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8,943,487 | 5 | 7 | 5. A system comprising: a memory comprising instructions executable by one or more processors; and the one or more processors coupled to the memory and operable to execute the instructions, the one or more processors being operable when executing the instructions to: use a source code-to-source code rewrite to optimize a C++ function comprising one or more loops for symbolic execution by: for a first loop of the one or more loops, the first loop comprising a first branching condition, performing semantics preserving source code-to-source code rewrite to convert the first loop to move the first branching condition outside the loop; and for a second loop of the one or more loops, the second loop comprising a second branching condition, rewriting the second loop to include a symbolic variable representing a return value and to replace the second loop with an equivalent logical formula relating the symbolic variable with inputs of the C++ function; and wherein: the C++ function comprises a C++ library function, the C++ function further comprises one or more conditional branches, using the source code-to-source code rewrite to optimize the C++ function comprises using a hack-in function to access the symbolic executor to move the exploration of the multiple paths from within the symbolic executor to within the solver, and using the source code-to-source code rewrite for the first loop and the second loop moves the exploration of multiple paths from within a symbolic executor to within a solver. | 5. A system comprising: a memory comprising instructions executable by one or more processors; and the one or more processors coupled to the memory and operable to execute the instructions, the one or more processors being operable when executing the instructions to: use a source code-to-source code rewrite to optimize a C++ function comprising one or more loops for symbolic execution by: for a first loop of the one or more loops, the first loop comprising a first branching condition, performing semantics preserving source code-to-source code rewrite to convert the first loop to move the first branching condition outside the loop; and for a second loop of the one or more loops, the second loop comprising a second branching condition, rewriting the second loop to include a symbolic variable representing a return value and to replace the second loop with an equivalent logical formula relating the symbolic variable with inputs of the C++ function; and wherein: the C++ function comprises a C++ library function, the C++ function further comprises one or more conditional branches, using the source code-to-source code rewrite to optimize the C++ function comprises using a hack-in function to access the symbolic executor to move the exploration of the multiple paths from within the symbolic executor to within the solver, and using the source code-to-source code rewrite for the first loop and the second loop moves the exploration of multiple paths from within a symbolic executor to within a solver. 7. The system of claim 5 , wherein the C++ function further comprises one or more conditional branches, and the one or more processors are further operable when executing the instructions to: for each conditional branch, construct a symbolic expression using an intermediate language to delay interpretation of the C++ function to the conditional branch. | 0.501408 |
8,984,390 | 21 | 27 | 21. A method comprising: receiving first input indicating that a user intends to capture a screenshot of a graphical user interface window; in response to the first input, providing the user with an interface for manipulating the screenshot; identifying a plurality of predefined regions of the graphical user interface window; receiving second input indicating a screenshot region, wherein the screenshot region is an arbitrary user-defined region of the graphical user interface; receiving, via the interface for manipulating the screenshot, final input indicating that the user is ready to take the screenshot; determining that screenshot region overlaps a first plurality of pre-defined regions, and that a portion of the first plurality of pre-defined regions is outside of the screenshot region; responsive to the final input and the determining, expanding the screenshot region to include the first plurality of pre-defined regions; based on the expanded screenshot region, generating an image of the first plurality of pre-defined regions of graphical user interface window; wherein the method is performed by one or more computing devices. | 21. A method comprising: receiving first input indicating that a user intends to capture a screenshot of a graphical user interface window; in response to the first input, providing the user with an interface for manipulating the screenshot; identifying a plurality of predefined regions of the graphical user interface window; receiving second input indicating a screenshot region, wherein the screenshot region is an arbitrary user-defined region of the graphical user interface; receiving, via the interface for manipulating the screenshot, final input indicating that the user is ready to take the screenshot; determining that screenshot region overlaps a first plurality of pre-defined regions, and that a portion of the first plurality of pre-defined regions is outside of the screenshot region; responsive to the final input and the determining, expanding the screenshot region to include the first plurality of pre-defined regions; based on the expanded screenshot region, generating an image of the first plurality of pre-defined regions of graphical user interface window; wherein the method is performed by one or more computing devices. 27. The method of claim 21 , further comprising at least one of: placing the image in a buffer or saving the image to a file. | 0.897541 |
8,903,759 | 23 | 32 | 23. A system that performs an action in response to a capture of data from a printed document, comprising: a hand-held device, the hand-held device comprising a data capture component that stores captured text, the captured text being a text transcription of an image of human-perceptible textual data captured from the printed document and a context component that determines context information that is current at a time of the data capture; and an action component that receives the captured textual data and the determined context information to identify an electronic counterpart to the printed document from the captured text, wherein the printed document has one or more context points, each of the context points is a visible feature of the printed document, each of the context points is associated in the electronic counterpart with an action, and each of the context points has a location within the printed document, and that performs an action based on the received captured data and the received context information by determining a location within the printed document from which the captured text was captured, and by determining distances between the location of the captured text and context points within the printed document; and wherein the action is performed based at least in part on the determined distances. | 23. A system that performs an action in response to a capture of data from a printed document, comprising: a hand-held device, the hand-held device comprising a data capture component that stores captured text, the captured text being a text transcription of an image of human-perceptible textual data captured from the printed document and a context component that determines context information that is current at a time of the data capture; and an action component that receives the captured textual data and the determined context information to identify an electronic counterpart to the printed document from the captured text, wherein the printed document has one or more context points, each of the context points is a visible feature of the printed document, each of the context points is associated in the electronic counterpart with an action, and each of the context points has a location within the printed document, and that performs an action based on the received captured data and the received context information by determining a location within the printed document from which the captured text was captured, and by determining distances between the location of the captured text and context points within the printed document; and wherein the action is performed based at least in part on the determined distances. 32. The system of claim 23 , wherein the action comprises scrolling to the captured text in the electronic counterpart. | 0.880522 |
