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AIPD_nlp_granted_claims

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18. The method of claim 16 , wherein the syntax grammar format comprises a string of delimited syntax tokens, and wherein each syntax token corresponds to a table identifier for at least one each of the plurality of inter-linked table objects.

19. The method of claim 18 , wherein parsing the input string for each syntax token further comprises parsing table identifiers corresponding to syntax tokens in the input string based on delimiters in the input string.

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20. The computer program product of claim 15 , wherein the grouping the plurality of responses to the query into the two or more groups of responses to the query comprises dividing the plurality of responses based on similarity, and wherein each group comprises a corresponding set of responses to the query that are similar to one another.

15. A computer program product comprising a computer readable storage medium having computer readable program code embodied thereon, the computer readable program code executable by a processor to perform a method comprising: receiving a query; establishing a target confidence level for the query, the target confidence level representing a requested level of accuracy for a result of the query; assigning a plurality of confidence levels to a plurality of analytics engines, the plurality of confidence levels comprising a respective confidence level for each analytics engine in the plurality of analytics engines; selecting a preliminary set of analytics engines from among the plurality of analytics engines; querying each analytics engine in the preliminary set of analytics engines based on the query; receiving a plurality of responses to the query, the plurality of responses comprising a response to the query from each analytics engine in the preliminary set of analytics engines; grouping the plurality of responses to the query into two or more groups of responses to the query, wherein each group is associated with a set of analytics engines that provided the set of responses in the group; calculating two or more group confidence levels for the two or more groups, wherein a group confidence level is calculated for each group based on the respective confidence level of each analytics engine in the set of analytics engines associated with the group; selecting a first group of the two or more groups of responses to the query, wherein the selecting is at least partially based on the target confidence level and the group confidence level of the first group; and summarizing into a final result the first group of responses to the query, the final result being an answer to the query.

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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.

8. The text subtitle decoder of claim 1 , wherein the text subtitle processor is configured to parse the text subtitle stream into palette information.

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43. The system according to claim 37 , wherein additional nodes and connections are formed in the network in response to the guesstimates.

37. A system, comprising: a host computing device including a machine learning network, the host computing device initializing at least one of i) nodes in the network and ii) connections between the nodes, to a respective predetermined strength value, the host computing device outputting questions, each question corresponding to at least one of the connections; and a plurality of client computing devices receiving the questions, the client computing devices transmitting guesstimates from users thereof in response to the questions, wherein the host computing device adjusts the predetermined strength value as a function of the guesstimates, and wherein the network is a consensus network and the users are able to combine their own guesstimates with those of other users to develop and evaluate the network.

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1. A method for creating a multimedia business simulation utilizing a rule-based expert system with a spreadsheet object component that includes data, calculations required for the simulation and communication information to provide a dynamic, goal based educational learning experience, comprising the steps of: (a) accessing the information in the spreadsheet object component of the rule-based expert system to retrieve information indicative of a goal; (b) utilizing the information in the spreadsheet object component of the rule-based expert system to integrate goal-based learning information in a structured, dynamic business simulation designed by a profiling component to motivate accomplishment of the goal; and (c) monitoring answers to questions posed to evaluate the progress toward the goal utilizing the spreadsheet object component of the rule-based expert system and providing dynamic, goal-based, remediation learning information feedback from a remediation object component including a knowledge system and a software tutor comprising an artificial intelligence engine which generates individualized coaching messages further motivates accomplishment of the goal; and (d) adjusting the feedback based on the student's progress toward the goal.

3. A method for creating a multimedia business simulation as recited in claim 1, including the step of evaluating the progress based on the work completed by the student.

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1. A computer-implemented method comprising: receiving a search query; identifying a lookup table corresponding to the search query; for each image in a collection of images: generating an image hash for the image based on one or more features extracted from the image, wherein the image hash comprises a plurality of hash characters, and computing a score for the image hash using the lookup table, wherein computing the score for each image hash comprises summing lookup table weights for each hash character of the plurality of hash characters, and wherein the lookup table includes a first index that corresponds to the lookup table value of each hash character, and a second index that corresponds to a position of each hash character in the image hash; ordering the images by the score of each image hash; and providing a group of the ordered images as search results responsive to the search query.

4. The method of claim 1 , wherein the search query is a text query and the ordered images represent a relevance of each image to the text query.

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7. The method according to claim 5 , wherein invoking the action comprises prioritizing at least some of the nuclear data structures and presenting the prioritized nuclear data structures to a user.

5. A computer-implemented method for text processing, comprising: processing one or more semantic constructs comprising semantic elements; recognizing respective syntactic roles of the semantic elements, wherein syntactic roles of the different semantic elements are represented by a given sentence as a hierarchical structure such as a tree, having subordinate and superordinate branches connected at nodes; representing the semantic constructs by respective hierarchical data structures, which represent hierarchical relationships among the semantic elements of the respective semantic constructs; removing at least one selected semantic element from the hierarchical data structures, so as to produce respective nuclear data structures; aggregating similar nuclear constructs, so that frequently occurring nuclear constructs are given high statistical significance; invoking an action with respect to the nuclear data structures; and computing respective occurrence frequencies of the nuclear data structures, wherein invoking the action comprises invoking the action with respect to one or more of the nuclear data structures whose occurrence frequencies meet a predefined condition, wherein removing the at least one selected semantic element comprises predefining a nuclearization depth, and determining the selected semantic elements to be removed responsively to the predefined nuclearization depth.

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12. The method of claim 11 wherein a matching algorithm from the one or more matching algorithms filter the array of strings or the comparison reference database.

13. The method of claims 12 further comprising: filtering the array of strings to obtain a set of strings wherein the set of strings comprise all strings within a specified position in the array from the date string corresponding to the earliest date; and comparing the set of strings to one or more data fields from each record in the comparison reference database.

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23. The method of claim 12 wherein determining a likelihood that each candidate object is an object of the predetermined object type further comprises adjusting the belief score based on an identity of an individual in the source image.

12. A method to recognize objects in an image, the method comprising: searching a source image for any candidate objects of a predetermined object type by applying a cascade classifier associated with the predetermined object type to the source image; determining a likelihood that each candidate object is an object of the predetermined object type by: determining a plurality of scores for a candidate object from a plurality of verification tests applied to the candidate object, each one of the scores determined from a corresponding one of the verification tests, wherein each one of the scores represents an indication of a difference between the candidate object and a set of reference images for the predetermined object type; and determining a belief score for the candidate object from the scores for the candidate object, the belief score indicating the likelihood that the candidate object is of the predetermined object type; and identifying the candidate object as a detected object of the predetermined object type when the belief score relative to a threshold belief score indicates the candidate object is of the predetermined object type.

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1. A computer-implemented method comprising: maintaining a user profile for each of a plurality of users of a social networking system, each user profile comprising a set of attributes; selecting a user from the plurality of users; receiving user profile information for at least one user of a set of users in the social networking system who are connected to the selected user in the social networking system; inferring a value of one or more attributes of the user profile for the selected user based on information describing the set of users who are connected to the selected user in the social networking system; comparing a confidence score value for an inferred user profile attribute to a threshold value; storing, responsive to the confidence score being above the threshold value, the inferred value of the user profile attribute with the user profile for the selected user; determining relevant information for the selected user based on the inferred user profile attribute; and sending the relevant information to the selected user.

27. The computer-implemented method of claim 1 , further comprising: determining whether the inferred value of the attribute of the selected user is consistent with at least one other user profile attribute of the user; and responsive to determining that the inferred value is inconsistent with at least one other user profile attribute, rejecting the inferred user profile value.

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16. The system of claim 15 , wherein the two or more pieces of communication data in each of the communication threads are related by being associated with two or more common participants.

15. The system of claim 14 , wherein each communication thread includes two or more pieces of the communication data that are related, regardless of the source, by having similar information in one or more of the common fields.

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6. A method for monitoring innovation activity, comprising: accumulating first metadata associated with an intellectual knowledge file; extracting at least one pattern from said intellectual knowledge file; assigning, using rules-based processing, said first metadata and said at least one pattern, said intellectual knowledge file to a concept space among a plurality of existing concept spaces; and, generating a report, said report correlating said intellectual knowledge file and said plurality of concept spaces, wherein said steps of accumulating, extracting, assigning, and generating are performed by a general-purpose computer specially programmed to perform said steps of accumulating, extracting, assigning, and generating.

7. The method of claim 6 further comprising: accumulating second metadata associated with a first plurality of intellectual knowledge files; extracting a first plurality of patterns from said first plurality of intellectual knowledge files; creating a first plurality of concept spaces, using rules-based processing, said first metadata, and said first plurality of patterns, wherein said plurality of existing concept spaces comprises said first plurality of concept spaces; and, grouping, using rules-based processing, said second metadata, and said first plurality of patterns, said first plurality of intellectual knowledge files into first respective concept spaces in said first plurality of concept spaces, wherein said steps of accumulating, extracting, creating, and grouping, are performed by said general-purpose computer.

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12. An information system as recited in claim 1 , wherein the knowledge manager includes: an interaction flow model; a rule base model; a constraint model; an optimization model; a conceptual model; a predictive model; and the ontology.

13. An information system as recited in claim 12 , wherein each of the interactive flow model and the ontology is in communication with the rule base model, the constraint model, and the optimization model, wherein the interactive flow model is configured to manage interaction flows with each of the rule base model, the constraint model, and the optimization model, and wherein interaction flows include a number of situations and each situation has a context description that contains event concepts that a situation of the number of situations requires to occur.

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2. The method of claim 1 , further comprising: identifying a reference script that includes reference phrases, wherein each of the phrases included in the reduced script is a reference phrase contained in the reference script.

3. The method of claim 2 , further comprising: associating each reference phrase in the reference script with reference speech assets that result from the associated phrase, when the phrase is spoken, recorded, and processed; matching the unfulfilled speech assets against the reference speech assets; and adding phrases associated with matched reference speech assets to the reduced script.

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5. The method of claim 1 , wherein said determining said relationship between said first and second character expressions further comprises: subtracting at least a portion of said first binary string from at least a portion of said second binary string; and determining said relationship based, at least in part, on a result of said subtraction.

