter, and (ii) that no term of the phrase corresponds to the parameter; in response to classifying the phrase as an incomplete phrase, generating a prompt for entry of the parameter; in response to the prompt, receiving data indicating an entered parameter; and providing, for output, a suggested complete phrase for performing the voice action using the entered parameter, the suggested complete phrase comprising a phrase that when subsequently received causes the voice action to be performed without prompting for the entry of the parameter after determining that the suggested complete phrase includes one or more terms that correspond to the voice action.

1. A computer-implemented method comprising: classifying a received phrase as an incomplete phrase for performing a voice action based at least on determining that (i) the voice action requires a parameter, and (ii) that no term of the phrase corresponds to the parameter; in response to classifying the phrase as an incomplete phrase, generating a prompt for entry of the parameter; in response to the prompt, receiving data indicating an entered parameter; and providing, for output, a suggested complete phrase for performing the voice action using the entered parameter, the suggested complete phrase comprising a phrase that when subsequently received causes the voice action to be performed without prompting for the entry of the parameter after determining that the suggested complete phrase includes one or more terms that correspond to the voice action. 3. The method of claim 1 , wherein generating a prompt for entry of the parameter comprises at least one of: generating a user interface for display that includes fields for entry of the parameter; and synthesizing speech that requests the user speak the parameter.

1. A method for globalizing handling of service management items, comprising the steps of: obtaining a service management item in a language convenient to a first of two or more actors; translating the service management item into a language-neutral format to obtain a language-neutral service management item; applying one or more annotators to the service management item, wherein the one or more annotators comprise one or more personalized annotators; translating the language-neutral service management item into a language convenient to a second of two or more actors acting on the service management item; and routing the translated service management item to the second of two or more actors, wherein one or more of said steps are performed by a hardware device.

1. A method for globalizing handling of service management items, comprising the steps of: obtaining a service management item in a language convenient to a first of two or more actors; translating the service management item into a language-neutral format to obtain a language-neutral service management item; applying one or more annotators to the service management item, wherein the one or more annotators comprise one or more personalized annotators; translating the language-neutral service management item into a language convenient to a second of two or more actors acting on the service management item; and routing the translated service management item to the second of two or more actors, wherein one or more of said steps are performed by a hardware device. 11. The method of claim 1 , wherein the step of translating the service management item into a language-neutral format comprises the first of two or more actors undertaking at least one of editing, correcting and deleting one or more of the one or more applied annotators.

2. The speech recognition system of claim 1 , wherein the model selector comprises: a device key verification part configured to register the device key to a device key tree which is a component of the multi-model tree when the device key is a new device key, or receive an available acoustic model list of the device key tree from the multi-model tree when the device key is not a new device key; and a model selecting part configured to select one acoustic model or multi acoustic models to be used for parallel recognition from the available acoustic model list, and transmit address and information of the device key of the selection to the speech recognizer.

2. The speech recognition system of claim 1 , wherein the model selector comprises: a device key verification part configured to register the device key to a device key tree which is a component of the multi-model tree when the device key is a new device key, or receive an available acoustic model list of the device key tree from the multi-model tree when the device key is not a new device key; and a model selecting part configured to select one acoustic model or multi acoustic models to be used for parallel recognition from the available acoustic model list, and transmit address and information of the device key of the selection to the speech recognizer. 3. The speech recognition system of claim 2 , wherein the available acoustic model list comprises, in a sequential order, a device independent model, a device dependent model, a detail device dependent model and a random ID dependent model included in the multi-model tree.

1. A computer-implemented method comprising: receiving, from a requesting computing device, a request for a resource of a requested website; using a server computer, determining a plurality of context values associated with the request, including a particular context value identifying information associated with a referral website that is different from the requested website; using the server computer, selecting a plurality of rules, wherein each of the plurality of rules specifies an expected context value, a website resource, and a rank, wherein the plurality of context values contains the expected context value; using the server computer, selecting a particular website resource from a plurality of website resources, wherein the particular website resource is the website resource of a rule having a highest rank of the plurality of rules; causing the particular website resource to be delivered to the requesting computing device.

1. A computer-implemented method comprising: receiving, from a requesting computing device, a request for a resource of a requested website; using a server computer, determining a plurality of context values associated with the request, including a particular context value identifying information associated with a referral website that is different from the requested website; using the server computer, selecting a plurality of rules, wherein each of the plurality of rules specifies an expected context value, a website resource, and a rank, wherein the plurality of context values contains the expected context value; using the server computer, selecting a particular website resource from a plurality of website resources, wherein the particular website resource is the website resource of a rule having a highest rank of the plurality of rules; causing the particular website resource to be delivered to the requesting computing device. 5. The computer-implemented method of claim 1 , wherein the particular context value identifies the referral website and is included in a REFERER field of an HTTP header of the request.

12. A computer readable medium containing a program which, when executed, performs operations for managing execution of a query against a database having a multiplicity of data records, the operations comprising: receiving, from a requesting entity, a query against the database; and performing an automated execution process, comprising: (i) iteratively executing the query against different samples of the database, each sample including a different subset of the multiplicity of data records; (ii) after each iterative execution of the query, determining whether a query result obtained for the iterative execution satisfies a predefined condition; and (iii) if the predefined condition is not satisfied, performing a predefined action.

12. A computer readable medium containing a program which, when executed, performs operations for managing execution of a query against a database having a multiplicity of data records, the operations comprising: receiving, from a requesting entity, a query against the database; and performing an automated execution process, comprising: (i) iteratively executing the query against different samples of the database, each sample including a different subset of the multiplicity of data records; (ii) after each iterative execution of the query, determining whether a query result obtained for the iterative execution satisfies a predefined condition; and (iii) if the predefined condition is not satisfied, performing a predefined action. 13. The computer readable medium of claim 12 , wherein performing the automated execution process further comprises, prior to each iterative execution of the query: selecting a plurality of data records from the multiplicity of data records, the plurality of data records defining a particular sample including a particular subset of the multiplicity of data records.

18. A kiosk configured to perform a computer-implemented method, comprising: determining at least one of a first language or a second language based at least in part on at least one of a selection by a user or a logic for automatically determining a language; receiving visual input of a target scene, the visual input representative of a captured image of the target scene, the visual input originating from a printed picture that is scanned; analyzing, using neural network based optical character recognition, the visual input to identify one or more locations within the visual input that comprise a textual element associated with the first language; translating the textual element into a translated textual element associated with the second language based at least in part on a first contextual hint determined based at least in part on an audio stream; and translating the audio stream into a transcribed audio segment based at least in part on: hidden Markov model based speech synthesis; one or more pauses comprised within the audio stream; a sentence structure associated with at least some of the audio stream; a number of syllables of the audio stream; and a second contextual hint determined based at least in part on the visual input.

18. A kiosk configured to perform a computer-implemented method, comprising: determining at least one of a first language or a second language based at least in part on at least one of a selection by a user or a logic for automatically determining a language; receiving visual input of a target scene, the visual input representative of a captured image of the target scene, the visual input originating from a printed picture that is scanned; analyzing, using neural network based optical character recognition, the visual input to identify one or more locations within the visual input that comprise a textual element associated with the first language; translating the textual element into a translated textual element associated with the second language based at least in part on a first contextual hint determined based at least in part on an audio stream; and translating the audio stream into a transcribed audio segment based at least in part on: hidden Markov model based speech synthesis; one or more pauses comprised within the audio stream; a sentence structure associated with at least some of the audio stream; a number of syllables of the audio stream; and a second contextual hint determined based at least in part on the visual input. 19. The kiosk of claim 18 , the method comprising scanning, by the kiosk, the digital picture.

11. A computer-implemented method comprising: selecting one or more business terms, wherein each of the one or more selected business terms corresponds to one or more standardized business definitions; identifying one or more data structures utilized by a first enforcement system that are equivalent to the one or more selected business terms; identifying one of the one or more native rules utilized by the first enforcement system that includes at least one of the one or more equivalent data structures, the identified native rule written in a first enforcement system-specific format; and creating a mapping entry that maps the identified native rule to a selected one of the canonical rules, wherein the selected canonical rule includes one or more of the one more selected business terms, and wherein the selected canonical rule is written in a canonical format that is independent from the first enforcement system-specific format.

11. A computer-implemented method comprising: selecting one or more business terms, wherein each of the one or more selected business terms corresponds to one or more standardized business definitions; identifying one or more data structures utilized by a first enforcement system that are equivalent to the one or more selected business terms; identifying one of the one or more native rules utilized by the first enforcement system that includes at least one of the one or more equivalent data structures, the identified native rule written in a first enforcement system-specific format; and creating a mapping entry that maps the identified native rule to a selected one of the canonical rules, wherein the selected canonical rule includes one or more of the one more selected business terms, and wherein the selected canonical rule is written in a canonical format that is independent from the first enforcement system-specific format. 13. The method of claim 11 further comprising: verifying that the identified native rule is unique compared to the one or more canonical rules; and in response to verifying the identified native rule is unique, creating the selected one of the canonical rules by translating the identified native rule into the selected one of the canonical rules.

8. A processing system for conducting a stakeholder relationship analysis for an organisation, the processing system comprising: a computer processing system; and a computer readable medium in communication with the computer processing system, wherein the computer readable medium when used by the computer processing system causes the computer processing system to: select a stakeholder model from a plurality of models stored in memory, the stakeholder model defining: a plurality of latent variables including: one or more experiential latent variables; one or more attitudinal latent variables; and one or more behavioural intention latent variables; record survey response data indicative of a plurality of responses to a survey conducted in relation to the organisation; identify, based upon the stakeholder model and the survey response data, one or more driver latent variables impacting upon the one or more behavioural intention latent variables; generate, using the stakeholder model and the one or more driver latent variables, impact data indicative of a predicted impact upon the organisation due to one or more modifications to at least some of the one or more driver latent variables; and generate, based on the impact data, a report indicative of one or more proposed modifications to the organisation associated with the one or more modified driver latent variables, to thereby improve the stakeholder relationship for the organisation.