5,454,046 | 27 | 30 | 27. A system for the symbolic-based recognition of a handwriting input to an input surface having an output provided as a stream of coordinate data points, comprising: a data base memory storing a compilation of text-pattern pairs, said pattern of each said pair having been derived as sample features of said handwriting and including an associated sample index derived as aspects of said handwriting sample features, said text of each said pair representing at least one predetermined character glyph; a processor, responsive to said output to extract pattern test features and a corresponding test index therefrom, responsive to access said memory stored compilation and identify a said sample index corresponding with said test index, responsive to carry out a comparison of said pattern test features with said pattern sample features associated with said identified sample index, responsive in the presence of a correspondence between said pattern test and sample features to derive output signals corresponding with said text associated with said pattern sample features, wherein said pattern sample and test features include stroke based features derived from each stroke defining sequence of said data points and comprising: a first stroke feature provided as a value corresponding with the average worm moment based moment of stroke, a second stroke feature provided as a value corresponding with the aspect ratio of a stroke, said stroke aspect ratio generally being determined to be K/.pi.* ARCTAN (stroke-height/stroke-width) where K is a constant, and a third stroke feature provided as a value corresponding with the beginning and ending locations of a stroke relative to a dimension of a word within which such stroke is incorporated, said third stroke feature for the general case being expressed as: x.sub.0 =K (x.sub.b -x.sub.min)/(x.sub.max -x.sub.min) and x.sub.1 =K (x.sub.e -x.sub.min)/(x.sub.max x.sub.min), y.sub.0 =K (y.sub.b -y.sub.min)/(y.sub.max y.sub.min) and y.sub.1 =K (y.sub.e -y.sub.min)/(x=y.sub.max y.sub.min), where x.sub.0, y.sub.0 are the stroke's respective beginning x- and y- coordinate features, x.sub.1, y.sub.1 are the strokes respective ending x- and y- coordinate features x.sub.b, y.sub.b are the stroke's beginning respective x- and y- coordinate values, x.sub.min, y.sub.min are the word's minimum respective x- and y- coordinates, x.sub.max, y.sub.max are the word's maximum respective x- and y- coordinates, and x.sub.e, y.sub.e are the stroke's respective x- and y- ending coordinate values; and display means responsive to said derived output signals for effecting the publication of a said predetermined character glyph sequence of said text associated with said pattern sample features. | 27. A system for the symbolic-based recognition of a handwriting input to an input surface having an output provided as a stream of coordinate data points, comprising: a data base memory storing a compilation of text-pattern pairs, said pattern of each said pair having been derived as sample features of said handwriting and including an associated sample index derived as aspects of said handwriting sample features, said text of each said pair representing at least one predetermined character glyph; a processor, responsive to said output to extract pattern test features and a corresponding test index therefrom, responsive to access said memory stored compilation and identify a said sample index corresponding with said test index, responsive to carry out a comparison of said pattern test features with said pattern sample features associated with said identified sample index, responsive in the presence of a correspondence between said pattern test and sample features to derive output signals corresponding with said text associated with said pattern sample features, wherein said pattern sample and test features include stroke based features derived from each stroke defining sequence of said data points and comprising: a first stroke feature provided as a value corresponding with the average worm moment based moment of stroke, a second stroke feature provided as a value corresponding with the aspect ratio of a stroke, said stroke aspect ratio generally being determined to be K/.pi.* ARCTAN (stroke-height/stroke-width) where K is a constant, and a third stroke feature provided as a value corresponding with the beginning and ending locations of a stroke relative to a dimension of a word within which such stroke is incorporated, said third stroke feature for the general case being expressed as: x.sub.0 =K (x.sub.b -x.sub.min)/(x.sub.max -x.sub.min) and x.sub.1 =K (x.sub.e -x.sub.min)/(x.sub.max x.sub.min), y.sub.0 =K (y.sub.b -y.sub.min)/(y.sub.max y.sub.min) and y.sub.1 =K (y.sub.e -y.sub.min)/(x=y.sub.max y.sub.min), where x.sub.0, y.sub.0 are the stroke's respective beginning x- and y- coordinate features, x.sub.1, y.sub.1 are the strokes respective ending x- and y- coordinate features x.sub.b, y.sub.b are the stroke's beginning respective x- and y- coordinate values, x.sub.min, y.sub.min are the word's minimum respective x- and y- coordinates, x.sub.max, y.sub.max are the word's maximum respective x- and y- coordinates, and x.sub.e, y.sub.e are the stroke's respective x- and y- ending coordinate values; and display means responsive to said derived output signals for effecting the publication of a said predetermined character glyph sequence of said text associated with said pattern sample features. 30. The system of claim 27 in which said pattern sample and test features include signature verification features derived from each word defining sequence of said data points and comprising: a first signature verification feature provided as a value corresponding with the relative interval of time taken to write the word. | 0.797365 |