6. The method of claim 5 , wherein said first character expression represents a first quantity and said second character expression represents a second quantity, and wherein said determining said relationship further comprises determining whether said first quantity is greater than said second quantity based, at least in part, on said result of said subtraction.

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11. A computer-implemented method for facilitating localization of linguistic assets of a virtual space, the method being performed by one or more processors configured to execute computer program modules, the method comprising: implementing, using the one or more processors, a first instance of a staging virtual space associated with a first locale, and a second instance of the staging virtual space being associated with a second locale, the first locale having first linguistic conventions, the second locale having second linguistic conventions, wherein the staging virtual space includes objects positioned within the staging virtual space; providing, using the one or more processors, the first instance of the staging virtual space and the second instance of the staging virtual space for presentation to a human translator; facilitating, using the one or more processors, editing of linguistic assets of the instances of the staging virtual space, the linguistic assets including a first linguistic asset associated with the first instance of the staging virtual space and a second linguistic asset associated with the second instance of the staging virtual space, the first linguistic asset corresponding to the second linguistic asset, the first linguistic asset or the second linguistic asset being editable by the human translator such that the first linguistic asset conforms to one or more of the first linguistic conventions and the second linguistic asset conforms to one or more of the second linguistic conventions; and implementing, using the one or more processors, a first instance of a production virtual space being associated with a first locale and including the first linguistic asset, and a second instance of a production virtual space being associated with the second locale and including the second linguistic asset, wherein the production virtual space includes objects at corresponding locations to the objects in the staging virtual space, and wherein the production virtual space includes additional logic controls, for users, over the objects compared to the staging virtual space.

13. The method of claim 11 , wherein at least one of the first linguistic conventions is different from a corresponding one of the second linguistic conventions.

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5. The system of claim 1 , wherein the error model providing for at least one suggested correction in the string corrects typographic errors corresponding to incremental user entries, and wherein the suggested corrections are derived by replacing one or more characters in the string resulting from phonetic substitutions.

1. A computer-implemented user-interface system for incrementally finding and presenting one or more items in response to keystrokes entered by a user on an input device having at least one layout of keys, each key having at least one corresponding alphanumeric symbol, the system comprising: a database stored in a computer memory, the database containing a catalog of items and corresponding descriptive terms that characterize the items, wherein the items include at least one of content items and data items; and a computer memory comprising instructions for causing a computer system to: determine a layout of keys present on the input device; receive a sequence of incremental keystrokes from the user; in response to each incremental keystroke of the sequence of incremental keystrokes, build a string corresponding to the sequence, each entry in the string having the alphanumeric symbol associated with the corresponding keystroke of the sequence of incremental keystrokes; in response to each incremental keystroke of the sequence of incremental keystrokes, map the string to the database to find the most likely items corresponding to the sequence of incremental keystrokes, the mapping being in accordance with a defined error model, the error model providing for at least one suggested correction in the string wherein at least one keystroke of the sequence of incremental keystrokes is replaced by at least one alphanumeric symbol, and wherein the at least one alphanumeric symbol that replaces the at least one keystroke of the sequence of incremental keystrokes includes an alphanumeric symbol associated with a keystroke other than the keystroke being replaced; and in response to each incremental keystroke of the sequence of incremental keystrokes, order and present the most likely items on a display device in accordance with defined ordering criteria such that the user-interface system receives ambiguous entries from the user and presents the most likely matching items in response to the entries.

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1. A method performed by a server device, of identifying whether a sequence of terms is a semantic unit, the method comprising: receiving, by a communication interface or an input device of the server device, the sequence of terms in a memory; calculating, by a processor of the server device, a first value representing a coherence of terms in the sequence; calculating, by the processor, a second value representing variation of context in which the sequence occurs; comparing, by the processor, the first value to a first threshold and the second value to a second threshold; identifying, by the processor, that the sequence is a semantic unit based at least in part on the first value satisfying the first threshold and the second value satisfying the second threshold; and outputting, by the communication interface or an output device of the server device, an indication that the sequence is a semantic unit based on identifying that the sequence is a semantic unit.

13. The method of claim 1 , further including: applying one or more rules to the sequence, and where identifying that the sequence is a semantic unit is further based at least in part on the application of the one or more rules.

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25. The party kit of claim 1 , wherein the party kit is a system for providing entertainment, the system comprising a display for displaying the one or more animated characters associated with the theme as a background of a scoring grid.

1. A party kit associated with a party theme, the party kit comprising: party gifts having at least one character thereon related to one or more animated characters associated with a theme, the one or more animated characters being displayed on one or more systems in a bowling center; and party supplies having the at least one character thereon which are related to the one or more animated characters displayed on the one or more systems in the bowling center.

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12. A method for setting up a callback for a party involved in a telephone call, the method comprising the steps of, once the telephone call has been placed on hold: listening in on audio of the telephone call by a speech analytics system to detect an event indicating to offer the callback to the party, the event comprising at least one of a word spoken by the party, a sound made by the party, and an emotion displayed by the party; and in response to detecting the event indicating to offer the callback to the party, interacting with the party by an interactive voice response system to obtain information from the party with respect to placing the callback to the party; and recording the information so that the callback can be placed to the party at a later time.

15. The method of claim 12 further comprising the step of providing the party with an approximate time the party is expected to remain on hold by the interactive voice response system.

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15. A method comprising: generating, by a computing system, a call through an application program interface (API); determining, by said computing system using said API in response to said call, an internal functionality of said computing system; presenting, by said computing system in response to said call, an user accessible content filter perspective user interface to a user; receiving, by said computing system from said user through said user accessible content filter perspective user interface, known user accessible input data associated with specified user accessible filter perspective content; determining, by a processor of said computing system in response to said known user accessible input data, a plurality of address space coordinates cross-referenced to said specified user accessible filter perspective content to be retrieved from a plurality of different storage sources simultaneously; simultaneously retrieving from a plurality of different storage sources, by said computing system in response to said plurality of address space coordinates, said specified user accessible filter perspective content; determining, by said computing system in response said known user accessible input data, a specified filtered perspective for viewing said specified user accessible filter perspective content retrieved from said plurality of different storage sources; and presenting, by said computing system to said user, said specified user accessible filter perspective content retrieved from said plurality of different storage sources using said specified perspective filter.

17. The method of claim 15 , wherein said receiving said known user accessible input data associated with said specified content comprises: discovering, by said computing system, content topics associated with said specified user accessible filter perspective content accessible by said user; receiving, by said computing system from said user, a selection for a first topic of said content topics; enabling, by said computing system for said user, access to a specified logical storage room representations comprising first content of said specified user accessible filter perspective content, wherein said first content is associated with said first topic; selecting, by said computing system in response to a user selection, an X coordinate, a Y coordinate, and a Z coordinate, wherein said X coordinate, said Y coordinate, and said Z coordinate are representative by a hierarchal direction to a transverse; selecting, by said computing system in response to a user selection, a perspective view for presenting said first content to said user; presenting, by said computing system to said user, said first content using said perspective view; and receiving, by said computing system from said user, a selection for updating or deleting said first content.

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1. A computer-implemented method of improving response time for a webpage when determining a response to user gestures relative to content located on the webpage, the computer-implemented method comprising: at a manipulation thread, receiving an input message associated with a touch input for a user gesture relative to content on a webpage, wherein the webpage comprises: at least one dependent region in which content is processed in response to a user gesture by a user interface thread that performs full hit testing; at least one independent region in which content is processed in response to a user gesture by an independent hit test thread; wherein the content that is associated with the independent region is associated with hit testing of a display tree associated with the webpage; wherein one or more declared values are associated with one or more properties for an element of the independent region; and wherein the element is associated with the declared one or more values; in response to the touch input, the manipulation thread sending notification of the input message associated with the touch input to an independent hit test thread rather than sending the notification to the user interface thread, wherein full hit testing by the user interface thread is bypassed for any input message associated with a touch input for the independent region; and the independent hit thread performing hit testing for any independent regions associated with the touch input by traversing at least a portion of the display tree, and once the hit test is performed, the independent hit test thread then notifying the manipulation thread to initiate one or more default touch behaviors associated with the touch input.

3. The method of claim 1 , wherein the default touch behaviors comprise enabling or disabling a pinch zoom manipulation.

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2. The method of claim 1 , wherein the candidate word set is based on user-inputted query keywords to a website.

1. A method of target word recognition, comprising: obtaining a candidate word set and corresponding characteristic computation data, the candidate word set comprising text data, and characteristic computation data being associated with the candidate word set; performing segmentation of the characteristic computation data to generate a plurality of text segments; combining the plurality of text segments to form a text data combination set; determining an intersection of the candidate word set and the text data combination set, the intersection comprising a plurality of text data combinations; determining a plurality of designated characteristic values for the plurality of text data combinations; determining, using a processor, a criterion, including: obtaining a training sample word set and sample characteristic computation data, the sample characteristic computation data comprising a plurality of sample words and designated characteristic values of the plurality of sample words; obtaining a sample text data combination set based on the plurality of sample words; determining a plurality of designated characteristic values of sample text data combinations in an intersection of the sample text data combination set and the training sample word set; and setting a threshold value of a designated characteristic value of a sample text data combination in the intersection as a part of the criterion; and based at least in part on the plurality of designated characteristic values for the plurality of text data combinations and according to at least the criterion, recognizing among the plurality of text data combinations, target words whose characteristic values fulfill the criterion.

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1. A method for detecting texts included in an image, comprising steps of: (a) an apparatus detecting or allowing other device to detect at least one text candidate in an inputted image, if acquired, by referring to feature values of pixels in the inputted image; (b) the apparatus classifying or allowing other device to classify (i) the detected text candidate as a strong text or a non-strong text by referring to a comparison result between a first threshold value and a first feature value of at least one pixel selected within a corresponding region where the detected text candidate is included or a value converted from the first feature value, and (ii) the text candidate classified as the non-strong text as a weak text or a non-text by referring to a comparison result between a second threshold value and a second feature value of at least one pixel selected within a corresponding region where the text candidate classified as the non-strong text is included or a value converted from the second feature value; and (c) the apparatus determining whether to classify or allow other device to classify the weak text as the strong text by referring to information on the strong text and information on the weak texts; wherein, at the step of (c), the apparatus reclassifies or allows other device to reclassify the weak text as strong text or non-text based on at least one degree of similarity between one or more respective characteristics of the weak text and the strong text.