8. A processing system for conducting a stakeholder relationship analysis for an organisation, the processing system comprising: a computer processing system; and a computer readable medium in communication with the computer processing system, wherein the computer readable medium when used by the computer processing system causes the computer processing system to: select a stakeholder model from a plurality of models stored in memory, the stakeholder model defining: a plurality of latent variables including: one or more experiential latent variables; one or more attitudinal latent variables; and one or more behavioural intention latent variables; record survey response data indicative of a plurality of responses to a survey conducted in relation to the organisation; identify, based upon the stakeholder model and the survey response data, one or more driver latent variables impacting upon the one or more behavioural intention latent variables; generate, using the stakeholder model and the one or more driver latent variables, impact data indicative of a predicted impact upon the organisation due to one or more modifications to at least some of the one or more driver latent variables; and generate, based on the impact data, a report indicative of one or more proposed modifications to the organisation associated with the one or more modified driver latent variables, to thereby improve the stakeholder relationship for the organisation. 14. The processing system according to claim 8 , wherein the processing system is configured to analyse the survey data by: performing a correlation analysis to determine a strength and direction of one or more linear relationships between at least some of the latent variables; and performing a multiple regression analysis of the one or more linear relationships to identify the one or more driver latent variables.

1. A method for communicating visual images to a handicapped person, said method comprising the steps of: providing at least one device for physically transmitting information to said handicapped person; providing information about said visual images to said handicapped person using said at least one device; and said information providing step comprising delivering a physical signal representative of a key word describing a portion of a visual image to a first part of a body of said handicapped person using said at least one device and further comprising transmitting at least one physical input describing a dynamic element associated with said key word to a second part of the body of said handicapped person; and wherein Dividing the fingers of a hand of said handicapped person into a first group consisting of a pointer finger and a middle finger and into a second group consisting of a ring finger and a pinky and said transmitting step comprises transmitting information about a bad character to one of said fingers of said first group and transmitting information about a good character to one of said fingers of said second group.

1. A method for communicating visual images to a handicapped person, said method comprising the steps of: providing at least one device for physically transmitting information to said handicapped person; providing information about said visual images to said handicapped person using said at least one device; and said information providing step comprising delivering a physical signal representative of a key word describing a portion of a visual image to a first part of a body of said handicapped person using said at least one device and further comprising transmitting at least one physical input describing a dynamic element associated with said key word to a second part of the body of said handicapped person; and wherein Dividing the fingers of a hand of said handicapped person into a first group consisting of a pointer finger and a middle finger and into a second group consisting of a ring finger and a pinky and said transmitting step comprises transmitting information about a bad character to one of said fingers of said first group and transmitting information about a good character to one of said fingers of said second group. 10. A method according to claim 1 , further comprising transmitting information about a start of and an end of a commercial advertisement to said handicapped person.

1. A question answering system comprising: a question input unit that receives an input question; a search unit that executes a first search processing on a basis of the input question; an answer candidate extraction unit that extracts an initial answer candidate on a basis of a result of the first search processing executed by the search unit; an answer candidate inspection unit that inspects the initial answer candidate extracted by the answer candidate extraction unit; and an answer output unit that outputs the initial answer candidate selected by the answer candidate inspection unit, as an answer of the input question, wherein: the answer candidate inspection unit generates one or more queries and executes a second search processing using the queries, each of the generated queries being a character string pattern relative to the input question and being generated based on one or more keywords included in the input question and one of the initial answer candidates extracted by the answer candidate extraction unit, the answer candidate inspection unit determines whether or not a composed word of a sentence, which is obtained as a result of the second search processing executed by the answer candidate inspection unit, has a similar lexical meaning to a lexical meaning of a specific word of the input question, and the answer candidate inspection unit selects the initial answer candidate included in the query, which is used when searching the sentence including the composed word that has the similar lexical meaning, as the answer.

1. A question answering system comprising: a question input unit that receives an input question; a search unit that executes a first search processing on a basis of the input question; an answer candidate extraction unit that extracts an initial answer candidate on a basis of a result of the first search processing executed by the search unit; an answer candidate inspection unit that inspects the initial answer candidate extracted by the answer candidate extraction unit; and an answer output unit that outputs the initial answer candidate selected by the answer candidate inspection unit, as an answer of the input question, wherein: the answer candidate inspection unit generates one or more queries and executes a second search processing using the queries, each of the generated queries being a character string pattern relative to the input question and being generated based on one or more keywords included in the input question and one of the initial answer candidates extracted by the answer candidate extraction unit, the answer candidate inspection unit determines whether or not a composed word of a sentence, which is obtained as a result of the second search processing executed by the answer candidate inspection unit, has a similar lexical meaning to a lexical meaning of a specific word of the input question, and the answer candidate inspection unit selects the initial answer candidate included in the query, which is used when searching the sentence including the composed word that has the similar lexical meaning, as the answer. 4. The question answering system according to claim 1 , further comprising: a question meaning analysis unit extracts a question focus and a modifier modifying the question focus from the input question, and analyzes a lexical meaning of the modifier as the lexical meaning of the specific word of the input question.

1. A method comprising: identifying a new media clip provided by a first user; providing a feature vector associated with the new media clip to a first classifier, wherein the feature vector associated with the new media clip includes a plurality of values representing one or more features extracted from content of the new media clip, wherein the first classifier was trained using a plurality of features vectors of a plurality of existing media clips to produce suggested semantic tags for respective existing media clips; obtaining, by a computer system, a first set of semantic tags for the new media clip from the first classifier; providing, by the computer system, the first set of semantic tags for the new media clip to a second classifier that was trained using input-output pairs, wherein an input of each input-output pair includes a subset of the suggested semantic tags that was previously produced by the first classifier and suggested to a second user for one of the respective existing clips, and wherein an output of the input-output pair includes one or more suggested semantic tags selected by the second user from the subset of the suggested semantic tags for the one of the respective existing clips; obtaining, by the computer system, a second set of semantic tags for the new media clip from the second classifier; and suggesting to the first user, by the computer system, the second set of semantic tags for the new media clip.

1. A method comprising: identifying a new media clip provided by a first user; providing a feature vector associated with the new media clip to a first classifier, wherein the feature vector associated with the new media clip includes a plurality of values representing one or more features extracted from content of the new media clip, wherein the first classifier was trained using a plurality of features vectors of a plurality of existing media clips to produce suggested semantic tags for respective existing media clips; obtaining, by a computer system, a first set of semantic tags for the new media clip from the first classifier; providing, by the computer system, the first set of semantic tags for the new media clip to a second classifier that was trained using input-output pairs, wherein an input of each input-output pair includes a subset of the suggested semantic tags that was previously produced by the first classifier and suggested to a second user for one of the respective existing clips, and wherein an output of the input-output pair includes one or more suggested semantic tags selected by the second user from the subset of the suggested semantic tags for the one of the respective existing clips; obtaining, by the computer system, a second set of semantic tags for the new media clip from the second classifier; and suggesting to the first user, by the computer system, the second set of semantic tags for the new media clip. 6. The method of claim 1 wherein the second set of semantic tags is obtained by applying a threshold to the output of the second classifier.

2. The method of claim 1 , further comprising: presenting a first text-based category in the text-based interface having various color selections for receiving a color to be modified in the document from the user; presenting a second text-based category in the text-based interface having selections for receiving a magnitude of the image modification or a resultant image modification; presenting a third text-based category in the text-based interface having selections different from the second text-based category for receiving the magnitude or the resultant image modification; receiving a selection respectively from the first, second and third text-based category; and compiling and presenting the human readable sentence in a window of the text-based interface representing the image modification while the modification is displayed in the image.

2. The method of claim 1 , further comprising: presenting a first text-based category in the text-based interface having various color selections for receiving a color to be modified in the document from the user; presenting a second text-based category in the text-based interface having selections for receiving a magnitude of the image modification or a resultant image modification; presenting a third text-based category in the text-based interface having selections different from the second text-based category for receiving the magnitude or the resultant image modification; receiving a selection respectively from the first, second and third text-based category; and compiling and presenting the human readable sentence in a window of the text-based interface representing the image modification while the modification is displayed in the image. 7. The method of claim 2 , wherein the first text-based category comprises a first portion of the human readable sentence, the second text-based category comprises a second portion of the human readable sentence, and the third text-based category comprises a third portion of the human readable sentence.

7. A system for implementing model definition, constraint enforcement, and validation, comprising: a client computing device that comprises at least a processor, a browser installed thereupon, and non-transitory memory allocated for the browser and is configured to at least: request a reference to one or more model definition resources for a representation of a model that pertains to a tax preparation or financial management software application at least by transmitting a request for the reference to a remote host computer hosting the tax preparation or financial management application via a network element; execute a model definition resolver, residing on and stored at least partially in memory of the client computing device, that identifies or determines one or more actual locations for the one or more model definition resources at least by resolving the reference and retrieving the one or more model definition resources from the one or more actual locations, wherein the one or more model definition resources specify constraints for constraint enforcement or validation on the model; reduce data processing and data to be populated into the model at least by removing a portion of the data and populating a remaining portion of the data into the model through using one or more validation modules and one or more formatting modules stored at least partially in the memory of the client computing device, wherein the client computing device reducing the data processing and the data is further configured to: disable the constraint enforcement or validation for a portion of a flow of the tax preparation or financial management application; reduce a total number of data elements and a total number of invalid data elements at least by formatting one or more user inputs for the tax preparation or financial management application at the one or more formatting modules; and perform the constraint enforcement or validation at the one or more validation modules on at least the one or more user inputs, which have been formatted, based in part or in whole upon at least the one or more model definition resources obtained by the model definition resolver residing on the client computing device from the remote host computer via the network element; and perform data binding for the data to bind the data to the model by using a first application programming interface located on the client computing device, wherein the client computing device that performs the data binding is further configured to: identify a model definition for a requested key of a key-value pair for the data and obtaining an actual implementation of the one or more model definition resources from the remote host computer by processing the reference with a mapping module; generate a first result at least by determining whether the requested key for the data is allowed in the model based at least in part upon the actual implementation of the one or more model definition resources and further generating a second result at least by determining whether a requested value of the key-value pair for the data matches the model definition; and perform the data binding for the data and the model based at least in part upon the first result and the second result.