9,626,447 | 1 | 9 | 1. A non-transitory computer-readable recording medium having recorded thereon a browser program running on a computer including a storage unit that stores a table showing correspondences between text languages of web pages and character strings used in URLs to indicate the respective text languages, the browser program causing the computer to perform: a receiving step of receiving a designation of a URL; an acquiring step of acquiring information indicating a text language designated by a user; a first searching step of searching for a top-level domain “com” or the top-level domain “com” with a slash “/” added to the end of the designated URL; a determining step of determining whether or not the designated URL includes, at the end thereof, the top-level domain “com” or the top-level domain “com” with a slash “/” added to the end thereof; a second searching step of, when the determining step determines that a top-level domain “com” or the top-level domain “com” with a slash “/” has been added to the end of the designated URL, acquiring source code of a web page indicated by the designated URL, and searching the acquired source code for a URL including a character string corresponding to the designated text language with reference to the table stored in the storage unit; and a display control step of, when the URL including the character string corresponding to the designated text language is found in the second searching step, acquiring a web page indicated by the found URL from a web server over a network and displaying the acquired web page indicated by the found URL, and, when the URL including the character string corresponding to the designated text language is not found in the second searching step, displaying the web page indicated by the designated URL according to the acquired source code. | 1. A non-transitory computer-readable recording medium having recorded thereon a browser program running on a computer including a storage unit that stores a table showing correspondences between text languages of web pages and character strings used in URLs to indicate the respective text languages, the browser program causing the computer to perform: a receiving step of receiving a designation of a URL; an acquiring step of acquiring information indicating a text language designated by a user; a first searching step of searching for a top-level domain “com” or the top-level domain “com” with a slash “/” added to the end of the designated URL; a determining step of determining whether or not the designated URL includes, at the end thereof, the top-level domain “com” or the top-level domain “com” with a slash “/” added to the end thereof; a second searching step of, when the determining step determines that a top-level domain “com” or the top-level domain “com” with a slash “/” has been added to the end of the designated URL, acquiring source code of a web page indicated by the designated URL, and searching the acquired source code for a URL including a character string corresponding to the designated text language with reference to the table stored in the storage unit; and a display control step of, when the URL including the character string corresponding to the designated text language is found in the second searching step, acquiring a web page indicated by the found URL from a web server over a network and displaying the acquired web page indicated by the found URL, and, when the URL including the character string corresponding to the designated text language is not found in the second searching step, displaying the web page indicated by the designated URL according to the acquired source code. 9. The non-transitory computer-readable recording medium according to claim 1 , wherein the computer is included in an image forming apparatus. | 0.954371 |
6,122,628 | 52 | 53 | 52. A computerized method of representing multidimensional data which includes spatial geographic information, said method comprising the steps of: a) partitioning the spatial geographic information into one or more clusters; b) generating and storing clustering information for said one or more clusters; c) generating one or more reduced dimensionality clusters and dimensionality reduction information for said one or more clusters; d) storing the dimensionality reduction information; e) creating a hierarchy of reduced dimensionality clusters by recursively applying said steps a) through d); and f) generating and storing one or more low-dimensional indexes for clusters at a lowest level of said hierarchy. | 52. A computerized method of representing multidimensional data which includes spatial geographic information, said method comprising the steps of: a) partitioning the spatial geographic information into one or more clusters; b) generating and storing clustering information for said one or more clusters; c) generating one or more reduced dimensionality clusters and dimensionality reduction information for said one or more clusters; d) storing the dimensionality reduction information; e) creating a hierarchy of reduced dimensionality clusters by recursively applying said steps a) through d); and f) generating and storing one or more low-dimensional indexes for clusters at a lowest level of said hierarchy. 53. The method of claim 52, wherein the geographic information includes image data, further comprising the steps of: extracting a set of features from the image data, wherein the features include one or more of: a texture; a shape; a contour; and a color histogram. | 0.938856 |
6,061,749 | 20 | 21 | 20. A method of normalising first data according to claim 17, further including the step of supplying control signals from a control unit to the FIFO buffer, latches and normalising circuit, and from an external source to the control unit. | 20. A method of normalising first data according to claim 17, further including the step of supplying control signals from a control unit to the FIFO buffer, latches and normalising circuit, and from an external source to the control unit. 21. A method of normalising first data according to claim 20, wherein the control signals include a normalisation factor supplied to the normalising circuit when the first and second data words comprise packed bytes, the normalisation factor indicating the size of the data objects within the data word. | 0.841195 |
7,969,457 | 3 | 4 | 3. An apparatus as described in claim 2 wherein the power element is a self-powered element. | 3. An apparatus as described in claim 2 wherein the power element is a self-powered element. 4. An apparatus as described in claim 3 wherein the activation element includes an activation button. | 0.960016 |
8,050,908 | 1 | 5 | 1. A method for converting a context-free grammar to a finite-state automaton representing the context-free grammar, the method comprising: generating a first finite-state automaton from a set of rules associated with the context-free grammar; generating, from the first finite-state automaton, at least one second finite-state automaton; receiving a topology that defines an application of the context-free grammar; generating a third finite-state automaton that represents the received topology; and expanding the third finite-state automaton based on the at least one second finite-state automaton. | 1. A method for converting a context-free grammar to a finite-state automaton representing the context-free grammar, the method comprising: generating a first finite-state automaton from a set of rules associated with the context-free grammar; generating, from the first finite-state automaton, at least one second finite-state automaton; receiving a topology that defines an application of the context-free grammar; generating a third finite-state automaton that represents the received topology; and expanding the third finite-state automaton based on the at least one second finite-state automaton. 5. The method of claim 1 , wherein generating the third finite-state automaton comprises: defining at least one initial state based on the topology; defining at least one final state based on the topology; and defining a plurality of edges representing active non-terminal symbols based on the topology, each edge labeled with one of the active non-terminal symbols and extending from at least one of an initial state and a final state of the third finite-state automaton. | 0.611842 |