12. The method of claim 1 , wherein the corresponding region where the detected text candidate is included is a bounding box region including the detected text candidate with an extra margin minimized.

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10. A non-transitory computer readable medium containing a computer program comprising instructions that when executed by a computer perform functions that are useful in learning a data format, the computer program comprising: instructions to input an initial description of a data format and a batch of data comprising data in a new data format not covered by the initial description, instructions to use the first description to parse the records in the data source, instructions to discard records in the input data that parse successfully, instructions to collect records that fail to parse, instructions to accumulate a quantity, M of records that fail to parse, instructions to return a modified description that extends the initial description to cover the new data, instructions to transform the first description, D into a second description D′ to accommodate differences between the input data format and the first description D by introducing options where a piece of data was missing in the input data and introducing unions where a new type of data was found in the input data; and instructions to use a non-incremental format inference system to infer descriptions for the aggregated portions of input data that did not parse using the first description D; and instructions to rank the parses by a metric that measures their quality and return only the top quantity k of the parses, wherein the metric comprises a triple: m=(e, s, c), where e is a quantity of errors, s is a quantity of characters skipped during Sync token recovery, and c is a quantity of characters correctly parsed.

11. The non-transitory computer readable medium of claim 10 , wherein a term with type R is a parse tree obtained from parsing the input data using a description D and wherein parsing a base type results in a string, an integer or an error, wherein a parse of a pair is a pair of representations, and a parse of a union is a parse selected from the group consisting of a parse of a first branch of the union or a parse of the second branch of the union.

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1. A method for providing caller identification information by a caller's mobile phone, the method comprising the steps of: when there is a request, by a caller, for an addition of a word or phrase after an input of a call recipient's phone number, receiving, by the caller's mobile phone, an input of the word or phrase from the caller; and when the caller inputs a request for making an outgoing call, generating, by the caller's mobile phone, an outgoing call message, which includes a caller's phone number, the recipient's phone number, and an input word or phrase, and sending the generated outgoing call message to the recipient's mobile phone, wherein receiving the input of the word or phrase from the caller comprises: upon generating an identifier to request addition of the word or phrase, distinguishing the recipient's phone number and the word or phrase and adding the identifier after the recipient's phone number; and receiving the input of the word or phrase from the caller, and continuously arranging the recipient's phone number, the identifier and the word or phrase in order and displaying the recipient's phone number, the identifier and the word or phrase on a single screen of the caller's mobile phone.

3. The method according to claim 1 , wherein, in the step of receiving the input of the word or phrase from the caller, the caller's mobile phone receives the input of the recipient's phone number as a phone number selected from a phonebook by the caller.

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12. A method comprising: generating, by a network device, a list of documents that violate a set of policy rules, the list of documents being generated based on previous searches of a client device performed by one or more of a plurality of search engines; causing, by the network device and by inputting a search term based on the set of policy rules, a search engine, of the plurality of search engines, to perform a search of a plurality of documents stored on the client device, the search engine being caused to perform the search on a periodic basis, when an application is closed in the client device, and when the client device stops receiving information input by a user via the application; analyzing, by the network device, search results output from the search engine against the set of policy rules, analyzing the search results including determining that the list of documents does not include at least one of the search results; determining, by the network device and in response to determining that the list of documents does not include the at least one of the search results, whether the plurality of documents includes confidential information based on analyzing the search results against the set of policy rules, a separate analysis of each document, of the plurality of documents, not causing at least one of the plurality of documents to be identified as including confidential information based on the analysis of the search results against the set of policy rules; generating, by the network device, one or more new policy rules when the plurality of documents includes the confidential information; and updating, based on generating the one or more new policy rules, the set of policy rules to formulate an updated set of policy rules, the updated set of policy rules including the one or more new policy rules.

16. The method of claim 12 , further comprising: updating the list of documents based on the determining that the plurality of documents includes the confidential information.

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1. A method for controlling a manner in which a software application accesses an application programming interface (API), wherein the software application executes on a computing device and the API is available on the computing device, and the method comprises: receiving, from the software application executing on the computing device, a request to access the API; extracting, from the API, first license information that identifies whether all software applications executing on the computing device are permitted to access the API; identifying, based on the first license information, that not all software applications executing on the computing device are permitted to access the API; and in response to identifying: extracting, from the software application, second license information that identifies whether the software application is permitted to access the API, determining, based on the second license information, that the software application is permitted to access the API, and in response to determining: granting the software application access to the API.

3. The method of claim 1 , wherein determining that the software application is permitted to access the API comprises utilizing a function, a key, and a text string associated with the software application.

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28. The computer and non-transitory computer-readable medium according to claim 26 , further comprising: automatically updating a list of constructs of a user interface screen associated with each selected computer with the created construct icon; and displaying the created construct icon in the updated list of constructs of the user interface screen of each selected computer.

26. The computer and non-transitory computer-readable medium according to claim 25 , the computer-readable medium further comprising: instructions for at least one of exporting or importing the created construct among selected client computers.

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12. The computer-implemented method of claim 6 further comprising applying a filter to the first script, wherein the filter excludes all or part of at least one of the one or more lines of source code.

6. A computer-implemented method under control of a computing device configured with specific computer-executable instructions, the computer-implemented method comprising: processing a network resource request that includes compiling a first script, the first script comprising a first one or more instructions that are compilable into computer-executable instructions; dividing the first script into at least a first portion comprising a first subset of the first one or more instructions and a second portion comprising a second subset of the first one or more instructions; calculating a first portion hash based at least in part on the first portion and a second portion hash based at least in part on the second portion; obtaining one or more chunk hashes, each of the one or more chunk hashes corresponding to a respective chunk stored in a data store, each respective chunk comprising computer-executable instructions; matching a first chunk hash of the one or more chunk hashes, wherein the first chunk hash corresponds to the first portion hash; obtaining a first chunk corresponding to the first chunk hash; compiling the second portion into a second chunk; assembling at least the first chunk and the second chunk into a set of computer-executable instructions corresponding to the first script; and transmitting the set of computer-executable instructions.

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15. The playback apparatus according to claim 11 , wherein the control unit comprises: an audio converting module, configured for converting the first character information in the first playback data into the first speech data.

16. The playback apparatus according to claim 15 , wherein the control unit further comprises: a translating module, configured for translating the first character information in the first playback data and providing the translated first character information to the audio converting module to allow the audio converting module to accordingly obtain the first speech data.

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5. The computer-implemented method of claim 1 , wherein generating the boolean query comprises: creating a string list, each element of the string list associated with the at least one token; generating a matrix having a first dimension and a second dimension, wherein the first dimension is associated with the string list and the second dimension is associated with the at least one category; setting each cell of the matrix equal to a first value if the string associated with the cell is in the category associated with the cell and a second value if not; determining a vector reflecting the presence of a token in the article; and determining the product of the vector and the matrix.

6. The computer-implemented method of claim 5 , further comprising if one of the at least one categories comprises an exclusion category, determining a complement of the vector for the exclusion category.

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53. A method of generating an audio prompt, the method comprising: at a unified messaging server, determining a language to use when generating an audio prompt; initializing an language in which the audio prompt is to be generated, wherein the unified messaging server generates text prompts and audio prompts, wherein the unified messaging server is in communication with at least an email server and a voice mail server; wherein the initializing the language comprises: obtaining a localization document, appropriate for the user, associated with the language, wherein the localization document contains rules that define a manner in which prompts are generated for the language; wherein the rules define which prompt file should be retrieved, from the email server or the voice mail server to be provided to a user in response to input sent from the user to the unified messaging server, to generate the prompt when at least one parameter representing user data input satisfies a particular rule; selecting, by applying the rules to the request received from the user, between text prompt files that apply to the email server and audio prompt files that apply to the voice mail server; based on the determined files, identifying prompt parameter settings associated with the audio prompt to be generated; providing information regarding the audio prompt to be generated, the language to use when generating the audio prompt, and the audio prompt parameter settings to a prompt module; and receiving a sequence of audio files from the prompt module, wherein the sequence of audio files represent the audio prompt.

55. The method of claim 53 , further comprising the steps of: parsing the localization document to generate a document object model (DOM) tree; and analyzing the DOM tree to determine files that are appropriate for the prompt.

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31. A method implemented by a processor associated with a server device, the method comprising: identifying, by a processor associated with the server device, a web page that includes a product; identifying, by a processor associated with the server device, a price or a product identification number associated with the product; assigning, by a processor associated with the server device, a confidence score to the price or the product identification number, the confidence score relating to a probability that the price or the product identification number is associated with the product; and determining, by a processor associated with the server device, whether to associate the price or the product identification number attribute with the product based on the assigned confidence score.

33. The method of claim 31 , further comprising: creating or supplementing, by a processor associated with the server device, a business listing based on the price or the product identification number and the product when the price or the product identification number is associated with the product.

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19. A method of protecting information in search queries using a search apparatus with a user interface that is configured for connection to a computer network, said network comprising a plurality of search engines on a plurality of servers, said method comprising: receiving a search query comprising a plurality of keywords; dividing the search query into a number of sub-queries, each sub-query comprising at least one of said keywords but less than all of said keywords; comparing each sub-query to an inhibited combinations list that includes a number of keywords from the search query; excluding sub-queries that are identified as inhibited combinations; submitting remaining sub-queries to different search engines such that each search engine receives less than all of said plurality of keywords such that private information from the search query is less discernible at any one of said search engines than if that search engine received more of said plurality of keywords of said search query; and generating an integrated search results based on a rank of hits included in individual search results returned from the different search engines in response to the submission of said sub-queries, in which hits that share rank are sorted based on a number of priority rules; and searching the integrated search results.