7. A system for implementing model definition, constraint enforcement, and validation, comprising: a client computing device that comprises at least a processor, a browser installed thereupon, and non-transitory memory allocated for the browser and is configured to at least: request a reference to one or more model definition resources for a representation of a model that pertains to a tax preparation or financial management software application at least by transmitting a request for the reference to a remote host computer hosting the tax preparation or financial management application via a network element; execute a model definition resolver, residing on and stored at least partially in memory of the client computing device, that identifies or determines one or more actual locations for the one or more model definition resources at least by resolving the reference and retrieving the one or more model definition resources from the one or more actual locations, wherein the one or more model definition resources specify constraints for constraint enforcement or validation on the model; reduce data processing and data to be populated into the model at least by removing a portion of the data and populating a remaining portion of the data into the model through using one or more validation modules and one or more formatting modules stored at least partially in the memory of the client computing device, wherein the client computing device reducing the data processing and the data is further configured to: disable the constraint enforcement or validation for a portion of a flow of the tax preparation or financial management application; reduce a total number of data elements and a total number of invalid data elements at least by formatting one or more user inputs for the tax preparation or financial management application at the one or more formatting modules; and perform the constraint enforcement or validation at the one or more validation modules on at least the one or more user inputs, which have been formatted, based in part or in whole upon at least the one or more model definition resources obtained by the model definition resolver residing on the client computing device from the remote host computer via the network element; and perform data binding for the data to bind the data to the model by using a first application programming interface located on the client computing device, wherein the client computing device that performs the data binding is further configured to: identify a model definition for a requested key of a key-value pair for the data and obtaining an actual implementation of the one or more model definition resources from the remote host computer by processing the reference with a mapping module; generate a first result at least by determining whether the requested key for the data is allowed in the model based at least in part upon the actual implementation of the one or more model definition resources and further generating a second result at least by determining whether a requested value of the key-value pair for the data matches the model definition; and perform the data binding for the data and the model based at least in part upon the first result and the second result. 12. The system of claim 7 , wherein the client computing device is further configured to: populate the key-value pair for the data into a repository for the model.

4. A system to query a set of documents based on multiple relevance value types, the system comprising: one or more computer processors; a memory containing a program which, when executed by the one or more computer processors, performs an operation comprising: providing, for each document in the set, a summary representing the respective document, wherein providing the summary comprises, for each document, identifying content marked by hierarchical tags and generating the summary from the content marked by higher-order hierarchical tags; parsing the set of documents using a received set of search terms in order to calculate, for each document in the set, a first relevance value based on the summary of the respective document and not based on the respective document itself; parsing, from the set of documents and using the received set of search terms, only a subset of documents having the highest first relevance values in order to calculate, for each document in the subset, a second relevance value based on the respective document itself and not based on the summary of the respective document, wherein the subset excludes at least one document in the set and for which a second relevance value is not calculated, thereby avoiding a processing cost associated with calculating the second relevance value for the at least one document in the set; and providing query results comprising documents from the subset that have the highest second relevance values, regardless of the first relevance value of any document in the set, wherein the query results exclude the at least one document in the set and at least one document in the subset.

4. A system to query a set of documents based on multiple relevance value types, the system comprising: one or more computer processors; a memory containing a program which, when executed by the one or more computer processors, performs an operation comprising: providing, for each document in the set, a summary representing the respective document, wherein providing the summary comprises, for each document, identifying content marked by hierarchical tags and generating the summary from the content marked by higher-order hierarchical tags; parsing the set of documents using a received set of search terms in order to calculate, for each document in the set, a first relevance value based on the summary of the respective document and not based on the respective document itself; parsing, from the set of documents and using the received set of search terms, only a subset of documents having the highest first relevance values in order to calculate, for each document in the subset, a second relevance value based on the respective document itself and not based on the summary of the respective document, wherein the subset excludes at least one document in the set and for which a second relevance value is not calculated, thereby avoiding a processing cost associated with calculating the second relevance value for the at least one document in the set; and providing query results comprising documents from the subset that have the highest second relevance values, regardless of the first relevance value of any document in the set, wherein the query results exclude the at least one document in the set and at least one document in the subset. 5. The system of claim 4 , wherein the summary is based on a list of contents of the document.

41. The method for representing information in a computer system according to claim 40, further comprising the steps of: reading a descriptive network for an active concept in the knowledge representation database forming a network of related records through the fundamental relationships of parent and type, combining the relationship lists from said read records, and storing the said relationships from said read records in said descriptive database, said relationship lists being combined by applying inheritance rules comprised of Taxonomy, Type, Composition and User inheritance, each relationships comprising said relationship list being stored in said descriptive database together with the URN of said active concept and the URN of source record, wherein said each relationship is stored in the Knowledge Representation Database.

41. The method for representing information in a computer system according to claim 40, further comprising the steps of: reading a descriptive network for an active concept in the knowledge representation database forming a network of related records through the fundamental relationships of parent and type, combining the relationship lists from said read records, and storing the said relationships from said read records in said descriptive database, said relationship lists being combined by applying inheritance rules comprised of Taxonomy, Type, Composition and User inheritance, each relationships comprising said relationship list being stored in said descriptive database together with the URN of said active concept and the URN of source record, wherein said each relationship is stored in the Knowledge Representation Database. 45. The method for representing information in a computer system according to claim 41, wherein User inheritance rules define manners of combining the relationship lists in a series of records linked through the URNs stored as internal values in relationships stored in said records.

12. The system of claim 9 , wherein determining the candidate similarity score includes: determining that a text phrase on the first page matches a text phrase on the second page; and adding a first amount to the candidate similarity score based on the determination that the text phrase of the first page matches the text phrase on the second page.

12. The system of claim 9 , wherein determining the candidate similarity score includes: determining that a text phrase on the first page matches a text phrase on the second page; and adding a first amount to the candidate similarity score based on the determination that the text phrase of the first page matches the text phrase on the second page. 15. The system of claim 12 , wherein the text phrase on the first page is located in a header of the first page or a title of the first page.

3. The method of claim 1 , wherein the method further comprises generating fingerprints for the first set of passages, and wherein at least one fingerprint corresponds to a state of the HMM.

3. The method of claim 1 , wherein the method further comprises generating fingerprints for the first set of passages, and wherein at least one fingerprint corresponds to a state of the HMM. 8. The method of claim 3 , wherein the fingerprints of the first set of passages include two-dimensional visual fingerprints.

14. The method of claim 8 , wherein the memory is a database in a memory system of the search site, and rewriting is performed with the memory system.

14. The method of claim 8 , wherein the memory is a database in a memory system of the search site, and rewriting is performed with the memory system. 16. The method of claim 14 , wherein the steps of mapping and determining are performed by the first system of the search site.

12. The apparatus of claim 11 wherein said address modifying means operates only when the contents of said buffer means and a coded signal are identical.

12. The apparatus of claim 11 wherein said address modifying means operates only when the contents of said buffer means and a coded signal are identical. 14. The apparatus of claim 12 wherein said first memory means includes said first entry and said second entry corresponding to a coded signal from a key in said second set of keying devices and said second memory means includes an entry corresponding to said second entry of said first memory means with an initial sub-entry corresponding to a space function.

48. An apparatus for providing a called party with audio prompts in a language selected by a calling party, comprising: means for determining dialed digits that identify said called party; means for selecting a language previously determined by said calling party from a plurality of languages for delivery of audio prompts in response to determined dialed digits; and means for providing said called party with audio prompts in the selected language.

48. An apparatus for providing a called party with audio prompts in a language selected by a calling party, comprising: means for determining dialed digits that identify said called party; means for selecting a language previously determined by said calling party from a plurality of languages for delivery of audio prompts in response to determined dialed digits; and means for providing said called party with audio prompts in the selected language. 52. The apparatus of claim 48, further comprising means for enabling said calling party to select specific languages or dialects for delivery of audio prompts to specific cities.

6. A method comprising: receiving text data; determining a sequence of linguistic units corresponding to the text data, the sequence of linguistic units comprising a first linguistic unit and a second linguistic unit; generating a representation of the first linguistic unit using a model for the first linguistic unit and a first parametric speech synthesis technique, wherein the first parametric speech synthesis technique comprises synthesizing speech using a computerized voice generator; retrieving a pre-recorded speech unit for the second linguistic unit from a unit selection database, wherein the pre-recorded speech unit comprises recorded speech configured with acoustic properties consistent with speech generated by the first parametric speech synthesis technique; concatenating the representation of the first linguistic unit and the pre-recorded speech unit for the second linguistic unit to generate audio data; and causing audio corresponding to the audio data to be output using an audio speaker.

6. A method comprising: receiving text data; determining a sequence of linguistic units corresponding to the text data, the sequence of linguistic units comprising a first linguistic unit and a second linguistic unit; generating a representation of the first linguistic unit using a model for the first linguistic unit and a first parametric speech synthesis technique, wherein the first parametric speech synthesis technique comprises synthesizing speech using a computerized voice generator; retrieving a pre-recorded speech unit for the second linguistic unit from a unit selection database, wherein the pre-recorded speech unit comprises recorded speech configured with acoustic properties consistent with speech generated by the first parametric speech synthesis technique; concatenating the representation of the first linguistic unit and the pre-recorded speech unit for the second linguistic unit to generate audio data; and causing audio corresponding to the audio data to be output using an audio speaker. 8. The method of claim 6 , wherein the first linguistic unit corresponds to a first language and the second linguistic unit corresponds to a second language.