8,689,175 | 1 | 4 | 1. A computer-implemented system comprising: a communications module to receive an input, the input specifying a rule expressed in a custom syntax, the custom syntax provided by a rules authoring system; and a translator to translate the rule expressed in the custom syntax into a translated rule, using at least one processor, the translated rule being in a form of a source code suitable for being compiled into an executable module, the translator comprising: a parser to parse the rule expressed in the custom syntax, a validator to utilize one or more validation rules to validate parameters associated with the rule, a validation rule from the one or more validation rules authored using the custom syntax, and a resource generator to extract resources associated with the rule and to process the resources into a format suitable for a runtime environment, the resources comprising one or more keywords; a compiler configured to compile the translated rule into a compiled rule; and a deployment module to publish the compiled rule to the runtime environment. | 1. A computer-implemented system comprising: a communications module to receive an input, the input specifying a rule expressed in a custom syntax, the custom syntax provided by a rules authoring system; and a translator to translate the rule expressed in the custom syntax into a translated rule, using at least one processor, the translated rule being in a form of a source code suitable for being compiled into an executable module, the translator comprising: a parser to parse the rule expressed in the custom syntax, a validator to utilize one or more validation rules to validate parameters associated with the rule, a validation rule from the one or more validation rules authored using the custom syntax, and a resource generator to extract resources associated with the rule and to process the resources into a format suitable for a runtime environment, the resources comprising one or more keywords; a compiler configured to compile the translated rule into a compiled rule; and a deployment module to publish the compiled rule to the runtime environment. 4. The system of claim 1 , wherein the runtime environment comprises a rules engine associated with a business application. | 0.519531 |
7,676,358 | 1 | 6 | 1. A method to process a document, comprising: partitioning, with a tokenizer operating on at least one computer, document text separated by spaces into a plurality of tokens based on the spaces; identifying, with a token processing unit operating on at least one computer, tokens to be ignored and not considered; determining, with the token processing unit, that a first token considered of the plurality of tokens comprises a chemical name fragment, wherein determining comprises: examining syntax of the first token, examining context of the first token with respect to at least one adjacent token of the plurality of tokens, and taking into account the syntax and the context, applying to the first token a plurality of regular expressions, rules, and a plurality of dictionaries comprised of a prefix dictionary, and a suffix dictionary to recognize the chemical name fragments; adding, with the token processing unit, the recognized chemical name fragment to a vector of chemical name fragments, where the chemical name fragment is identified by a vector index variable; combining, with the token processing unit, the recognized chemical name fragment with at least one of the adjacent tokens that are determined to be a chemical name fragment into a complete chemical name, where combining comprises: initializing the chemical name fragment vector index variable, incrementing the chemical name fragment vector index variable, where the incrementing continues at least until no chemical name fragments remain; setting a string combination to include the chemical name fragments identified by the initialized and incremented chemical name fragment vector index variables, and adding the string combination to a vector c as the complete chemical name; assigning, with a sentence parser unit operating on at least one computer, the complete chemical name with one part of speech; and storing in a memory the complete chemical name assigned with the one part of speech. | 1. A method to process a document, comprising: partitioning, with a tokenizer operating on at least one computer, document text separated by spaces into a plurality of tokens based on the spaces; identifying, with a token processing unit operating on at least one computer, tokens to be ignored and not considered; determining, with the token processing unit, that a first token considered of the plurality of tokens comprises a chemical name fragment, wherein determining comprises: examining syntax of the first token, examining context of the first token with respect to at least one adjacent token of the plurality of tokens, and taking into account the syntax and the context, applying to the first token a plurality of regular expressions, rules, and a plurality of dictionaries comprised of a prefix dictionary, and a suffix dictionary to recognize the chemical name fragments; adding, with the token processing unit, the recognized chemical name fragment to a vector of chemical name fragments, where the chemical name fragment is identified by a vector index variable; combining, with the token processing unit, the recognized chemical name fragment with at least one of the adjacent tokens that are determined to be a chemical name fragment into a complete chemical name, where combining comprises: initializing the chemical name fragment vector index variable, incrementing the chemical name fragment vector index variable, where the incrementing continues at least until no chemical name fragments remain; setting a string combination to include the chemical name fragments identified by the initialized and incremented chemical name fragment vector index variables, and adding the string combination to a vector c as the complete chemical name; assigning, with a sentence parser unit operating on at least one computer, the complete chemical name with one part of speech; and storing in a memory the complete chemical name assigned with the one part of speech. 6. A method as in claim 1 , where said regular expressions comprise a plurality of patterns, individual ones of which are comprised of at least one of characters, numbers and punctuation. | 0.752646 |
10,163,022 | 10 | 17 | 10. A training apparatus for learning one or more parameters of a Convolutional Neural Networks (CNN) used to recognize one or more characters included in a text in a scene text image of training set, comprising: a communication part for acquiring (i) segmented character images obtained by dividing an image of the text in the scene text image into separate images of the characters, (ii) the image of the text or (iii) the scene text image; and a processor for performing processes of (i) generating or allowing another device to generate each multidimensional feature vector corresponding to each of the segmented character images, (ii) generating or allowing another device to generate a support vector to be used for recognizing a specific character image by executing at least one of computations with multidimensional feature vectors ci+j of one or more neighboring character images, wherein the specific character image and the neighboring character images are included in the segmented character images, wherein index j is not equal to 0 and −m≤j≤n, and wherein m and n are positive integers; (iii) obtaining or allowing another device to obtain a merged vector or its processed value by executing a computation with the support vector and a multidimensional feature vector ci of the specific character image; and (iv)(iv-1) determining or allowing another device to determine that the specific character image is a specific