23. The method of claim 19 , further comprising calculating a co-occurrence probability for the keywords of each sub-query and excluding from use as a sub-query a specific number of keyword combinations having the lowest co-occurrence probability.

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5. A computer system, comprising: a display area adapted to display content to a user; at least one input device adapted to receive input from the user, wherein the at least one input device comprises: a near input device which allows the user to provide input when in close proximity to the display area; and a far input device which allows the user to provide input when the user is not in close proximity to the display area, wherein the far input device is different from the near input device; a context component adapted to generate context information based on the type of input device used by the user of the computer system; and a display interface component adapted to control the content displayed to the user by the display area based at least in part on the context information.

6. The computer system of claim 5 , wherein: the display area is further adapted to display content to the user in a first size when the user uses the near input device; and the display area is further adapted to display content to the user in a second size, larger than the first size, when the user uses the far input device.

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9. The method of claim 8 , wherein a reference picture index of the temporal merge candidate is 0.

8. The method of claim 1 , wherein a motion vector of the temporal merge candidate is a motion vector of a temporal merge candidate block within a temporal merge candidate picture, and a position of the temporal merge candidate block is determined depending on a position of the current prediction unit within an LCU.

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10. A system comprising: one or more computers; and one or more data storage devices having instructions stored thereon that, when executed by the computers, cause the computers to perform operations comprising: training each of a plurality of predictive models using training data, wherein the predictive models include two or more predictive models of a same type that are trained with different combinations of features of the training data; generating, for each of the plurality of trained predictive models, a respective score that represents an estimation of an effectiveness of the respective trained predictive model; receiving a request for a prediction that includes input data from a client system; in response to receiving the request for the prediction, selecting a first subset of the plurality of trained predictive models based on the respective scores of the trained predictive models in the first subset, wherein the plurality of trained predictive models includes the first subset and a second subset, each subset comprises at least one trained predictive model, the first subset and the second subset are disjoint sets, and the predictive models in the first subset have higher respective scores than predictive models that were not selected; obtaining a respective predictive output from only each of the selected predictive models in the first subset based on the request and using the input data; combining the predictive outputs to generate a result; and providing the result to the client system.

18. The system of claim 10 , wherein: receiving the request for the prediction that includes the input data comprises receiving the request for a predictive output of a certain type; and selecting the first subset of the plurality of trained predictive models based on the respective scores of the trained predictive models in the first subset comprises selecting the first subset of the plurality of trained predictive models using the certain type identified in the request.

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14. The system of claim 13 , wherein the temporal relationship is determined by analyzing log files.

13. The system of claim 12 , wherein the operations further comprise: determining the second search query is related to the first search query based on a temporal relationship between the first search query and the second search query.

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1. A method, comprising: dividing, via at least one automatic speech recognition component, at least one voice interaction into viewable panel-level segments; assigning, via the at least one automatic speech recognition component, a score to each of the panel-level segments, each score indicating a match accuracy between the panel-level segment and a corresponding expected text of the panel-level segment; and evaluating, via the at least one automatic speech recognition component, each score against a standard, the standard defining a required score for each of the panel-level segments to be declared as a match to their corresponding expected texts.

6. The method of claim 1 , comprising determining whether at least one agent has adequately followed at least one script by performing at least one of a following action: defining at least one score assigned by the at least one automatic speech recognition component; and reading by the at least one agent the at least one script to the at least one client, based at least in part on a comparison of data representing an actual duration of the at least one interaction to data representing an expected duration parameter associated with the at least one interaction.

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3. A method, according to claim 1 , wherein the identification of the first one of the authors includes a routing ID of the first one of the authors, wherein the routing ID facilitates locating the first one of the authors on the Internet.

4. A method, according to claim 3 , wherein the routing ID is an email address of the first one of the authors.

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7. The MMS decoder according to claim 6 , wherein the file parsing module comprises: a parsing processing unit configured for parsing an original MMS data packet, every time a file is acquired, determining a format of the file by checking a content-type field of the file and saving the file into a file body of a corresponding format; A non-SMIL file acquisition unit configured for acquiring all the non-SMIL files in a parsing result and counting the number of all the non-SMIL files as a first non-SMIL file number.

8. The MMS decoder according to claim 7 , wherein the format of the file comprises one or more formats selected from a group consisting of a text format, an image format, an audio format, a video format and an attachment format.

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16. The non-transitory computer-readable medium of claim 15 , further comprising code for generating one or more notifications indicative of a reason for the hold to an approver or reviewer based on a designated approver or reviewer established in a routing setup.

17. The non-transitory computer-readable medium of claim 16 , further comprising code for modifying the information representing the timesheet based on information submitted by an approver relative to the current approver in the sequence of approvers.

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9. The voice command recognition method of claim 7 , wherein the determining of context comprises calculating a distance from the user based on the time a voice reaches the audio sensors, and comparing magnitude of a difference between the voice and ambient noise, wherein the activating or the remaining inactive are based on the calculated distance and the difference between the voice and the ambient noise.

7. A voice command recognition method, comprising: receiving a voice at audio sensors placed at different locations of a device; determining user context based on the voice, wherein the context comprises vocalization from a user; determining whether the voice is from a specific pre-registered user; in response to a determination that the voice is from the pre-registered user, calculating a distance from the user based on the time the voice reaches the audio sensors, activating the device to recognize a command from the voice or remaining inactive based on the calculated distance.

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2. The document reading apparatus according to claim 1 , further comprising a receiving unit configured to receive, from the user through the screen displayed by the displaying unit, the instruction not to stop conveyance of the document by the conveying unit, even in the case where the detecting unit detects multi-feed of the document, wherein, in a case where the receiving unit has received the instruction, the stopping unit does not stop the conveyance of the document by the conveying unit, even in the case where the detecting unit detects multi-feed of the document, and wherein, in a case where the receiving unit has not received the instruction, the stopping unit stops the conveyance of the document by the conveying unit in the case where the detecting unit detects multi-feed of the document.

5. The document reading apparatus according to claim 2 , further comprising an obtaining unit configured to obtain a number of times the detecting unit detects multi-feed of a document, wherein, in a case where the receiving unit has not received the instruction and the number of times obtained by the obtaining unit has reached a predetermined number, the stopping unit does not stop the conveyance of the document by the conveying unit, even in the case where the detecting unit detects multi-feed of the document.

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19. The computer readable medium of claim 14 , wherein the first link comprises a code embedded within the TOC document which, when triggered, causes the target document to move to a position where the first anchor is visible.

14. A computer readable medium having computer executable instructions for creating a hyperlinked table-of-contents in a frameset, comprising: creating a first frame and a second frame within the frameset, the first frame comprising a TOC document that is created in response to section headings of a target document and the second frame comprising the target document, wherein the TOC document and the target document are different documents; the target document being directly editable and having a first selected heading identifying a first section in the target document and a second selected heading identifying a second section in the target document; wherein the first section and the second section in the target document are non-contiguous sections of the same document; wherein the frameset is self-updating such that changes to the target document are automatically reflected within the TOC document; scanning the target document to locate the first and second selected headings; creating a first hyperlink entry in the TOC document that includes a first link that is associated with a first anchor in the target document, wherein the first anchor is positioned proximate to the first selected heading, and wherein triggering the first link causes the target document to reveal at least a portion of the first selected heading in the second frame; creating a second hyperlink entry in the TOC document that includes a second link that is associated with a second anchor in the target document, wherein the second anchor is positioned proximate to the second selected heading, and wherein triggering the second link causes the target document to reveal at least a portion of the second selected heading in the second frame; and automatically updating the first hyperlink entry in the TOC document in response to an update event without direct manual intervention; wherein the update event comprises printing the TOC document.

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4. The method of claim 1 wherein identifying at least one executable file of the same specific category with an acceptable reputational score further comprises: examining categorization data pertaining to executable files in the database; and identifying categorization data in the database concerning at least one executable file with an acceptable reputational score, the identified categorization data comprising indications of extracted terms related to those of the executable file with the unacceptable reputational score.

5. The method of claim 4 wherein identifying at least one executable file of the same specific category with an acceptable reputational score, the identified categorization data comprising indications of extracted terms related to those of the executable file with the unacceptable reputational score, further comprises: applying at least one machine learning technique to categorization data corresponding to executable files in the database, to identify contextually related groups of terms pertaining to executable files with acceptable reputations.

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5. The computer program product of claim 4 wherein the plurality of instructions are further configured for (1) analyzing the derived features, and (2) determining the subject related to the received source data in response to the analysis of the received source data and the derived features.

4. The computer program product of claim 3 wherein the plurality of instructions are further configured for (1) analyzing the received source data, and (2) determining the subject related to the received source data in response to the analysis of the received source data.

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1. A computer-implemented expert system including: a memory for: storing a plurality of hypotheses, where the hypotheses are arranged in at least a first and second disjoint group of hypotheses, wherein the first group of hypotheses includes hypotheses that may be taken by the system and the second group includes hypotheses that may not be taken by the system; and storing questions for rejecting hypotheses of the second group; an output for supplying questions from the memory; an input for receiving initial data and answers to outputted questions; a processor programmed to: select questions from the stored questions for those hypotheses from the second group that are possible in dependence on the initial data; determine from at least one answer received in response to outputting the selected questions whether at least one of the hypotheses of the second group is possible by comparing received answers to a predetermined answer sequence stored in or derivable from the memory; and in response to determining that no hypothesis of the second group is possible, supply via the output a most likely hypothesis of the first group in dependence on at least the initial data, wherein the expert system is used for medical diagnosis, the initial data includes a health complaint, the second group of hypotheses containing medical diagnoses that require attention of a medical expert, the first group of hypotheses containing medical diagnoses that do not require any medical attention or can be cured or treated without assistance of a medical expert, and the processor is programmed to select the questions in dependence on the health complaint.

3. An expert system as claimed in claim 1 , wherein the processor is programmed to supply via the output an indication that no hypothesis can be made in response to determining that at least one hypothesis of the second group is possible.