12. A method for provisioning cloud templates, comprising: storing cloud templates in a template library according to hierarchical categories comprising at least an industry type and an industrial control project type; receiving, by a cloud template provisioning system that includes a processor, a query from a client device specifying one or more categories of the hierarchical categories; selecting a subset of the cloud templates that correspond to the one or more categories; rendering, on the client device, identification information for the subset of cloud templates; in response to receiving selection data from the client device indicating a selected cloud template of the subset of cloud templates, delivering the selected cloud template to a memory location associated with the client device; and configuring a portion of a cloud-based industrial application based on one or more configuration instructions generated by the selected cloud template based on configuration data provided to the cloud template via the client device.

12. A method for provisioning cloud templates, comprising: storing cloud templates in a template library according to hierarchical categories comprising at least an industry type and an industrial control project type; receiving, by a cloud template provisioning system that includes a processor, a query from a client device specifying one or more categories of the hierarchical categories; selecting a subset of the cloud templates that correspond to the one or more categories; rendering, on the client device, identification information for the subset of cloud templates; in response to receiving selection data from the client device indicating a selected cloud template of the subset of cloud templates, delivering the selected cloud template to a memory location associated with the client device; and configuring a portion of a cloud-based industrial application based on one or more configuration instructions generated by the selected cloud template based on configuration data provided to the cloud template via the client device. 15. The method of claim 12 , wherein the delivering comprises delivering a data historian template, and the configuring comprises at least one of defining a device tag from which data is to be collected by a cloud-based data historian, defining a data collection frequency for the cloud-based data historian, or defining a hierarchical data model of an enterprise for use by the cloud-based data historian in connection with locating and storing industrial data.

1. A method to implant in a patient a dynamic spine stabilization, motion preservation system comprising the steps of: accessing the surgical site; implanting first and second anchor systems in a first vertebra; implanting third and fourth anchor systems in a second vertebra; positioning a first horizontal anchor system relative to the first and second anchor systems with a vertical rod system connected to the first horizontal anchor system; positioning a second horizontal anchor system relative to the third and fourth anchor systems; deploying vertical rods from a vertical rod system from a position about parallel to the first horizontal rod system to a position about perpendicular to the first horizontal rod system; and moving the vertical rods into engagement with mounts on the second horizontal anchor system.

1. A method to implant in a patient a dynamic spine stabilization, motion preservation system comprising the steps of: accessing the surgical site; implanting first and second anchor systems in a first vertebra; implanting third and fourth anchor systems in a second vertebra; positioning a first horizontal anchor system relative to the first and second anchor systems with a vertical rod system connected to the first horizontal anchor system; positioning a second horizontal anchor system relative to the third and fourth anchor systems; deploying vertical rods from a vertical rod system from a position about parallel to the first horizontal rod system to a position about perpendicular to the first horizontal rod system; and moving the vertical rods into engagement with mounts on the second horizontal anchor system. 10. The method of claim 1 including moving a saddle head of the anchor system about a spherical connection with a bone anchor until the desired position of the saddle head is reached with respect to the horizontal rod system and locking the saddle head against the horizontal rod system and locking the saddle head and the horizontal rod system against a spherical connector about which the saddle head moves about a bone anchor head.

1. A file search apparatus tat searches for a file based on one or more searched-keywords associated with object files and one or more searching-keywords selected by a user, the file search apparatus comprising: a display unit that displays an image; an installation module to which a storage medium storing object files is installed; a setting module that sets the searching-keywords according to an instruction by the user; an acquisition module that, when the searching-keywords are set by the setting module and the storage medium is installed to the installation module; acquires the searched-keywords associated with object files from the storage medium; a collation module that performs collation as to whether each of the searching-keywords set by the setting module matches any of the searched-keywords acquired by the acquisition module; a display control module that controls the display unit to display in a manner that results of the collation by the collation module is recognizable; an information storage module that stores icons in association with the respective searched-keywords; wherein the display control module reads an icon associated with a searched-keyword corresponding to each of the searching-keywords that are selected from the searched-keywords, from the information storage module, and displays the read icons as the searching-keywords.

1. A file search apparatus tat searches for a file based on one or more searched-keywords associated with object files and one or more searching-keywords selected by a user, the file search apparatus comprising: a display unit that displays an image; an installation module to which a storage medium storing object files is installed; a setting module that sets the searching-keywords according to an instruction by the user; an acquisition module that, when the searching-keywords are set by the setting module and the storage medium is installed to the installation module; acquires the searched-keywords associated with object files from the storage medium; a collation module that performs collation as to whether each of the searching-keywords set by the setting module matches any of the searched-keywords acquired by the acquisition module; a display control module that controls the display unit to display in a manner that results of the collation by the collation module is recognizable; an information storage module that stores icons in association with the respective searched-keywords; wherein the display control module reads an icon associated with a searched-keyword corresponding to each of the searching-keywords that are selected from the searched-keywords, from the information storage module, and displays the read icons as the searching-keywords. 2. The file search apparatus according to claim 1 , wherein the display control module controls the display unit to display the searching-keywords in a manner that the results of collation by the collation module is recognizable.

1. A method comprising: establishing multiple sessions with a database system, each session associated with at least one transaction; identifying transactions that operate on the same set of one or more tuples; re-allocating transactions between or among the sessions such that the identified transactions that operate on the same set of one or more tuples are allocated to one of the sessions; identifying statements in a particular one of the transactions that specify modification operations that are commutative and associative; combining the identified statements into one statement; and submitting the one statement to the database system.

1. A method comprising: establishing multiple sessions with a database system, each session associated with at least one transaction; identifying transactions that operate on the same set of one or more tuples; re-allocating transactions between or among the sessions such that the identified transactions that operate on the same set of one or more tuples are allocated to one of the sessions; identifying statements in a particular one of the transactions that specify modification operations that are commutative and associative; combining the identified statements into one statement; and submitting the one statement to the database system. 2. The method of claim 1 , wherein identifying the statements comprises identifying Structured Query Language (SQL) statements.

1. A processor implemented method for automated classification of business rules from text, the method comprising: identifying, via one or more hardware processors, a business rule from a text document, wherein the business rule comprises one or more rule intents, wherein one or more rule intent comprise at least one of keywords, parts of speech (POS) tags, and wildcards and said one or more rule intents are utilized to automatically identify the business rule from the text document; creating, via the one or more hardware processors, a rule repository based on an identified business rule and the one or more rule intents, wherein the rule intents are atomic constraints stored in an ontology form to automatically identify the business rule from the text document; comparing, via the one or more hardware processors, the one or more rule intents in the business rule with the one or more clusters associated with a plurality of rule types in the rule repository to compute a match score, wherein a match score is indicative of number of rule intents and based on the match score, the business rule is annotated with one or more rule types in the rule repository, and wherein the rule repository is periodically updated with a plurality of new rule intents, said rule intent patterns and new rule types; classifying, via the one or more hardware processors, the business rule under at least one rule type, amongst the plurality of rule types, to further classify the business rule into at least one of formatted and unformatted text documents for referring and tracing the text document by annotating the business rule with one or more corresponding rule intents and one or more rule types in the rule repository; clustering, via the one or more hardware processors, said plurality of new rule intents in an agglomerative manner to identify one or more clustering rule intents that co-occur frequently in the rule repository; assembling, via the one or more hardware processors, the one or more clusters into a plurality of clustering techniques, wherein the rule repository is periodically updated with the corresponding rule intent and the rule type to classify sentences obtained from a training dataset; and automatically associating, via the one or more hardware processors, the business rules extracted from the text document with a plurality of corresponding knowledge element types and system components to reference, trace and re-use at least one of requirement artifacts and system documentation.

1. A processor implemented method for automated classification of business rules from text, the method comprising: identifying, via one or more hardware processors, a business rule from a text document, wherein the business rule comprises one or more rule intents, wherein one or more rule intent comprise at least one of keywords, parts of speech (POS) tags, and wildcards and said one or more rule intents are utilized to automatically identify the business rule from the text document; creating, via the one or more hardware processors, a rule repository based on an identified business rule and the one or more rule intents, wherein the rule intents are atomic constraints stored in an ontology form to automatically identify the business rule from the text document; comparing, via the one or more hardware processors, the one or more rule intents in the business rule with the one or more clusters associated with a plurality of rule types in the rule repository to compute a match score, wherein a match score is indicative of number of rule intents and based on the match score, the business rule is annotated with one or more rule types in the rule repository, and wherein the rule repository is periodically updated with a plurality of new rule intents, said rule intent patterns and new rule types; classifying, via the one or more hardware processors, the business rule under at least one rule type, amongst the plurality of rule types, to further classify the business rule into at least one of formatted and unformatted text documents for referring and tracing the text document by annotating the business rule with one or more corresponding rule intents and one or more rule types in the rule repository; clustering, via the one or more hardware processors, said plurality of new rule intents in an agglomerative manner to identify one or more clustering rule intents that co-occur frequently in the rule repository; assembling, via the one or more hardware processors, the one or more clusters into a plurality of clustering techniques, wherein the rule repository is periodically updated with the corresponding rule intent and the rule type to classify sentences obtained from a training dataset; and automatically associating, via the one or more hardware processors, the business rules extracted from the text document with a plurality of corresponding knowledge element types and system components to reference, trace and re-use at least one of requirement artifacts and system documentation. 2. The processor implemented method as claimed in claim 1 , wherein the identifying comprises: extracting a sentence from a text document; classifying the extracted sentence as one of a candidate rule and a non-rule based on a Bayesian classification technique; and comparing a syntactic structure of the candidate rule with a plurality of rule intent patterns in the rule repository to identify the candidate rule as the business rule.