letter included in a predetermined set of letters by referring to the merged vector or its processed value, and (iv-2) adjusting or allowing another device to adjust the parameters by referring to a result of the classification. | 10. A training apparatus for learning one or more parameters of a Convolutional Neural Networks (CNN) used to recognize one or more characters included in a text in a scene text image of training set, comprising: a communication part for acquiring (i) segmented character images obtained by dividing an image of the text in the scene text image into separate images of the characters, (ii) the image of the text or (iii) the scene text image; and a processor for performing processes of (i) generating or allowing another device to generate each multidimensional feature vector corresponding to each of the segmented character images, (ii) generating or allowing another device to generate a support vector to be used for recognizing a specific character image by executing at least one of computations with multidimensional feature vectors ci+j of one or more neighboring character images, wherein the specific character image and the neighboring character images are included in the segmented character images, wherein index j is not equal to 0 and −m≤j≤n, and wherein m and n are positive integers; (iii) obtaining or allowing another device to obtain a merged vector or its processed value by executing a computation with the support vector and a multidimensional feature vector ci of the specific character image; and (iv)(iv-1) determining or allowing another device to determine that the specific character image is a specific letter included in a predetermined set of letters by referring to the merged vector or its processed value, and (iv-2) adjusting or allowing another device to adjust the parameters by referring to a result of the classification. 17. The training apparatus of claim 10 , wherein the parameters are adjusted by performing a backpropagation training technique. | 0.859956 |
8,468,491 | 16 | 20 | 16. A non-transitory machine-readable medium embodying instructions which, when executed by a computer-implemented system, cause the computer-implemented system to execute a method comprising: receiving selection of a segmented view of an enterprise meta-model, the segmented view depicting a discrete portion of a business process to be modeled, and the enterprise meta-model being a computer-accessible representation of business rules and policies for an enterprise stored in a computer-readable enterprise meta-model repository; registering input of a business process model through arrangement of a set of visual modeling elements, the visual modeling elements limited by the segmented view depicting the discrete portion of the business process to be modeled and representing building blocks for the business process to be modeled; receiving selection of a business policy to apply to the business process model from a set of matching business policies associated with the business process model; transforming the business process model into a machine-readable representation of the business process model; and storing the machine-readable representation into the computer-readable enterprise meta-model repository. | 16. A non-transitory machine-readable medium embodying instructions which, when executed by a computer-implemented system, cause the computer-implemented system to execute a method comprising: receiving selection of a segmented view of an enterprise meta-model, the segmented view depicting a discrete portion of a business process to be modeled, and the enterprise meta-model being a computer-accessible representation of business rules and policies for an enterprise stored in a computer-readable enterprise meta-model repository; registering input of a business process model through arrangement of a set of visual modeling elements, the visual modeling elements limited by the segmented view depicting the discrete portion of the business process to be modeled and representing building blocks for the business process to be modeled; receiving selection of a business policy to apply to the business process model from a set of matching business policies associated with the business process model; transforming the business process model into a machine-readable representation of the business process model; and storing the machine-readable representation into the computer-readable enterprise meta-model repository. 20. The non-transitory machine-readable medium of claim 16 , wherein selecting a business policy includes requesting matching business policies, the matching business policies having a relevance to the business process model. | 0.652778 |
8,380,651 | 22 | 35 | 22. A computer storage medium encoded with computer program instructions that when executed by one or more computers cause the one or more computers to perform operations including: providing a user interface for creating a table having at least one input column and at least one output column, wherein each input column is associated with an input variable and each output column is associated with an output variable; in at least one row of the table, receiving one or more conditions on input values in respective input columns, the conditions in the at least one row identifying more than one set of potential values of the input variables, and receiving one or more output values in respective output columns, thereby defining a rule case of a rule specification; generating a function for transforming data based on the rule specification; associating the function with the functional component; receiving changes to values in the rule specification, the changes including new potential values of the input variables for a condition; confirming that the changed rule specification is valid; and associating a new function with the functional component. | 22. A computer storage medium encoded with computer program instructions that when executed by one or more computers cause the one or more computers to perform operations including: providing a user interface for creating a table having at least one input column and at least one output column, wherein each input column is associated with an input variable and each output column is associated with an output variable; in at least one row of the table, receiving one or more conditions on input values in respective input columns, the conditions in the at least one row identifying more than one set of potential values of the input variables, and receiving one or more output values in respective output columns, thereby defining a rule case of a rule specification; generating a function for transforming data based on the rule specification; associating the function with the functional component; receiving changes to values in the rule specification, the changes including new potential values of the input variables for a condition; confirming that the changed rule specification is valid; and associating a new function with the functional component. 35. The computer storage medium of claim 22 in which receiving the table of test columns includes receiving from a user a set of input values, matching the set of input values to the potential input values of the rule specification, and storing the set of input values to a column of the table. | 0.71345 |