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6. The method of claim 1 , wherein visually emphasizing the matched portions of the parsed text from the content in the end user application by visually differentiating the matched portions of the parsed text from the content further comprises adjusting a font size of text associated with the matched portions of the parsed text proportionately based upon the determined frequency of match to respective ones of the social bookmarks and associated metadata.

7. The method of claim 6 , wherein the font size of text being adjusted is greater for text associated with a high frequency of match to respective ones of the social bookmarks and associated metadata.

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1. A machine-implemented method for dynamically identifying and removing potential stopwords from a search query, the method comprising: receiving a search query comprising plural terms; identifying a possible stopword among the plural terms that appears in a predefined list of possible stopwords; determining one or more query splits by matching the search query to one or more query patterns, wherein each query pattern corresponds to a respective rule for interpreting the search query, wherein each query pattern is matched to the search query based on the respective rule, and wherein each query split comprises at least one of a location portion that uses one or more of the plural terms or a subject portion that uses one or more of the plural terms; removing the possible stopword from at least one of the location portion or the subject portion for at least one of the query splits based on a stopword removal rule, wherein the stopword removal rule specifies whether to remove the stopword from the location portion only, the subject portion only, or both the location portion and the subject portion; querying the search engine with the location portion and the subject portion for each of the one or more query splits, and respectively obtaining scored search results for each of the one or more query splits; and selecting the search results returned for at least one of the query splits to return as a response to the search query.

2. The method of claim 1 , wherein the selected search results correspond to a query split in which the possible stopword was removed.

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16. The method of claim 1 , wherein classifying the plurality of image components into a plurality of component types comprises identifying image components as selectively one of text, pictures, and graphics.

1. A method for modifying color in an image using component specific natural language color commands comprising: identifying an input image to be modified; segmenting the input image into a plurality of image components, the image components being discrete segments of the input image; assigning each of the plurality of image components a component identity based on content of the image component using pattern recognition, object recognition, and boundaries analysis; classifying the plurality of image components into a plurality of component types; receiving a component specific natural language color command for a color modification of the input image, the component specific natural language color command including a component type identifier and a color modifier; parsing the component specific natural language color command to obtain the component type identifier and the color modifier contained in the component specific natural language color command; identifying, without user input, the one or more component types corresponding to the parsed component type identifier; identifying all of the image components classified with each of the identified component types; attributing the color modifier to a predefined color space associated with each of the identified image components, the color modifier indicating the color modification to be performed; and applying the color modification to the identified image components to adjust the color of the identified image components.

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7. The method of claim 1 , in response to receiving an indication of a user moving the electronic pointer away from the displayed command object, causing the displayed command object to grow increasingly translucent as the distance between the electronic pointer and the displayed command object increases.

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.

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15. The computer-readable device of claim 14 , wherein optimizing the ranking model uses a SVM technique to minimize a regularized hinge loss function.

14. A computer-readable device storing code, which when executed, runs a method for improving a ranking model, the method comprising: obtaining a training set, wherein the training set includes: training queries; a document set associated with each training query; and document labels, from which at least one perfect and imperfect document set permutations can be inferred for each training query; initializing active sets to include a perfect document set permutation, and an imperfect document set permutation for each training query; optimizing a ranking model in accordance with an empirical loss function, wherein the empirical loss function utilizes the active sets; identifying, for each training query, the perfect document set permutation for which the ranking model scores lowest, and the imperfect document set permutation for which the ranking model scores highest; updating the active sets to include at least one identified document set permutation that was not initially present; and using the updated active sets to additionally optimize the ranking model.

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32. A computer readable medium having instructions for causing a computer to create an interactive graphic user interface (GUI) for providing a diagram of patent claims, the diagram comprising: an interactive graphical user interface (GUI) viewable on an electronic display, the GUI including a diagram of at least part of a patent claims series; wherein the claims are parsed hierarchically wherein the diagram comprises graphical claim structure and textual claim content associated with each patent claim and wherein, for each patent claim, the graphical claim structure fully includes the textual claim content, the textual content of each claim ending with a numerical representation of how many claims directly depend on that claim; and the claims, including both the graphical claim structure and the fully included textual claim content, are dynamically compressible hierarchically; wherein the graphical claim structure comprises multiple geometric outlines, each outline operable to fully contain the textual claim content of one claim, and at least one line directly connecting the outlines to each other according the hierarch of the at least part of a patent claims series; wherein at least one of the multiple geometric outlines further has a visual emphasis, the visual emphasis indicating whether there are additional levels of the hierarchy of the at least part of a patent claims series directly connected to the at least one of the multiple geometric outlines that has a visual emphasis.

41. The computer readable medium having instructions for causing a computer to create an interactive GUI of claim 32 , wherein the claims are displayed in html.

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1. A method, comprising: receiving a message that includes a document and an encrypted access code; in response to opening of the message, generating an image having embedded information that includes the encrypted access code; capturing a copy of the image at a portable device; decrypting the encrypted access code from the copy of the image using information stored in the portable device to yield a decrypted access code; and generating an output that includes the decrypted access code, the output allowing access to the document.

9. The method of claim 1 , further comprising: generating an encrypted passcode capable of being decrypted by the portable device, wherein the generating the image comprises including the encrypted passcode in the image.

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11. The method of claim 10 , further comprises: crawling one or more data sources by an agent operative on a computing device to collect the textual content from at least one data source accessible by a plurality of user nodes; performing phrase extraction from the textual content to generate phrases; and identifying the plurality of sentiment phrases and the plurality of non-sentiment phrases from the generated phrases; and storing the identified hidden connections and created term taxonomies in a data warehouse storage.

12. The method of claim 11 , wherein identifying the sentiment phrases and non-sentiment phrases further comprises: comparing each of the generated phrases to sentiment phrases and non-sentiment phrases stored in a phrases database; determining that a phrase is a sentiment phrase when a match is found between the phrase and at least a sentiment phrase in the phrase database; and determining a phrase is a non-sentiment phrase if a match is found between the phrase and at least a non-sentiment phrase in the phrase database.

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20. A non-transitory computer-readable storage medium storing processor-executable instructions for controlling a computing device, comprising: program code to cause an automated speech recognition (ASR) module, configured to convert audio data to text data, of the computing device to transform the audio data received by the computing device into a sequence of speech units represented in the audio data; program code to determine a first duration of the sequence of speech units of the received audio data; program code to determine, using the first duration, an expected duration of a single speech unit of the received audio data in the sequence of speech units; program code to determine, by the ASR module, a second duration of the single speech unit; program code to determine a duration score of the single speech unit, the duration score corresponding to the second duration in relation to the expected duration; and program code to determine a speech recognition result based at least in part on the duration score of the single speech unit, wherein the speech recognition result is the text data corresponding to the received audio data representing speech; and program code to cause a command to be executed using the text data.

24. The non-transitory computer-readable storage medium of claim 20 , in which the program code to determine the first duration comprises program code to exclude at least one speech unit in the sequence of speech units when determining the first duration.

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6. The apparatus of claim 1, wherein the document framework includes means for stacking one or more commands; and means for undoing the commands.

7. The apparatus of claim 6, wherein the means for undoing commands includes means for undoing commands in an order opposite from which the commands were stacked.

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1. A method comprising the steps of: receiving a request over a network from a user for data related to a context, wherein the request is a URL comprising a context query, wherein the context query comprises at least one context criteria; parsing and translating, via the network, the at least one context criteria, whereby the at least one context criteria is parsed and translated to a standardized format; disambiguating, via the network, the at least one parsed and translated context criteria, whereby the at least one parsed and translated context criteria is resolved to canonical values; formulating a network data query based on the at least one disambiguated context criteria so as to search, via the network, for user profile data, social network data, spatial data, temporal data and topical data that is available via the network and relates to the context query so as to identify at least one data object that relates to the at least one disambiguated context criteria; checking permissions, via the network, relating to the at least one data object to determine if the user is permitted to access the at least one data object; if the user is permitted to access the at least one data object, transmitting, over the network, a reference to the at least one data object over the network to the user, wherein the reference to the at least one data object contains sufficient information to enable the user to access the at least one data object over the network.

10. The method of claim 1 wherein the reference to the at least one data object is a hyperlink embedded in a webpage, wherein the webpage is transmitted to the user.

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13. The method according to claim 1 , wherein: said obtaining step is carried out using a voice input unit; said preprocessing is carried out using a pre-processing unit for pre-processing voice samples from voice input unit; said deriving and comparing steps are carried out using a main processing unit for processing said pre-processed voice samples and detecting emotional arousal therefrom; and said processing result is outputted to a main indicators output unit for outputting an indication of emotional arousal.

14. The method according to claim 13 , wherein said voice input unit of the apparatus includes a voice capturing unit and a voice sampling and digitizing unit coupled to said voice capturing unit for sampling and digitizing captured voice input.

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15. The set of computer-readable non-transitory storage media of claim 14 , wherein the first probability list is further generated based upon a set of concept nodes of the social graph that are connected to the set of user nodes through a second set of edges.

14. The set of computer-readable non-transitory storage media of claim 11 , wherein a first probability list of the plurality of probability lists is generated based upon known user attributes from a set of user nodes of the social graph that are connected to the user node through a set of edges.

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47. The computing device of claim 42 wherein the content rater component is further configured to, before the automatic generating of the at least one aggregate assessments of the content of the review, determine whether the received evaluations satisfy a content rating threshold, and wherein the automatic generating of the at least one aggregate assessments of the content of the review is performed only when it is determined that the received evaluations satisfy the content rating threshold.