6. The method of claim 3 , further comprising executing the following operation in the data processing device: associating values and/or formulas with the pre-established DTD.

6. The method of claim 3 , further comprising executing the following operation in the data processing device: associating values and/or formulas with the pre-established DTD. 11. The method of claim 6 , wherein associating includes, not necessarily in the following order: first associating one or more lists of data objects or formulas producing data objects with a DTD construct; second associating at least one of the lists or formulas with at least one variable name; and using the variable name as a parameter in at least one other formula.

6. A method of designing a graphics processing unit, comprising: receiving an abstract model of a graphics system having a class of commands and a set of state variables; and semantically processing said abstract model to generate validation logic for said class of commands utilizing a reduced memory space shadow memory having a memory size smaller than that of a memory size associated with storing a full representation of said set of state variables, wherein said semantic processing comprises semantic processing and parsing validation checks to determine a subset of said set of state variables required for validation.

6. A method of designing a graphics processing unit, comprising: receiving an abstract model of a graphics system having a class of commands and a set of state variables; and semantically processing said abstract model to generate validation logic for said class of commands utilizing a reduced memory space shadow memory having a memory size smaller than that of a memory size associated with storing a full representation of said set of state variables, wherein said semantic processing comprises semantic processing and parsing validation checks to determine a subset of said set of state variables required for validation. 7. The method of claim 6 , wherein said semantic processing comprises generating a database for creating a hardware description language file to implement said validation logic in hardware.

7. A flow description document processing apparatus comprising: an extracting unit constructed to extract, from a first flow description document, a first description which specifies services to be invoked by the first flow description document, and extract, from a second flow description document, a second description which specifies services to be invoked by the second flow description document; a detection unit constructed to detect a common part between the first description of the first flow description document and the second description of the second flow description document, wherein services specified by the first description extracted from the first flow description document include one or more services among services specified by the second description extracted from the second flow description document; and a rewrite unit constructed to rewrite the common part in the second flow description document into a reference to the common part in the first flow description document, wherein the reference includes identification information of the first flow description document, and information which specifies a start tag and end tag of the common part.

7. A flow description document processing apparatus comprising: an extracting unit constructed to extract, from a first flow description document, a first description which specifies services to be invoked by the first flow description document, and extract, from a second flow description document, a second description which specifies services to be invoked by the second flow description document; a detection unit constructed to detect a common part between the first description of the first flow description document and the second description of the second flow description document, wherein services specified by the first description extracted from the first flow description document include one or more services among services specified by the second description extracted from the second flow description document; and a rewrite unit constructed to rewrite the common part in the second flow description document into a reference to the common part in the first flow description document, wherein the reference includes identification information of the first flow description document, and information which specifies a start tag and end tag of the common part. 12. The apparatus according to claim 7 , wherein the start tag and the end tag of the common part are tags of a markup language.

1. A method performed by one or more server devices, comprising: receiving, at a processor of the one or more server devices, a search query from a client device; selecting, using a processor of the one or more server devices, a plurality of documents based on the search query; identifying, using a processor of the one or more server devices, one or more categories associated with the plurality of documents; generating, using a processor of the one or more server devices, a score for each of the one or more categories; selecting, using a processor of the one or more server devices, a category of the one or more categories, as a recommended category, based on the scores generated for the one or more categories; and presenting, using a processor of the one or more server devices, information regarding the plurality of documents and the recommended category to the client device.

1. A method performed by one or more server devices, comprising: receiving, at a processor of the one or more server devices, a search query from a client device; selecting, using a processor of the one or more server devices, a plurality of documents based on the search query; identifying, using a processor of the one or more server devices, one or more categories associated with the plurality of documents; generating, using a processor of the one or more server devices, a score for each of the one or more categories; selecting, using a processor of the one or more server devices, a category of the one or more categories, as a recommended category, based on the scores generated for the one or more categories; and presenting, using a processor of the one or more server devices, information regarding the plurality of documents and the recommended category to the client device. 2. The method of claim 1 , where the search query relates to businesses in a particular geographic area.

1. A method comprising: modifying, using one or more processors, a query string of characters using a set of heuristics; performing, using one or more processors, a character-by-character comparison of the modified query string with at least one known string of characters in a corpus in order to locate an exact match for the modified query string; and responsive to not finding an exact match for the modified query string in the corpus, performing, by one or more processors, the following steps in order to locate an equivalent for the modified query string: forming a plurality of sub-strings of characters from the modified query string, the sub-strings having varying lengths such that at least two of the formed sub-strings differ in length, each sub-string comprising a composition of characters selected based on a frequency of occurrence of the composition in the modified query string; and using an information retrieval technique on the sub-strings formed from the modified query string to identify a known string of characters equivalent to the query string.

1. A method comprising: modifying, using one or more processors, a query string of characters using a set of heuristics; performing, using one or more processors, a character-by-character comparison of the modified query string with at least one known string of characters in a corpus in order to locate an exact match for the modified query string; and responsive to not finding an exact match for the modified query string in the corpus, performing, by one or more processors, the following steps in order to locate an equivalent for the modified query string: forming a plurality of sub-strings of characters from the modified query string, the sub-strings having varying lengths such that at least two of the formed sub-strings differ in length, each sub-string comprising a composition of characters selected based on a frequency of occurrence of the composition in the modified query string; and using an information retrieval technique on the sub-strings formed from the modified query string to identify a known string of characters equivalent to the query string. 2. The method of claim 1 , wherein forming a plurality of sub-strings of characters comprises successively extending sub-strings based on frequency of occurrence of the sub-strings in the modified query string.

8. A system comprising: a computer-readable medium having instructions stored thereon, which, when executed by a processor, cause the system to: obtain one or more query terms in a first query; and for each of the one or more query terms: search a standardized entity taxonomy to locate a standardized entity that most closely matches the query term, the standardized entity taxonomy comprising an entity identification for each of a plurality of different standardized entities; calculate a confidence score for a query term-standardized entity pair for the standardized entity that most closely matches the query term; in response to a determination that the confidence score transgresses a threshold, associate the query term with the entity identification corresponding to the standardized entity that most closely matches the query term; retrieve one or more query rewriting rules corresponding to an entity type of the standardized entity having the entity identification; and execute the one or more query rewriting rules to rewrite the first query such that the rewritten query is more restrictive than the first query.

8. A system comprising: a computer-readable medium having instructions stored thereon, which, when executed by a processor, cause the system to: obtain one or more query terms in a first query; and for each of the one or more query terms: search a standardized entity taxonomy to locate a standardized entity that most closely matches the query term, the standardized entity taxonomy comprising an entity identification for each of a plurality of different standardized entities; calculate a confidence score for a query term-standardized entity pair for the standardized entity that most closely matches the query term; in response to a determination that the confidence score transgresses a threshold, associate the query term with the entity identification corresponding to the standardized entity that most closely matches the query term; retrieve one or more query rewriting rules corresponding to an entity type of the standardized entity having the entity identification; and execute the one or more query rewriting rules to rewrite the first query such that the rewritten query is more restrictive than the first query. 11. The system of claim 8 , wherein the entity type is a skill and the one or more query rewriting rules include determining if there are any standardized titles having an affinity score with the standardized entity that most closely matches the query term higher than a preset threshold, and adding any such standardized titles to the first query with an AND connector.

15. A device for forming a structured document from an unstructured input document, the device comprising: memory storing program logic; and a processor in electrical communication with the memory and executing the program logic to: extract a plurality of tokens from the input document, each token of the plurality of tokens having a visual style; produce, for each token of the plurality of tokens, a corresponding first probability distribution across a plurality of classes each being related to information conveyed by the tokens; produce, for each token of the plurality of tokens, a corresponding second probability distribution across the plurality of classes, the corresponding second probability distribution being based at least in part on the class, of the plurality of classes, in which the token's surrounding tokens in context are most likely to be classified; and produce, for each token of the plurality of tokens, a corresponding third probability distribution across the plurality of classes, the corresponding third probability distribution being based at least in part on the corresponding visual style of the token; and classify each token of the plurality of tokens into one of the plurality of classes as a function of one or more of the first probability distribution, the second probability distribution, and the third probability distribution, wherein to classify each token, the processor executes the program logic to determine, for each class of the plurality of classes, a relative likelihood (RL) of token belonging to the class, by calculating the RL from the token's corresponding second probability distribution for the class (C) and the token's corresponding third probability distribution for the class (S) according to the function: RL=C*S 4 .

15. A device for forming a structured document from an unstructured input document, the device comprising: memory storing program logic; and a processor in electrical communication with the memory and executing the program logic to: extract a plurality of tokens from the input document, each token of the plurality of tokens having a visual style; produce, for each token of the plurality of tokens, a corresponding first probability distribution across a plurality of classes each being related to information conveyed by the tokens; produce, for each token of the plurality of tokens, a corresponding second probability distribution across the plurality of classes, the corresponding second probability distribution being based at least in part on the class, of the plurality of classes, in which the token's surrounding tokens in context are most likely to be classified; and produce, for each token of the plurality of tokens, a corresponding third probability distribution across the plurality of classes, the corresponding third probability distribution being based at least in part on the corresponding visual style of the token; and classify each token of the plurality of tokens into one of the plurality of classes as a function of one or more of the first probability distribution, the second probability distribution, and the third probability distribution, wherein to classify each token, the processor executes the program logic to determine, for each class of the plurality of classes, a relative likelihood (RL) of token belonging to the class, by calculating the RL from the token's corresponding second probability distribution for the class (C) and the token's corresponding third probability distribution for the class (S) according to the function: RL=C*S 4 . 17. The device of claim 15 , wherein the third probability distribution of a corresponding token of the plurality of tokens is further based on the second probability distribution of the corresponding token.