9,697,300 | 11 | 12 | 11. A non-transitory computer readable medium configured to provide a method for graph syntax validation when executable instructions are executed, comprising instructions for: receiving (i) an input graph that contains one or more input graph nodes, the input graph is checked for use of valid syntax or invalid syntax, (ii) transformation rules, and (iii) a minimal valid graph, wherein the input graph nodes indicate, in a graph notation, types of nodes, wherein the types of the nodes include functions, events, and gateways, wherein a syntax is defined by the transformation rules; transforming, in response to receiving the input graph, the input graph toward the minimal valid graph using the transformation rules, each of the transformation rules includes a source pattern and a target pattern, further comprising, in the transforming, source pattern-matching by comparing the input graph with the source pattern of the transformation rules and determining whether the input graph matches the source pattern of one or more transformation rules; and rule-executing, by replacing the input graph nodes that are determined to match the source pattern of one or more transformation rules with the target patterns for the one or more transformation rules, wherein the transforming recurs until either the input graph has been determined to be reduced to the minimal valid graph indicating that the input graph uses the valid syntax, or until it is determined that none of the transformation rules match the input graph indicating that the input graph uses an invalid syntax; and outputting, after the transforming, a result indicating either that the input graph is determined to use the valid syntax or the input graph is determined to use the invalid syntax. | 11. A non-transitory computer readable medium configured to provide a method for graph syntax validation when executable instructions are executed, comprising instructions for: receiving (i) an input graph that contains one or more input graph nodes, the input graph is checked for use of valid syntax or invalid syntax, (ii) transformation rules, and (iii) a minimal valid graph, wherein the input graph nodes indicate, in a graph notation, types of nodes, wherein the types of the nodes include functions, events, and gateways, wherein a syntax is defined by the transformation rules; transforming, in response to receiving the input graph, the input graph toward the minimal valid graph using the transformation rules, each of the transformation rules includes a source pattern and a target pattern, further comprising, in the transforming, source pattern-matching by comparing the input graph with the source pattern of the transformation rules and determining whether the input graph matches the source pattern of one or more transformation rules; and rule-executing, by replacing the input graph nodes that are determined to match the source pattern of one or more transformation rules with the target patterns for the one or more transformation rules, wherein the transforming recurs until either the input graph has been determined to be reduced to the minimal valid graph indicating that the input graph uses the valid syntax, or until it is determined that none of the transformation rules match the input graph indicating that the input graph uses an invalid syntax; and outputting, after the transforming, a result indicating either that the input graph is determined to use the valid syntax or the input graph is determined to use the invalid syntax. 12. The non-transitory computer-readable medium of claim 11 , further comprising result-visualizing, by the processor, the transformation result. | 0.583333 |
7,930,288 | 9 | 14 | 9. A non-transitory machine-readable medium having stored thereon machine-executable instructions that if executed by a machine cause the machine to perform a method, the method comprising: accessing an ontology that describes a structure of application data stored in an application, where the ontology includes respective ontology data elements mapped to respective application objects that characterize application data elements; searching for and selecting one or more data elements in one or more documents belonging to an enterprise; and upon locating an ontology data element in a document, creating a link between the document and the application data mapped to the ontology data element, where the link is appended to the document for future processing. | 9. A non-transitory machine-readable medium having stored thereon machine-executable instructions that if executed by a machine cause the machine to perform a method, the method comprising: accessing an ontology that describes a structure of application data stored in an application, where the ontology includes respective ontology data elements mapped to respective application objects that characterize application data elements; searching for and selecting one or more data elements in one or more documents belonging to an enterprise; and upon locating an ontology data element in a document, creating a link between the document and the application data mapped to the ontology data element, where the link is appended to the document for future processing. 14. The non-transitory machine-readable medium of claim 9 , including receiving a query and identifying a document relevant to the query based, at least in part, on the ontology. | 0.872493 |
8,429,179 | 16 | 17 | 16. The computer readable medium of claim 15 , further comprising instructions for constructing a survey based on a survey ontology, including adding concepts to the survey ontology, wherein the added concepts are mapped to the domain ontology. | 16. The computer readable medium of claim 15 , further comprising instructions for constructing a survey based on a survey ontology, including adding concepts to the survey ontology, wherein the added concepts are mapped to the domain ontology. 17. The computer readable medium of claim 16 , wherein the survey is a graph representation of a set of questions mapped to the survey ontology. | 0.950954 |
9,053,102 | 1 | 13 | 1. A method for deriving and utilizing a context object to generate a synthetic context-based object, the method comprising: deriving, by one or more processors, a context object for a non-contextual data object, wherein the non-contextual data object ambiguously relates to multiple subject-matters, wherein the context object provides a context that identifies a specific subject-matter, from multiple subject-matters, of the non-contextual data object, and wherein the context object is derived by contextually searching and analyzing a document, which contains multiple instances of the non-contextual data object, to derive the context object; establishing a minimum validity threshold for the context object, wherein the minimum validity threshold defines a probability that a derived context object accurately describes the context of the non-contextual data object; expanding a range of a search area of the document until the minimum validity threshold is reached; associating, by one or more processors, the non-contextual data object with the context object to define a synthetic context-based object; associating, by one or more processors, the synthetic context-based object with at least one specific data store, wherein said at least one specific data store comprises data that is associated with data contained in the non-contextual data object and the context object; constructing, by one or more processors, a dimensionally constrained hierarchical synthetic context-based object library for multiple synthetic context-based objects, wherein synthetic context-based objects within a same dimension of the dimensionally constrained hierarchical synthetic context-based object library share data from a same non-contextual data object, and wherein synthetic context-based objects within the same dimension of the dimensionally constrained hierarchical synthetic context-based object library contain disparate data from