42. A computing device for selecting information to provide to users based on reputations of evaluators of the information, comprising: one or more processors; a content rater component configured to, when executed by at least one of the one or more processors: receive from a reviewer user a review related to an item available from a Web merchant; receive evaluations of the review from each of multiple evaluator users, each received evaluation including a quantitative assessment of contents of the review for each of one or more of multiple content rating dimensions available for use in assessing the review, each of the evaluator users having a single existing reputation weight for the Web merchant based at least in part on previous evaluations supplied by that evaluator user for multiple other reviews for items available from the Web merchant; and automatically generate at least one aggregate assessment of the content of the review based at least in part on combining quantitative assessments from the received evaluations for the review, one or more of the generated aggregate assessments being further based on the single existing reputation weights of the evaluator users in such a manner that a first quantitative assessment from a first evaluator user with a first reputation weight has a different impact on that generated aggregate assessment than that first quantitative assessment from a distinct second evaluator user with a distinct second reputation weight; an evaluator reputation assessor component configured to automatically update the single existing reputation weights for each of one or more of the evaluator users for the Web merchant based on a relationship of the quantitative assessments from the evaluation of that evaluator user to the quantitative assessments from the evaluations of other of the evaluator users; and a content manager system configured to, when executed by at least one of the one or more processors, determine whether to provide the review to another user based at least in part on one or more of the automatically generated aggregate assessments for the content of the review.

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9. One or more computer-storage media embodying computer-useable instructions that, when employed by a computing device, cause the computing device to perform a method comprising: receiving a grammar usable by a search engine to route search queries to corresponding domains of information to find and return information for the search queries, the grammar comprising a plurality of rules, each rule comprising a sequence of token classes used to describe search queries, each token class comprising a logical grouping of tokens, each token comprising a string of one or more characters; parsing the grammar to identify the plurality of rules and token classes; eliminating, from the grammar, any duplicate rules identified from parsing the grammar; assigning a score to each rule indicative of an importance of each rule to the grammar, wherein the score for each rule is based at least in part on the frequency with which each rule corresponds with search queries contained in query logs; identifying one or more rules as important rules based on the one or more rules having a high score indicative of a high importance to the grammar; removing the one or more important rules from consideration for compression; identifying, from the token classes, two or more unimportant token classes that are eligible for compression and at least one important token class that is not eligible for compression; breaking at least one rule into a plurality of sub-rules based on important token classes, wherein each sub-rule includes a portion of the token classes from the at least one rule and each sub-rule begins and ends with an important token class and wherein a beginning token class and ending token class in each rule is treated as an important token class for purposes of breaking each rule into the plurality of sub-rules; identifying one or more sub-rules containing only important token classes; removing the one or more sub-rules containing only important token classes from consideration for compression; eliminating, from the grammar, any duplicate sub-rules identified; analyzing the plurality of sub-rules to identify at least one set of sub-rules as compression candidates; analyzing the unimportant token classes in the at least one set of sub-rules to identify two or more unimportant token classes for compression; merging the two or more unimportant token classes from the at least one set of sub-rules to generate a merged token class; substituting the merged token class in the grammar for the two or more unimportant token classes that were merged to generate the merged token class, wherein substituting the merged token class in the grammar for the two or more unimportant token classes that were merged to generate the merged token class comprises substituting the merged token class for all instances within the grammar of the two or more unimportant token classes that were merged to generate the merged token class; and eliminating any duplicate sub-rules and any duplicate rules after substituting the merged token classes in the grammar to generate a compressed grammar.

13. The one or more computer-storage media of claim 9 , wherein analyzing the plurality of sub-rules to identify the at least one set of sub-rules as compression candidates comprises identifying a set of two or more sub-rules that begin with the same token class as the other sub-rules in the set.

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1. In a computer-based system, a method of training a multi-category classifier using a binary SVM algorithm, said method comprising: calculating at least one feature vector for each of a plurality of training examples; transforming each of said at least one feature vectors using a first mathematical function so as to provide desired information about each of said training examples; building a SVM classifier for each one of a plurality of categories, calculating a solution for the SVM classifier for the first category using predetermined initial value(s) for said at least one tunable parameter; and testing said solution for said first category to determine if the solution is characterized by either over-generalization or over-memorization, wherein the SVM classifier is used on real world data, the probability of category membership of the real world data being output to at least one of a user, another system, and another process, wherein whether said SVM classifier solution for said first category is characterized by either over-generalization or over-memorization is based on a difference between a harmonic mean of said first and second estimated probabilities, on the one hand, and an arithmetic mean of said first and second estimated probabilities, on the other hand.

12. The method of claim 1 , wherein the calibration of SVM scores is performed using training examples allocated to a holdout set.

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18. An apparatus as claimed in claim 6, wherein the sets of code words belonging to each pair of coding states of the first type are disjunct.

6. An apparatus as claimed in claim 5, the sync word generator means being adapted to generate the following sync words: TBL ______________________________________ 1 11100011111110 6 2 10100011111110 6 3 01100011111110 6 4 01011100000001 1 5 10011100000001 1 6 00011100000001 1 7 11000100000001 1 ______________________________________ where the value in the first column indicates the coding state after having converted the information word directly preceding the sync word to be added, the bit sequence in the second column indicates the sync word generated in response to said coding state and the value in the third column indicates the coding state required to obtain the codeword directly following the sync word.

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1. A tool for implementing Korean characters using a consonant-vowel combination system, wherein the Korean characters can be implemented by combining six consonant and vowel plates including: letter plate “ ” formed to have a perpendicular shape, in which a horizontal length and a vertical length of an outer side are the same and a horizontal length and a vertical length of an inner side are the same; letter plate “ ” formed by symmetrically rotating letter plate “ ” and adding a horizontal length of letter plate “ ” to a bottom; letter plate “ ” formed to have a diameter that is the same as a vertical length of letter plate “ ”; letter plate “ ” formed by rotating letter plate “ ” clockwise into the shape of “ ,” disposing it at the upper side and adding the vertical length of the inner side of letter plate “ ” beneath “ ”, letter plate “ (short)” formed by rotating letter plate “ ” in a horizontal direction to be the same as a horizontal length of letter plate “ ”; and letter plate“ (long)” formed to have a total length of adding the horizontal length of letter plate “ ” and the horizontal length of letter plate “ ”.

2. The tool as claimed in claim 1 , wherein the consonant-vowel combination system is configured to have relational expressions of A=2B, C=A+B, and D=A+C by forming: letter plate “ ” such that the horizontal length and the vertical length of the outer side are formed to have the same length ‘A’ and width and height of an inner space and width of both ends of letter plate “ ” are formed to have the same length ‘B’, letter plate “ ” such that a horizontal length is formed to be the same as length ‘A’ of letter plate “ ” and a vertical length is formed to be the same as length ‘C’ by adding length ‘A’ and length ‘B’ of letter plate “ ”; letter plate “ ” to have a diameter that is the same as length ‘C’ of letter plate “ ”; letter plate “ ” such that a vertical length is formed to be the same as length ‘C’ of letter plate “ ” and width and length of a horizontal protrusion in a middle are formed to be the same as length ‘B’ of letter plate “ ”; letter plate “ (short)” such that a horizontal length is formed to be the same as length ‘C’ of letter plate “ ” and width is formed to be the same as length ‘B’ of letter plate “ ”; and letter plate“ (long)” such that length ‘D’ is formed by adding length ‘C’ of letter plate “ ,” “ ,” or “ (short)” to length ‘A’ of letter plate “ ” and width is formed to be the same as length ‘B’ of letter plate “ ”.

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10. The system of claim 9 , the preference component processes at least one of a user setting for a cost, a value, or a language preference.

11. The system of claim 10 , the preference component includes a model where a user assesses a parameter v, indicating a dollar value of receiving a correct answer to a question, and where a parameter c represents a cost of each query rewrite submitted to a search engine.

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8. An apparatus, comprising: a processor; and a memory storing instructions that, when executed by the processor, cause the processor to perform operations, the operations comprising: receiving an audio signal generated by a microphone from speech spoken by a user; recognizing, by the processor, a command prefix in the audio signal, the command prefix representing a spoken word in the speech that is recognized by the processor as preceding any voice command; identifying a next spoken word after the command prefix as the voice command; storing associations between different next spoken words and different vehicular commands; retrieving a vehicular command of the different vehicular commands that is associated with the next spoken word; and executing the vehicular command in response to the next spoken word after the command prefix.

11. The apparatus of claim 8 , wherein the operations further comprise displaying an event indicator.

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5. The method of claim 3 , wherein an input dataset comprises at least one data factor.

6. The method of claim 5 , wherein the data factor includes user data from an online publisher.

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7. The character recognition apparatus according to claim 1 , wherein the processor is configured to calculate the recognition score using a neatness of a character compared to a plurality of characters in each of the plurality of recognition result candidates.

1. A character recognition apparatus comprising: a processor configured to: extract a plurality of strokes from a recognition target; extract noise candidates from the plurality of strokes; generate a plurality of recognition result candidates obtained by removing at least one of the noise candidates from the recognition target; perform character recognition on the plurality of recognition result candidates and calculate recognition scores for each of the plurality of recognition result candidates; and use the recognition scores to specify a most likely recognition result candidate from the plurality of recognition result candidates as a recognition result.

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8. The system of claim 7 , wherein the receiving the user action input includes reading the location of a pointer on the editing screen display.

7. A computer hardware system, comprising: a web page authoring system having a user input system and an editing screen display for displaying a representation of a tag associated with a display artifact represented on the editing screen display, wherein, the web page authoring system is configured to perform: receiving a user action input selecting a reference point on the editing screen display for a web page being authored; setting a reference area on the editing screen display enclosing the selected reference point; selecting the display object closest to the reference point as a reference display artifact from among display artifacts in the reference area; selecting a tag associated with the reference display artifact from among tags associated with the display artifacts in the reference area; and drawing a first rectangle on the editing screen display artifact and a second, larger rectangle enclosing said first rectangle, a space between said first and second rectangles representing the selected tag, wherein the selected tag, associated with the first rectangle and the selected display object, includes an open tag and a corresponding close tag.

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1. A system comprising: a server computer configured to receive a search query from a client device, the search query comprising a text string; a parsing logic configured to parse the search query using parsing criteria based on one or more dictionaries to determine keywords associated with the search query, wherein the keywords associated with the search query include one or more portions of the text string; the server computer configured to search a database using the keywords associated with the search query and obtain a search result that includes a universal resource locator, wherein the server computer is configured to identify a plurality of the keywords in the universal resource locator, to modify the universal resource locator by inserting previously non-existing space between at least two of the plurality of identified keywords in the universal resource locator, to generate display data comprising the modified universal resource locator having the plurality of identified keywords and the inserted space therebetween, and to send the display data to the client device; wherein the server computer inserts the space in the universal resource locator by inserting a HTML tag between characters of the universal resource locator before sending the display data to the client device, wherein the HTML tag comprises at least one of a div tag, an italics tag, and a span tag.