1. A method of incorporating query results into an abstract database, comprising: receiving a first set of query results produced by executing a first abstract query using a first data abstraction model against a first database; determining one or more mappings between the first set of query results and one or more logical fields in a second data abstraction model, wherein the second data abstraction model models underlying physical data in a manner making a schema of the physical data transparent to a user of the second data abstraction model, further comprising: determining similarities between at least a portion of the first set of query results and at least one field in the second database; and determining at least one logical field that maps to the at least one field in the second database; and modifying one or more logical field definitions within the second data abstraction model to further map to the at least a portion of the first set of query results, based on the determined one or more mappings, wherein the one or more logical field definitions correspond to the one or more logical fields, such that abstract queries can be executed against both the second database and the first set of query results using the modified second data abstraction model, wherein the first database is distinct from the second database, and wherein the first data abstraction model is distinct from the second data abstraction model.

1. A method of incorporating query results into an abstract database, comprising: receiving a first set of query results produced by executing a first abstract query using a first data abstraction model against a first database; determining one or more mappings between the first set of query results and one or more logical fields in a second data abstraction model, wherein the second data abstraction model models underlying physical data in a manner making a schema of the physical data transparent to a user of the second data abstraction model, further comprising: determining similarities between at least a portion of the first set of query results and at least one field in the second database; and determining at least one logical field that maps to the at least one field in the second database; and modifying one or more logical field definitions within the second data abstraction model to further map to the at least a portion of the first set of query results, based on the determined one or more mappings, wherein the one or more logical field definitions correspond to the one or more logical fields, such that abstract queries can be executed against both the second database and the first set of query results using the modified second data abstraction model, wherein the first database is distinct from the second database, and wherein the first data abstraction model is distinct from the second data abstraction model. 5. The method of claim 1 , further comprising: receiving an abstract query for processing; executing the abstract query against the second database and the first set of query results using the modified second data abstraction model to produce a second set of query results; and transmitting the second set of query results.

2. A method for controlling the voice of a synthetic character that is autonomous and interacts with others in a shared environment, such character having an associated mental state that changes over time at least in part as a function of said mental state, said mental state comprising a dynamic perceptual state that depends on said shared environment, said shared environment including the entities with which the character is interacting, and an action state defining actions to be taken by the character, the method comprising: providing speech data corresponding to at least a part of an intended communication generated by the character; creating modified speech data by modifying, by an automatically determined amount, at least one of the pitch or duration of at least a portion of the speech data, such modification based at least in part on at least a portion of said mental state; and generating speech sounds associated with the character using the modified speech data; wherein the mental state is determined at least in part by interaction of the character with others in a shared environment.

2. A method for controlling the voice of a synthetic character that is autonomous and interacts with others in a shared environment, such character having an associated mental state that changes over time at least in part as a function of said mental state, said mental state comprising a dynamic perceptual state that depends on said shared environment, said shared environment including the entities with which the character is interacting, and an action state defining actions to be taken by the character, the method comprising: providing speech data corresponding to at least a part of an intended communication generated by the character; creating modified speech data by modifying, by an automatically determined amount, at least one of the pitch or duration of at least a portion of the speech data, such modification based at least in part on at least a portion of said mental state; and generating speech sounds associated with the character using the modified speech data; wherein the mental state is determined at least in part by interaction of the character with others in a shared environment. 17. The method of claim 2 , wherein said speech sounds have a natural quality such as might be displayed by a living animal or person.

6. The computer-implemented method of claim 4 , further comprising performing at least one of automatic speech recognition or natural language understanding on a second utterance using the predictive language model interpolated with a second language model.

6. The computer-implemented method of claim 4 , further comprising performing at least one of automatic speech recognition or natural language understanding on a second utterance using the predictive language model interpolated with a second language model. 7. The computer-implemented method of claim 6 , wherein the second language model is one of a content domain-specific personal language model or the general language model.

1. A method for selecting a language from a number of languages used by an electronic device during a text entry session, the method comprising: receiving contact information for recipients that comprises a number of language tags for each recipient; determining that the text entry session is associated with composing a message to a plurality of the recipients; examining at least one of the number of language tags associated with each of the plurality of recipients; and automatically selecting a language for use by a text disambiguation function during the text entry session based on the examined language tags.

1. A method for selecting a language from a number of languages used by an electronic device during a text entry session, the method comprising: receiving contact information for recipients that comprises a number of language tags for each recipient; determining that the text entry session is associated with composing a message to a plurality of the recipients; examining at least one of the number of language tags associated with each of the plurality of recipients; and automatically selecting a language for use by a text disambiguation function during the text entry session based on the examined language tags. 2. The method of claim 1 , wherein when a predetermined percentage of the plurality of recipients share a common language tag, the selected language is a language represented by the common language tag.

10. A computer-readable storage medium storing instructions that when executed by a computer cause the computer to perform a method for converting a template written in a templating language, the method comprising: receiving the template in the templating language at a translator; compiling the template with the translator create a function in the host language; merging the function in the host language with a subset of code written in the host language; converting the subset of code written in the host language into a safe subset of code written in the host language, wherein the safe subset of code written in the host language adheres to a pre-determined schema and a pre-determined set of constraints; and translating the safe subset of code written in the host language to a subset of code written in a templated language, wherein the translating comprises: determining that an element within the safe subset of code written in the host language comprises a dynamic value, and wrapping the dynamic value with host language code that allows the dynamic value to be translated to the templated language upon execution of the subset of code written in the templated language.

10. A computer-readable storage medium storing instructions that when executed by a computer cause the computer to perform a method for converting a template written in a templating language, the method comprising: receiving the template in the templating language at a translator; compiling the template with the translator create a function in the host language; merging the function in the host language with a subset of code written in the host language; converting the subset of code written in the host language into a safe subset of code written in the host language, wherein the safe subset of code written in the host language adheres to a pre-determined schema and a pre-determined set of constraints; and translating the safe subset of code written in the host language to a subset of code written in a templated language, wherein the translating comprises: determining that an element within the safe subset of code written in the host language comprises a dynamic value, and wrapping the dynamic value with host language code that allows the dynamic value to be translated to the templated language upon execution of the subset of code written in the templated language. 14. The computer-readable storage medium of claim 10 , wherein the method further comprises: ensuring that the template adheres to a pre-determined set of constraints; and ensuring that the subset of code written in the templated language adheres to a pre-determined set of constraints.

4. A method of determining the relative effectiveness of search engines comprising the steps of: selecting documents from a database; generating from the selected documents controlled relevant documents by manipulation of relevant terms in the selected documents based on a relevancy algorithm; wherein the relevancy algorithm relates to positions of the relevant terms in documents and the generation of the controlled relevant documents is done by placing search terms into various positions in the controlled relevant documents in accordance with the relevant algorithm; using the generated controlled relevant documents to create relevancy reference vectors; searching the database using each of the search engines and generating performance vectors with the search results for each engine; and comparing the performance vectors for each engine to the relevancy reference vectors to provide a relevancy rating for the search engines based on the relevancy algorithm using a relevancy vector chart made from the relevancy reference vectors to obtain a relative ranking of each of the search engines wherein at least 3 different controlled relevant documents are produced for each selected document using a given relevancy algorithm with search terms placed in 3 different positions in different documents with each document compared with the document relevant to the search area with an absence of search terms in the document to generate the relevancy reference vectors of the relevancy vector chart.

4. A method of determining the relative effectiveness of search engines comprising the steps of: selecting documents from a database; generating from the selected documents controlled relevant documents by manipulation of relevant terms in the selected documents based on a relevancy algorithm; wherein the relevancy algorithm relates to positions of the relevant terms in documents and the generation of the controlled relevant documents is done by placing search terms into various positions in the controlled relevant documents in accordance with the relevant algorithm; using the generated controlled relevant documents to create relevancy reference vectors; searching the database using each of the search engines and generating performance vectors with the search results for each engine; and comparing the performance vectors for each engine to the relevancy reference vectors to provide a relevancy rating for the search engines based on the relevancy algorithm using a relevancy vector chart made from the relevancy reference vectors to obtain a relative ranking of each of the search engines wherein at least 3 different controlled relevant documents are produced for each selected document using a given relevancy algorithm with search terms placed in 3 different positions in different documents with each document compared with the document relevant to the search area with an absence of search terms in the document to generate the relevancy reference vectors of the relevancy vector chart. 5. The method of claim 4 , including removing relevant terms from the selected documents prior to placing the search terms into the various positions of the controlled relevant documents.

13. A computer-implemented method for navigating a set of recordings stored on a handheld storage and retrieval device on a display of the handheld storage and retrieval device, comprising: on the display of the handheld device, presenting an initial screen for displaying only a hierarchical menu comprising only two hierarchical levels of a plurality of hierarchical levels of the set of recordings, the initial screen having initial headers representing a first one of the plurality of hierarchical levels and oriented according to a first orientation, said initial headers providing top-level categories for filtering the set of recordings, wherein each of said initial headers includes an association with a child list, whereby said initial headers form a band of lists represented by the initial headers; navigating the initial headers according to the first orientation; presenting a list of elements representing a second one of the plurality of hierarchical levels oriented according to a second orientation different from the first orientation, the list of elements associated with a currently selected header of the initial headers as a child list for the currently selected header; navigating the list of elements according to the second orientation; selecting an element from the list of elements; in response to said selecting the element, presenting a new screen having the list of elements as element headers displayed according to the first orientation, the element headers including the selected element as a new currently selected header of the element headers and displayed in an order determined by the selecting; and presenting a second list of elements representing a third one of the plurality of hierarchical levels and associated with the new currently selected header of the element headers as a child list for the new currently selected header, the second list of elements displayed according to the second orientation, the new screen displaying only the element headers and the second list of elements; and repeating navigating and selecting until a particular hierarchical level comprising atomic elements is displayed in the second orientation, wherein upon selection of an atomic element of the atomic elements, the display screen displays only the particular hierarchical level in the first orientation with the selected atomic element as an active header and an information display box containing information about the selected atomic element.