different context objects; receiving, from a requester, a request for at least one data store that is associated with synthetic context-based objects within the same dimension of the dimensionally constrained hierarchical synthetic context-based object library; and returning, to the requester, said at least one specific data store that is associated with synthetic context-based objects within the same dimension of the dimensionally constrained hierarchical synthetic context-based object library. | 1. A method for deriving and utilizing a context object to generate a synthetic context-based object, the method comprising: deriving, by one or more processors, a context object for a non-contextual data object, wherein the non-contextual data object ambiguously relates to multiple subject-matters, wherein the context object provides a context that identifies a specific subject-matter, from multiple subject-matters, of the non-contextual data object, and wherein the context object is derived by contextually searching and analyzing a document, which contains multiple instances of the non-contextual data object, to derive the context object; establishing a minimum validity threshold for the context object, wherein the minimum validity threshold defines a probability that a derived context object accurately describes the context of the non-contextual data object; expanding a range of a search area of the document until the minimum validity threshold is reached; associating, by one or more processors, the non-contextual data object with the context object to define a synthetic context-based object; associating, by one or more processors, the synthetic context-based object with at least one specific data store, wherein said at least one specific data store comprises data that is associated with data contained in the non-contextual data object and the context object; constructing, by one or more processors, a dimensionally constrained hierarchical synthetic context-based object library for multiple synthetic context-based objects, wherein synthetic context-based objects within a same dimension of the dimensionally constrained hierarchical synthetic context-based object library share data from a same non-contextual data object, and wherein synthetic context-based objects within the same dimension of the dimensionally constrained hierarchical synthetic context-based object library contain disparate data from different context objects; receiving, from a requester, a request for at least one data store that is associated with synthetic context-based objects within the same dimension of the dimensionally constrained hierarchical synthetic context-based object library; and returning, to the requester, said at least one specific data store that is associated with synthetic context-based objects within the same dimension of the dimensionally constrained hierarchical synthetic context-based object library. 13. The method of claim 1 , further comprising: receiving the request from the requester via a request pointer, wherein the request pointer points to a user-specified synthetic context-based object. | 0.92437 |
7,707,566 | 7 | 9 | 7. The one or more computer-readable media of claim 1 wherein the architecture is combinable with the one or more software development components. | 7. The one or more computer-readable media of claim 1 wherein the architecture is combinable with the one or more software development components. 9. The one or more computer-readable media of claim 7 wherein the one or more software development components provides target execution architecture data to the code generator. | 0.947274 |
9,026,701 | 24 | 25 | 24. The non-transitory computer-readable storage medium of claim 20 , further comprising: sending instructions for sending a response to the first request, wherein the response conforms to a response format defined in the first language. | 24. The non-transitory computer-readable storage medium of claim 20 , further comprising: sending instructions for sending a response to the first request, wherein the response conforms to a response format defined in the first language. 25. The non-transitory computer-readable storage medium of claim 24 , wherein the response format comprises: at least one instruction; and data to be used when performing the at least one instruction. | 0.964196 |
10,019,491 | 17 | 18 | 17. A non-transitory, computer program product comprising code stored therein and executable by one or more processors to cause machine learning in the selection of a ranked response to a structured data input having a natural language processing output schema received from a requesting device, wherein the code is executable to cause the one or more data processors to: receive the structured data input, wherein the structured data input includes filtering parameters for conversion into response template filtering criteria; convert the filtering parameters into the response template filtering criteria; query a library of response templates to identify candidate response templates that meet the response template filtering criteria to filter the response templates; receive a selection of the candidate response templates that meet the response template filtering criteria and respond to the structured input data, wherein the candidate response templates include static data; operate a ranking engine to rank the selection of candidate response templates in accordance with ranking criteria; select a highest ranked candidate response template to provide a response to a device; derive the response to the structured data input from the selected, highest ranked, candidate response template; provide the response to a recipient device; and provide feedback to the ranking engine to refine the ranking criteria. | 17. A non-transitory, computer program product comprising code stored therein and executable by one or more processors to cause machine learning in the selection of a ranked response to a structured data input having a natural language processing output schema received from a requesting device, wherein the code is executable to cause the one or more data processors to: receive the structured data input, wherein the structured data input includes filtering parameters for conversion into response template filtering criteria; convert the filtering parameters into the response template filtering criteria; query a library of response templates to identify candidate response templates that meet the response template filtering criteria to filter the response templates; receive a selection of the candidate response templates that meet the response template filtering criteria and respond to the structured input data, wherein the candidate response templates include static data; operate a ranking engine to rank the selection of candidate response templates in accordance with ranking criteria; select a highest ranked candidate response template to provide a response to a device; derive the response to the structured data input from the selected, highest ranked, candidate response template; provide the response to a recipient device; and provide feedback to the ranking engine to refine the ranking criteria. 18. The non-transitory, computer program product of claim 17 wherein the requesting device accesses data that provides insight into an individual and formulates the structured data input as a proactive query to obtain a proactive response to provide to the recipient device that is germane to the individual based on the insight. | 0.612941 |