6. The system of claim 1 , wherein the display data comprises at least 0.01 em of whitespace between the keywords.

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6. The method of claim 1 , wherein the plurality of context scores represent a general context, a historical-user-selection context, and a domain context, each of which is factored by a respective weight when combined in the statistical model, and wherein the respective weight of each term is customizable to tune the statistical model.

1. A method of re-formatting an input based on one or more contexts comprising: receiving the input that has been submitted to an application; identifying a plurality of outputs comprising possible suggestions for re-formatting the input; calculating a respective score of each output of the plurality of outputs by applying a statistical model to a respective combination of the input and each output, wherein a respective score of each output comprises a plurality of context scores that quantify a plurality of contexts of the respective combination of the input and each output; and wherein a context score of a context is calculated by applying a customizable weight assigned to the context to a frequency with which the input was previously re-formatted to the output when the context was applicable; selecting one or more suggested outputs from among the one or more outputs based on the respective scores; and providing the one or more suggested outputs as options to re-format the input.

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1. A machine-implemented method for analyzing a plurality of online social networks, the method comprising: receiving information about the plurality of online social networks, the information including messages, user identifiers, and relationship data regarding a plurality of actors of the plurality of online social networks; analyzing the received information, the analyzing further comprising: identifying available characteristics as defined by a plurality of online social network schemas, adding the identified available characteristics to a master actor ontology, determining a plurality of derived graph characteristics, adding the plurality of derived graph characteristics to the master actor ontology, determining a plurality of user behavior classifications, adding the plurality of user behavior classifications to the master actor ontology, determining a derived introversion/extroversion indicator for the plurality of actors based on a respective actor's attributes included in the master actor ontology, and adding the derived introversion/extroversion indicator to the master actor ontology; the method further comprising: inputting at least one logical expression representing at least one correlation of interest with respect to the master actor ontology; converting the at least one logical expression into at least one query in a standard graph query format; executing the at least one query over the master actor ontology to produce at least one query result; after executing the at least one query, determining additional derived values from the master actor ontology based, at least in part, on the at least one query result; and storing the additional derived values from the master actor ontology, wherein the method is performed by a plurality of processing devices, each of the plurality of processing devices performing processing on a respective portion of the master actor ontology.

24. The machine-implemented method of claim 1 , wherein the respective actor's attributes are one or more of an in-degree, an out-degree, a number of followers, a number following the actor, likes, or dislikes.

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6. The computer implemented method of claim 1 further comprising: receiving, by a client computing device, the customized virtual world having the set of portals responsive to the query; identifying, by the client computing device, a set of client virtual world software platforms installed at a data processing system associated with a user to form a set of available client platforms; rendering, by the client computing device, the customized virtual world with the set of portals; activating, by the client computing device, each portal in the set of portals associated with a platform in the set of available client platforms on the client computing device; and inactivating, by the client computing device, each portal in the set of portals associated with a platform that is absent from the set of available client platforms on the client computing device.

7. The computer implemented method of claim 6 wherein identifying a set of client virtual world software platforms further comprises: receiving, by the client computing device, a target rating for the customized virtual world; and identifying, by the client computing device, a set of client virtual world software platforms installed at a data processing system associated with the user to form the set of available client platforms, wherein each client virtual world software platforms in the set of client virtual world software platforms has a rating that is equal to or less than the target rating.

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28. The system of claim 20 , wherein the confidence score is further based on a location-probability distribution associated with the candidate place-entity.

29. The system of claim 28 , wherein: the location-probability distribution associated with the candidate place-entity comprises a point, the point corresponding to the particular geographic location of the candidate place-entity; and the confidence score is further based on a distance between the point and a geographic location of the mobile-client system.

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11. The auditory RRC-humanoid robot of claim 10 , wherein a TSM is programmed to repeat sounds spoken by a trainer-supervisor.

12. The auditory RRC-humanoid robot of claim 11 , wherein the TSM is programmed to accurately repeat all the words and sentences taken from a lexicon comprising at least 50,000 words that represents a total vocabulary of the robot.

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10. The method of claim 9 , wherein each value associated with each node in the overall set of nodes is a Boolean value.

9. A method of exposing commands in a document-centric application program executed by a computer, the method comprising: automatically displaying, by the computer, a window on a display device, the window generated by the document-centric application program, the document-centric application program operating at the computer, the window containing a work area and a controls area, the work area containing a document, the controls area not initially containing a context block; storing, by the computer, Hyper-Text Markup Language (HTML) code that specifies a title of the context block and a set of commands of the context block, the set of command executable by the document-centric application program, the title identifying a task, the set of commands useful to a user in accomplishing the task; storing a tree data structure at the computer, the tree data structure comprising an overall set of nodes, each node in the overall set of nodes being an independent data structure, the overall set of nodes including a root node and a set of child nodes, each node in the set of child nodes being a child of one other node in the overall set of nodes, the overall set of nodes comprising a set of leaf nodes and a set of non-leaf nodes, no node in the overall set of nodes being a child of any node in the set of leaf nodes, each node in the set of non-leaf nodes having at least one child node in the overall set of nodes, the root node not being a child of any node in the overall set of nodes, each node in the overall set of nodes associated with a value, each node in the overall set of nodes associated with a Boolean expression, the Boolean expressions associated with each of node in the set of non-leaf nodes taking as operands the values associated with each child node of the node, the set of leaf nodes including a first leaf node; ascertaining, at the computer, whether a change has occurred to selected text portions of the document, the selected text portions of the document being portions of the document selected using a cursor, the cursor controlled by a user, wherein the document is a document in which the user is working; in response to ascertaining that the change has occurred to the selected text portions of the document, making, at the computer, a change to the value associated with the first leaf node; in response to a change to the value associated with any non-root node, using, at the computer, the Boolean expression associated with a parent node to make a determination whether to change a value associated with the parent node, the non-root node being in the set of child nodes, the parent node being a parent of the non-root node; in response to making a determination to change the value associated with the parent node, changing, at the computer, the value associated with the parent node; in response to determining that the value associated with the root node has changed from a first value to a second value, automatically causing, at the computer, the controls area of the user interface to contain the context block, the context block containing the title of the context block and the set of commands of the context block, the computer causing the user interface to contain the context block independent of the user selecting any displayed menu item, the context block not obscuring the document, each command in the set of commands of the context block being selectable by the user, at least one command in the set of commands of the context block being selectable by the user to perform an action on the selected text portions of the document; in response to determining that the value associated with the root node has changed from the second value to the first value, automatically causing, at the computer, the user interface not to contain the context block.

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1. A machine-implemented method for determining a true geometry of a point of interest (POI), the method comprising: identifying, using one or more computing devices, a plurality of geometries associated with one or more points of interest according to one or more relationships; generating, using one or more computing devices, a candidate set of geometries for a first point of interest of the one or more points of interest, the candidate set of geometries including at least two candidate geometries that include geometries being associated with the first point of interest according to the one or more relationships; ranking, using one or more computing devices, the candidate geometries of the candidate set according to a ranking criteria, wherein the ranking sorts the candidate geometries according to the likelihood of the respective candidate geometry to be a true geometry of the first point of interest; selecting, using one or more computing devices, a first candidate geometry of the candidate geometries of the candidate set as the true geometry for the first point of interest according to the ranking; and storing, using one or more computing devices, the first candidate geometry in association with the first point of interest.

7. The method of claim 1 , wherein the ranking criteria includes an occupancy count for each candidate geometry of the candidate geometries, wherein the occupancy count indicates the number of points of interest of the one or more points of interest being associated with the candidate geometry by the one or more relationships.

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4. The method according to claim 3 , wherein each record of said node table further comprises a field for at least one simple property of the corresponding node, at least one simple property being capable of taking at most one value per node.

3. The method according to claim 1 , wherein said converting step further comprises the step of generating a node table with one record for each said node, each said record of said node table including a field containing the value of said unique node identifier allocated to said node.

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16. The computer readable medium of claim 15 , wherein the operations to deliver a visual cue comprise creating a pane display of the identified entity and the selected contextually relevant action.

15. The computer readable medium of claim 14 , wherein the operations to deliver the selected contextually relevant action to at least the second user comprise delivering a visual cue to at least the second user.

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2. The system of claim 1 , wherein the tiers comprise a web tier, a middleware tier, and a database tier.

1. A system comprising: a memory; a computer processor; a model engine executing on the computer processor and configured to: obtain a context managed entity from a client system; query a managed entity repository to obtain a plurality of managed entities linked to the context managed entity, wherein the plurality of managed entities comprises a software managed entity, a first hardware managed entity, and a second hardware managed entity; generate a node in a topology model object for each of the plurality of managed entities; and generate a plurality of link objects in the topology model object for a plurality of relationships between the plurality of managed entities, wherein the plurality of link objects comprises a containment link object; and a graphing engine executing on the computer processor and configured to: obtain the topology model object from the model engine; render a topology graph comprising each node and each link object in the topology model object by arranging each node into tiers; and expand the containment link object in the topology graph to show a containment link between the first hardware managed entity nesting as a hardware component within the second hardware managed entity.

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10. A parsing method, comprising: employing at least one processor configured to execute computer-executable instructions stored in memory to perform the following acts: parsing an input stream with one or more immutable parser configurations including an immutable stack and immutable lookahead buffer; and producing an immutable parse tree as a function of the parsing.

15. The method of claim 10 further comprising producing different versions of the stack and lookahead buffer that share common unchanged elements.

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12. The mobile device of claim 9 , wherein the object mask removes the at least one scene object.

9. The mobile device of claim 7 , wherein the AR model comprises an object mask.

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19. The computer-implemented method of claim 1 , further comprising providing, to a second user, a user interface comprising: a representation of the digital video; and a visual timeline representing the duration of the digital video and time locations of a plurality of annotations associated with the digital video.