13. A computer-implemented method for navigating a set of recordings stored on a handheld storage and retrieval device on a display of the handheld storage and retrieval device, comprising: on the display of the handheld device, presenting an initial screen for displaying only a hierarchical menu comprising only two hierarchical levels of a plurality of hierarchical levels of the set of recordings, the initial screen having initial headers representing a first one of the plurality of hierarchical levels and oriented according to a first orientation, said initial headers providing top-level categories for filtering the set of recordings, wherein each of said initial headers includes an association with a child list, whereby said initial headers form a band of lists represented by the initial headers; navigating the initial headers according to the first orientation; presenting a list of elements representing a second one of the plurality of hierarchical levels oriented according to a second orientation different from the first orientation, the list of elements associated with a currently selected header of the initial headers as a child list for the currently selected header; navigating the list of elements according to the second orientation; selecting an element from the list of elements; in response to said selecting the element, presenting a new screen having the list of elements as element headers displayed according to the first orientation, the element headers including the selected element as a new currently selected header of the element headers and displayed in an order determined by the selecting; and presenting a second list of elements representing a third one of the plurality of hierarchical levels and associated with the new currently selected header of the element headers as a child list for the new currently selected header, the second list of elements displayed according to the second orientation, the new screen displaying only the element headers and the second list of elements; and repeating navigating and selecting until a particular hierarchical level comprising atomic elements is displayed in the second orientation, wherein upon selection of an atomic element of the atomic elements, the display screen displays only the particular hierarchical level in the first orientation with the selected atomic element as an active header and an information display box containing information about the selected atomic element. 14. A method according to claim 13 , wherein the currently selected header of the element headers of the new screen is the header based upon the selected element.

7. The Internet searching application method as recited in claim 6 , wherein the constellation of previously visited websites are displayed in an interconnected graphical tree form based on a traversal history of the user regarding how the user navigated from one website to another in one or more Internet searching sessions.

7. The Internet searching application method as recited in claim 6 , wherein the constellation of previously visited websites are displayed in an interconnected graphical tree form based on a traversal history of the user regarding how the user navigated from one website to another in one or more Internet searching sessions. 8. The Internet searching application method as recited in claim 7 , wherein the traversal history includes the user's navigation data gathered from a plurality of UE devices associated with the user.

1. A method for analyzing data, the method comprising: generating a query result in response to querying data using a query, wherein the data is in a markup language format; generating a graph in response to the query result and using the data in the markup language format, wherein entities of the data in the markup language format are represented as nodes within the graph; and identifying a pattern associated with the query result using the graph, wherein the data in the markup language format is used for pattern identification and the pattern is a correlation between entities, and wherein identifying the pattern comprises identifying a positive correlation comprising a node from the graph that is determined to be relevant to the query result and a negative correlation comprising a node from the graph that is determined to be least relevant to the query result.

1. A method for analyzing data, the method comprising: generating a query result in response to querying data using a query, wherein the data is in a markup language format; generating a graph in response to the query result and using the data in the markup language format, wherein entities of the data in the markup language format are represented as nodes within the graph; and identifying a pattern associated with the query result using the graph, wherein the data in the markup language format is used for pattern identification and the pattern is a correlation between entities, and wherein identifying the pattern comprises identifying a positive correlation comprising a node from the graph that is determined to be relevant to the query result and a negative correlation comprising a node from the graph that is determined to be least relevant to the query result. 5. The method of claim 1 , wherein one selected from the set of the query and the query result comprises at least one of an XQuery and a Structured Query Language/eXtensible Markup Language (SQL-XML).

7. The computer program product of claim 6 , further comprising: refining the query context, wherein refining the query context includes: presenting one or more search result contexts related to the first results to the user; receiving input from the user regarding a user-chosen search result context; and modifying the query context based on the user-chosen search result context; searching second data for second results, wherein the searching is limited by the refined query context; and providing second results to the user.

7. The computer program product of claim 6 , further comprising: refining the query context, wherein refining the query context includes: presenting one or more search result contexts related to the first results to the user; receiving input from the user regarding a user-chosen search result context; and modifying the query context based on the user-chosen search result context; searching second data for second results, wherein the searching is limited by the refined query context; and providing second results to the user. 8. The computer program product of claim 7 wherein the second data is the first results.

1. A method of operation for a processor-based device to identify a machine-readable symbol in an image, the processor-based device including at least one processor and at least one nontransitory processor-readable storage medium, the method comprising: receiving, in the at least one nontransitory processor-readable storage medium, a plurality of training images, each training image corresponding to one of a plurality of machine-readable symbols; generating, by the at least one processor, a distortion model for the training images; generating, by the at least one processor, a plurality of distorted image signals based at least in part on the distortion model and the training images, each of the plurality of distorted image signals corresponding to one of the machine-readable symbols; transforming, by the at least one processor, the plurality of distorted image signals from a signal space into a first transform space; extracting, by the at least one processor, classification features from the transformed distorted image signals in the first transform space; training, by the at least one processor, a first machine-readable symbol classifier based at least in part on the extracted classification features; determining, by the at least one processor, a quality measure for the first machine-readable symbol classifier; transforming, by the at least one processor, the plurality of distorted image signals from the signal space into a second transform space; extracting, by the at least one processor, classification features from the distorted image signals in the second transform space; training, by the at least one processor, a second machine-readable symbol classifier based at least in part on the extracted classification features; determining, by the at least one processor, a quality measure for the second machine-readable symbol classifier; and selecting one of the first machine-readable symbol classifier or the second machine-readable symbol classifier based at least in part on the determined quality measure.

1. A method of operation for a processor-based device to identify a machine-readable symbol in an image, the processor-based device including at least one processor and at least one nontransitory processor-readable storage medium, the method comprising: receiving, in the at least one nontransitory processor-readable storage medium, a plurality of training images, each training image corresponding to one of a plurality of machine-readable symbols; generating, by the at least one processor, a distortion model for the training images; generating, by the at least one processor, a plurality of distorted image signals based at least in part on the distortion model and the training images, each of the plurality of distorted image signals corresponding to one of the machine-readable symbols; transforming, by the at least one processor, the plurality of distorted image signals from a signal space into a first transform space; extracting, by the at least one processor, classification features from the transformed distorted image signals in the first transform space; training, by the at least one processor, a first machine-readable symbol classifier based at least in part on the extracted classification features; determining, by the at least one processor, a quality measure for the first machine-readable symbol classifier; transforming, by the at least one processor, the plurality of distorted image signals from the signal space into a second transform space; extracting, by the at least one processor, classification features from the distorted image signals in the second transform space; training, by the at least one processor, a second machine-readable symbol classifier based at least in part on the extracted classification features; determining, by the at least one processor, a quality measure for the second machine-readable symbol classifier; and selecting one of the first machine-readable symbol classifier or the second machine-readable symbol classifier based at least in part on the determined quality measure. 5. The method of claim 1 wherein extracting classification features from the distorted image signals in the first transform space comprises extracting a set of spectral coefficients.

1. A healthcare dictionary system providing a term repository accessible for use in supporting the operation of a healthcare enterprise, comprising: an input processor for acquiring healthcare transaction message data including data for communication from a first healthcare facility to at least a second different healthcare facility in at least one of a plurality of different communication protocol data formats and being communicated between different facilities of a healthcare enterprise; a data processor for, parsing said acquired transaction message data to identify a communication protocol data format of said transaction message and extracting a term from said acquired transaction message data, comparing said extracted term to terms in a first term repository, said first term repository including at least one of, (a) definitions indicating meaning of a plurality of healthcare terms used by a particular healthcare facility and (b) synonyms of a plurality of healthcare terms used by a particular healthcare facility and updating said first term repository to include said extracted term in response to a determination, said extracted term is absent from said first term repository; and a communication processor for intermittently processing content of said first term repository to be suitable for communication to a second term repository including definitions of a plurality of healthcare terms used by a different healthcare facility.

1. A healthcare dictionary system providing a term repository accessible for use in supporting the operation of a healthcare enterprise, comprising: an input processor for acquiring healthcare transaction message data including data for communication from a first healthcare facility to at least a second different healthcare facility in at least one of a plurality of different communication protocol data formats and being communicated between different facilities of a healthcare enterprise; a data processor for, parsing said acquired transaction message data to identify a communication protocol data format of said transaction message and extracting a term from said acquired transaction message data, comparing said extracted term to terms in a first term repository, said first term repository including at least one of, (a) definitions indicating meaning of a plurality of healthcare terms used by a particular healthcare facility and (b) synonyms of a plurality of healthcare terms used by a particular healthcare facility and updating said first term repository to include said extracted term in response to a determination, said extracted term is absent from said first term repository; and a communication processor for intermittently processing content of said first term repository to be suitable for communication to a second term repository including definitions of a plurality of healthcare terms used by a different healthcare facility. 18. A system according to claim 1 , wherein said data processor processes said extracted term to be compatible with said first term repository by storing said term in said first term repository together with at least one of, (a) a term identifier code created by said data processor and (b) an organization identifier code associated with a source system of said extracted term.

1. A method for recognizing a list in a vector graphics based document comprising: receiving the vector graphics based document in an original format, the original format having a set of list rendering instructions for at least one list in the vector graphics based document; parsing the vector graphics based document to determine that the at least one list exists in the vector graphics based document, wherein the parsing comprises identifying a plurality of list items, the list items identified based upon a same word indentation for a first word of respective list items, wherein the first word of respective list items follow at least one of a letter, number, and symbol indicative of the list item; and outputting the list to an output medium in a modified format, where the modified format is chosen based on the particular output medium.