20. The computer-implemented method of claim 19 , further comprising a user interface element for selectively displaying time locations of the plurality of associated annotations based on identities of users who added the annotations.

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16. The method of claim 15 , wherein the receiving comprises receiving the phonetic input on the input interface comprising an array of keys that are operable by a user, and wherein the indication comprises a representation of a selected one of the array of keys whose operation provides access to the further information.

15. The method of claim 13 , further comprising: controlling the display device to provide an indication that the further information relating to each of the displayed ideogrammatic representations is accessible for viewing by the user by operating an input interface.

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13. The system of claim 9 , wherein the source further comprises a tool, responsive to a processing of the meta-document, for generating and storing processing information and associated metadata on the meta-document.

9. A data processing system for managing document information comprising: a memory storing a meta-document for tracking and storing all information pertaining to actions performed by an application program on a document comprising document information on a computer-readable storage medium, the meta-document, wherein the meta-document comprises a file structure including: an object conveying document information, processing information pertaining to processing of the meta-document, and metadata for indexing and retrieving the processing information, wherein all of which are stored on the meta-document and retrievable from the meta-document; wherein the processing information comprises all information pertaining to each time the meta-document is processed by the application program being executed by the data processing system and any results of the processing during the entire life of the meta-document, the processing information being stored on the meta-document each time the meta-document is processed and being retrievable from the meta-document; and wherein the metadata comprises all associated metadata pertaining to each time the meta-document is processed by the application program being executed by the data processing system during the entire life of the meta-document, the metadata being stored on the meta-document each time the meta-document is processed and being retrievable from the meta-document; a processor for executing an application program for processing the meta-document; and a plurality of sources, each source located at a different location, wherein each time the meta-document is received by a source, processing information and its associated metadata is parsed and extracted from the meta-document at the source; and processing information pertaining to transmitting the meta-document to the source and parsing at the source and associated metadata stored on the meta-document, wherein the meta-document further comprises a first instruction, embedded on the object, responsive to processing of the meta-document, for generating and storing processing information and associated metadata on the meta-document, wherein the parsing step is performed by the first instruction, and wherein the meta-document farther comprises a second instruction, embedded on the object, for parsing and extracting selected processing information stored on the meta-document, and farther comprising the step of: parsing the meta-document for extracting the selected processing information and associated metadata; and distributing the extracted selected processing information to the source.

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23. The decoding method of claim 22 , wherein the linguistic model utilizes linguistic score calculation that applied on the encoding sequence combinations.

22. The decoding method of claim 17 , wherein selecting one of the encoding sequence combinations is determined by a linguistic model.

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8. The method of claim 7 wherein said content description data further comprises description information and keyword phrases.

7. The method of claim 5 wherein generating a plurality of content description pages comprises generating said content description pages using said content description data comprising said keywords and said at least a portion of said citation information for said publication and a template specifying the appearance and layout of said content description data.

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1. A method of facilitating text-to-speech conversion of a username, the method comprising: retrieving a name of a user associated with said username, said name comprising a first name of said user; determining a common or diminutive variation of said first name; and determining a pronunciation of said username based, at least in part, on whether said common or diminutive variation of said first name forms at least part of said username, and by calculating a likelihood of pronounceability of a portion of said username that is not said common or diminutive variation of said first name, wherein the method is performed by a computing device.

8. The method of claim 1 wherein said determining said pronunciation of said username further comprises, when said likelihood of pronounceability is high, generating a phonetic representation of said portion pronounced as a whole or generating a tokenized representation of said portion as a whole suitable for interpretation by a text-to-speech engine.

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18. The apparatus of claim 15 , wherein the one or more links include a link to an image, the mobile device further to download the image and format the image to fit a screen size of the mobile device.

15. An apparatus, comprising: a mobile device operative to: receive a condensed document, the condensed document having a link to a section of the document on a server, wherein the link indicates a file size of the section; download the section from the server; modify the downloaded section at the mobile device; and transmit the modified section to the server.

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19. The method of claim 18 , wherein the at least one user-specific characteristic also includes pitch and at least one formant of the utterance, and wherein the user-specific confidence threshold is also determined using a multiple regression calculation including the pitch and the at least one formant of the utterance, and a pitch coefficient and at least one formant coefficient developed from a plurality of development speakers.

20. The method of claim 19 , wherein the step (d) determination is carried out by setting the user-specific confidence threshold to a value that is relative to the calculated average or the multiple regression calculated value.

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4. The method of claim 1 , wherein the first domain comprises a first spectral dataset and the second domain comprises a second spectral dataset, wherein the first spectral dataset is different from the second spectral dataset.

1. A method for object detection in remotely-sensed images, the method comprising: identifying a first domain within a remotely-sensed image, wherein the first domain comprises textural features and spectral features; training a first classifier to detect a first object within the image based on a textural relationship between the textural features within the first domain; training a second classifier to detect the first object based on: the textural relationships within the first domain; and a spectral relationship between the spectral features within the first domain; learning a classifier relationship between the trained first classifier and the trained second classifier; training an object detector to detect at least one object based on the learned classifier relationship in the first domain; detecting the at least one object within a second domain using the trained object detector, wherein the second domain comprises textural and spectral features; comparing the detection of the at least one object in the first domain to the detection of the at least one object detected in the second domain; modifying the object detector based on the comparison between the detection of the at least one object in the first domain and the detection of the at least one object in the second domain; and detecting one or more objects in an additional domain with the modified optimal fast object detector.

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15. The system of claim 12 , wherein each light path expression in the one or more light path expressions is generated by concatenating a set of event descriptors in a string.

16. The system of claim 15 , wherein the string may include at least one of a wildcard symbol and an operator defined in a grammar for the light path expressions.

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17. The computing device of claim 14 , wherein determining whether to escalate from a row-level locking strategy to a page level locking strategy further comprises performing calculations regarding a particular database column referenced by the query.

14. A computing device, comprising: a processor; and a memory containing a program for optimizing the execution of a database query, which, when executed, performs an operation, comprising: receiving a query configured to be executed against a database containing data, wherein the query has been previously executed against the database, and wherein statistics reflecting a number of records required to execute the query were recorded during a prior execution of the query, prior to executing the query, analyzing the query to select a locking strategy to use in executing the query, the locking strategy being selected from at least two different locking strategies, wherein the locking strategy specifies which of the data to prevent other queries from accessing when the query is executed, wherein the analyzing comprises determining whether to escalate from a row level locking strategy to a page level locking strategy by evaluating: the number of records evaluated during execution of the query during the prior execution of the query; and an estimated number of records required to execute the query, upon determining at least one of the number of records and the estimated number of records exceeds a specified threshold, escalating to at least the page level locking strategy, upon determining neither the number of records nor the estimated number of records exceeds the specified threshold, selecting the row level locking strategy, and executing the query using the selected locking strategy.

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1. A computer implemented system for analyzing document content for display with reduced cognitive load, comprising: means for receiving a document for analysis; means for analyzing document content of the document; means for generating a set of salient words and phrases from the document content based upon the linguistic content of the words and phrases in the document; means for tagging the salient words and phrases in the set of salient words and phrases; and means for reading the set of salient words and phrases.

6. The system of claim 1 wherein said means for reading the document content further comprises means for reading the salient words and phrases.

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15. A system for identifying interesting characteristics within a collection of information, the system comprising: at least one processor operatively connected to a memory, the processor configured to execute system engines from the memory; an analysis engine configured to analyze one or more groups from the collection of information, wherein the analysis engine is further configured to determine automatically at least one identifying characteristic within a collection of information; a measurement engine configured determine a measurement of distinctiveness one or more groups from the collection of information based on a statistical distribution of the at least one identifying characteristic; a normalization engine configured to normalize the measurement of distinctiveness to account for a size of a group of the one or more groups and a size of at least one other group by determining an amount by which the measurement of distinctiveness exceeds a mean score for one or more groups of a similar or identical size; and an organization engine configured organize the one or more groups based on the measurement of distinctiveness.

18. The system according to claim 15 , wherein the measurement engine is further configured to determine the measurement of distinctiveness based on a calculation across multiple groups.

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11. A system for assigning text to a record from an image of the record, comprising: one or more processors; and a memory, the memory storing instructions that are executable by the one or more processors and configure the system to: obtain a scanned image of a record; determine at an optical character recognition system that at least some words in the scanned image are unidentified; evaluate the record image in order to locate each of multiple word images corresponding to the unidentified words; for each located word image, identify multiple word features of that word image; assign each of the multiple word images that have similar word features to one of a plurality of word clusters; select a representative word image in each of the word clusters as a centroid; receive, from an analyst, the centroid in each of the word clusters, and corresponding data representing text for the centroid; and assign the representing text for the centroid to all other word images in the same word cluster as the centroid.

17. The system of claim 11 , wherein each word image is assigned to a word cluster based on its feature vector, by: calculating a distance between each one of the multiple word images and every other one of the multiple word images, based on feature vectors associated with those word images; selecting, from among the multiple word images, two of the word images that are closest in distance to each other; and assigning the two of the word images to the word cluster.

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1. A method comprising: receiving a report from a target user of a social networking system identifying malicious activity performed on the social networking system and identifying an object maintained by the social networking system associated with the malicious activity; identifying objects connected to the target user through the social networking system and disabled by the social networking system; retrieving information describing a type of remedial action taken by the social networking system to disable one or more of the identified objects connected to the target user; calculating, by a computer processor, a disabled connectivity score for the target user based at least in part on the retrieved information, the disabled connectivity score indicating a trustworthiness of the report from the target user; and performing an action affecting the object identified by the report based on the calculated disabled connectivity score.

3. The method of claim 1 , wherein performing the action affecting the report based on the calculated disabled connectivity score comprises: comparing the calculated disabled connectivity score with a plurality of ranges of disabled connectivity scores, each range of disabled connectivity scores associated with an action; and selecting an action corresponding to a range of disabled connectivity scores that includes the calculated disabled connectivity score.