1. A method for recognizing a list in a vector graphics based document comprising: receiving the vector graphics based document in an original format, the original format having a set of list rendering instructions for at least one list in the vector graphics based document; parsing the vector graphics based document to determine that the at least one list exists in the vector graphics based document, wherein the parsing comprises identifying a plurality of list items, the list items identified based upon a same word indentation for a first word of respective list items, wherein the first word of respective list items follow at least one of a letter, number, and symbol indicative of the list item; and outputting the list to an output medium in a modified format, where the modified format is chosen based on the particular output medium. 10. The method of claim 1 , wherein parsing the vector graphics based document to determine that the at least one list exists in the vector graphics based document comprises: parsing a first line of the vector graphics based document to detect a character of a first list item; parsing a second line of the vector graphics based document to detect a character of a second list item, wherein the second list item is different than the first list item, wherein the character of the second list item is at least one of a character identical to the character of the first list item and a character sequential to the character of the first list item; verifying that the first and second list items are items of the at least one list, the verification comprising comparing a left indent of the character of the first list item with a left indent of the character of the second list item; and determining that the at least one list exists based upon the verification.

9. The method of claim 6 , further comprises the user agent conducting a process to receive from an identity manager at least one indication of the at least one information card each representative of a user identity, and to determine a privacy preference for the at least one information card.

9. The method of claim 6 , further comprises the user agent conducting a process to receive from an identity manager at least one indication of the at least one information card each representative of a user identity, and to determine a privacy preference for the at least one information card. 10. The method of claim 9 , further comprises the user agent evaluating further includes evaluating the privacy preference of the at least one information card determined by the identity manager to satisfy the security policy requirements.

11. A method, implemented by a computing device, comprising: providing a chunking specification; providing one or more part-of-speech-tagged corpora; chunking the one or more corpora, with a processor, in accordance with the chunking specification, the chunking comprising: chunking nouns, noun-verbs, pronouns, noun-adjectives, noun morphemes, and named entities, either single or as the head in a modifier-head structure with a modifier comprising a noun, a numerical phrase, or an adjective phrase, as noun chunks; chunking verbs, verb-particle structures in which the particle comprises an oriented verb or an auxiliary, and modifier-verb structures in which the modifier comprises an adverbial phrase or an auxiliary verb, in verb chunks; refining the chunking specification through iterative chunking consistency feedback with a training corpus, wherein one or more groups of identical or similar sections of text from different parts of the training corpus that have been chunked differently from each other are compared, and for one or more of the groups, the chunking of one of the identical or similar sections of text is selected to replace the chunking of the other identical or similar sections of text within the group; automatically calculating, with a processor, a consistency ratio for the chunking of the training corpus; comparing the calculated consistency ratio with a chunk consistency threshold, wherein refining the chunking specification is continued at least until the calculated consistency ratio meets the chunk consistency threshold; incorporating the refined chunking specification in a chunking utility that also comprises a plurality of definitions of chunk types and a set of chunking rules; receiving an input; assigning chunk types to portions of the input based at least in part on the chunking utility comprising the refined chunking specification; and providing an output comprising the portions of the input with the assigned chunk types.

11. A method, implemented by a computing device, comprising: providing a chunking specification; providing one or more part-of-speech-tagged corpora; chunking the one or more corpora, with a processor, in accordance with the chunking specification, the chunking comprising: chunking nouns, noun-verbs, pronouns, noun-adjectives, noun morphemes, and named entities, either single or as the head in a modifier-head structure with a modifier comprising a noun, a numerical phrase, or an adjective phrase, as noun chunks; chunking verbs, verb-particle structures in which the particle comprises an oriented verb or an auxiliary, and modifier-verb structures in which the modifier comprises an adverbial phrase or an auxiliary verb, in verb chunks; refining the chunking specification through iterative chunking consistency feedback with a training corpus, wherein one or more groups of identical or similar sections of text from different parts of the training corpus that have been chunked differently from each other are compared, and for one or more of the groups, the chunking of one of the identical or similar sections of text is selected to replace the chunking of the other identical or similar sections of text within the group; automatically calculating, with a processor, a consistency ratio for the chunking of the training corpus; comparing the calculated consistency ratio with a chunk consistency threshold, wherein refining the chunking specification is continued at least until the calculated consistency ratio meets the chunk consistency threshold; incorporating the refined chunking specification in a chunking utility that also comprises a plurality of definitions of chunk types and a set of chunking rules; receiving an input; assigning chunk types to portions of the input based at least in part on the chunking utility comprising the refined chunking specification; and providing an output comprising the portions of the input with the assigned chunk types. 19. The method of claim 11 , further in which a pattern with a verb or a possessive particle, then a first noun, then a second noun is chunked with the first and second nouns in separate chunks if the verb and first noun form a verb phrase that modifies the second noun or the possessive particle and first noun form a noun phrase that modifies the second noun.

1. A method of providing a floating command object that is contextually relevant to selected text, the method comprising: upon receiving a selection of text in an electronic document for editing, displaying a command object adjacent to the selected text such that at least a portion of the selected text remains visible, the command object providing text editing functionality in response to the selection of the text; displaying in the command object a set of functionality commands that are relevant to editing the selected text, the set of functionality commands being a subset of a broader range of functionality commands available for editing the selected text, wherein displaying in the command object the set of functionality commands comprises displaying the set of functionality commands with a first set of visual representations similar to a second set of visual representations associated with displaying the broader range of functionality commands; associating an opacity of the displayed command object to a distance between an electronic pointer and the displayed command object; and continuing to display the command object after receiving a selection of one of the set of functionality commands.

1. A method of providing a floating command object that is contextually relevant to selected text, the method comprising: upon receiving a selection of text in an electronic document for editing, displaying a command object adjacent to the selected text such that at least a portion of the selected text remains visible, the command object providing text editing functionality in response to the selection of the text; displaying in the command object a set of functionality commands that are relevant to editing the selected text, the set of functionality commands being a subset of a broader range of functionality commands available for editing the selected text, wherein displaying in the command object the set of functionality commands comprises displaying the set of functionality commands with a first set of visual representations similar to a second set of visual representations associated with displaying the broader range of functionality commands; associating an opacity of the displayed command object to a distance between an electronic pointer and the displayed command object; and continuing to display the command object after receiving a selection of one of the set of functionality commands. 4. The method of claim 1 , in response to receiving a selection of another object for editing, closing the display of the command object from its display position relative to the previously selected text.

17. A method performed by data processing apparatus, the method comprising: selecting a non-text content item that is associated with each of a plurality of web pages; receiving label data that includes a set of initial labels for the non-text content item and a resource identifier for each initial label, wherein each initial label includes one or more words; selecting one or more sets of matching web pages from the plurality of web pages, wherein each set of matching web pages includes two or more matching web pages; grouping, for each set of matching web pages, initial labels that are associated with the set of matching web pages into a separate initial label group that corresponds to the set of matching web pages; selecting one or more sets of matching labels, wherein each set of matching labels includes two or more initial labels; grouping each set of matching labels into a separate initial label group that corresponds to the set of matching labels; and selecting, as a final label for the non-text content item, an n-gram of one or more words that are included in at least a threshold number of separate initial label groups.

17. A method performed by data processing apparatus, the method comprising: selecting a non-text content item that is associated with each of a plurality of web pages; receiving label data that includes a set of initial labels for the non-text content item and a resource identifier for each initial label, wherein each initial label includes one or more words; selecting one or more sets of matching web pages from the plurality of web pages, wherein each set of matching web pages includes two or more matching web pages; grouping, for each set of matching web pages, initial labels that are associated with the set of matching web pages into a separate initial label group that corresponds to the set of matching web pages; selecting one or more sets of matching labels, wherein each set of matching labels includes two or more initial labels; grouping each set of matching labels into a separate initial label group that corresponds to the set of matching labels; and selecting, as a final label for the non-text content item, an n-gram of one or more words that are included in at least a threshold number of separate initial label groups. 30. The method of claim 17 , wherein selecting one or more sets of matching labels comprises selecting two or more labels that each include a word that corresponds to a same concept.

1. A method for tracking interests of users based on personal information, the method comprising: monitoring a stream of documents from more than one user to obtain therefrom a plurality of electronic documents each belonging to one of the users; generating, for each obtained electronic document, a dynamic interest profile (DIP) document based on information obtained from that electronic document; associating each generated DIP document with one of the plurality of users; uniquely assigning a document queue to each of the users; selecting, for each DIP document, one of the document queues into which to place that DIP document based on the user with which that DIP document is associated; polling each document queue to detect changes to that document queue; determining, in response to detecting a change to the document queue of each user, at least one person of importance to that user and one term of importance to that user based on the DIP documents in the document queue uniquely assigned to that user; generating an interest profile of each user that includes each person and each term of importance of that user; and storing the interest profiles of the users in a DIP database.

1. A method for tracking interests of users based on personal information, the method comprising: monitoring a stream of documents from more than one user to obtain therefrom a plurality of electronic documents each belonging to one of the users; generating, for each obtained electronic document, a dynamic interest profile (DIP) document based on information obtained from that electronic document; associating each generated DIP document with one of the plurality of users; uniquely assigning a document queue to each of the users; selecting, for each DIP document, one of the document queues into which to place that DIP document based on the user with which that DIP document is associated; polling each document queue to detect changes to that document queue; determining, in response to detecting a change to the document queue of each user, at least one person of importance to that user and one term of importance to that user based on the DIP documents in the document queue uniquely assigned to that user; generating an interest profile of each user that includes each person and each term of importance of that user; and storing the interest profiles of the users in a DIP database. 11. The method of claim 1 , further comprising: updating incremental feature vectors for each user based on the information obtained from the plurality of electronic documents for that user; storing the incremental feature vectors for each user in the DIP database; periodically performing an importance calculation using the incremental feature vectors for each user, to update the interest profile of that user; and storing the importance feature vectors for each user in the DIP database.