5,404,507 | 17 | 19 | 17. A method of retrieving a record of an item from a plurality of information databases in response to a series of input words, the method comprising the steps of: comparing each word contained in the series of input words with words contained in a first and a second one of a plurality of search expression databases associated with a first one of the plurality of information databases; generating a set of search expressions, each search expression in the set including words from the first search expression database for providing an equivalent representation of one or more of the series of input words and words from the second search expression database for providing selected words close in character content to one or more of the series of input words; searching in the first one of the plurality of information databases for retrieving records containing the search expressions; searching in other of the plurality of information databases for retrieving records containing the search expressions when none of the retrieved records in the first one of the plurality of information databases best matches the series of input words in accordance with a predetermined parameter; and selecting in accordance with the predetermined parameter the retrieved record from any of the plurality of information databases that best matches the series of input words. | 17. A method of retrieving a record of an item from a plurality of information databases in response to a series of input words, the method comprising the steps of: comparing each word contained in the series of input words with words contained in a first and a second one of a plurality of search expression databases associated with a first one of the plurality of information databases; generating a set of search expressions, each search expression in the set including words from the first search expression database for providing an equivalent representation of one or more of the series of input words and words from the second search expression database for providing selected words close in character content to one or more of the series of input words; searching in the first one of the plurality of information databases for retrieving records containing the search expressions; searching in other of the plurality of information databases for retrieving records containing the search expressions when none of the retrieved records in the first one of the plurality of information databases best matches the series of input words in accordance with a predetermined parameter; and selecting in accordance with the predetermined parameter the retrieved record from any of the plurality of information databases that best matches the series of input words. 19. The method of claim 17 wherein the predetermined parameter comprises assigning a closeness value to each record found in accordance with the retrieving step. | 0.503086 |
9,704,054 | 17 | 18 | 17. An imaging device comprising: an imaging sensor; at least one memory device; and at least one computer processor, wherein the at least one computer processor is configured to at least: capture a first image using the imaging sensor; provide a first input comprising the first image to a first machine learning tool, wherein the first machine learning tool is configured to associate an image with one of a plurality of clusters of labels; receive a first output from the first machine learning tool, wherein the first output comprises a first pseudolabel associated with a first cluster of the plurality of clusters of labels; identify the first cluster based at least in part on the first pseudolabel; determine whether one of the labels of the first cluster may be associated with the first image to a predetermined degree of confidence; and upon determining that the one of the labels of the first cluster may be associated with the first image to the predetermined degree of confidence, store an association of the one of the labels of the first cluster with the first image in the at least one memory device. | 17. An imaging device comprising: an imaging sensor; at least one memory device; and at least one computer processor, wherein the at least one computer processor is configured to at least: capture a first image using the imaging sensor; provide a first input comprising the first image to a first machine learning tool, wherein the first machine learning tool is configured to associate an image with one of a plurality of clusters of labels; receive a first output from the first machine learning tool, wherein the first output comprises a first pseudolabel associated with a first cluster of the plurality of clusters of labels; identify the first cluster based at least in part on the first pseudolabel; determine whether one of the labels of the first cluster may be associated with the first image to a predetermined degree of confidence; and upon determining that the one of the labels of the first cluster may be associated with the first image to the predetermined degree of confidence, store an association of the one of the labels of the first cluster with the first image in the at least one memory device. 18. The imaging device of claim 17 , wherein the at least one computer processor is further configured to at least: upon determining that the one of the labels of the first cluster may not be associated with the first image to the predetermined degree of confidence, provide a second input comprising the first image and the first pseudolabel to the first machine learning tool; receive a second output from the first machine learning tool, wherein the second output comprises a second pseudolabel associated with a second cluster of the plurality of clusters of labels; and identify the second cluster based at least in part on the second pseudolabel, wherein the second cluster is a subset of the first cluster. | 0.796634 |
8,417,513 | 12 | 16 | 12. The method of claim 10 , further comprising instantiating sentences based on data from the domain according to the sentence template. | 12. The method of claim 10 , further comprising instantiating sentences based on data from the domain according to the sentence template. 16. The method of claim 12 , further comprising indexing the instantiated sentences to enable contextual searches. | 0.954905 |
7,986,843 | 24 | 25 | 24. A system comprising: a mobile device network configured to transmit digital images; a server environment configured to provide electronic search service over a computer network; and means for connecting the mobile device network with the server environment, the means for connecting comprising means for applying optical character recognition to extract words from the digital images and means for providing the extracted words and the digital images to the server environment for electronic search service of the digital images via the computer network; wherein the means for applying comprises means for selecting between at least two dictionary based language models according to received indications of document type, and means for post-processing the extracted words in accordance with the selected dictionary based language model; and wherein an indication of document type comprises a user specified category selected from a group including business cards and credit card receipts. | 24. A system comprising: a mobile device network configured to transmit digital images; a server environment configured to provide electronic search service over a computer network; and means for connecting the mobile device network with the server environment, the means for connecting comprising means for applying optical character recognition to extract words from the digital images and means for providing the extracted words and the digital images to the server environment for electronic search service of the digital images via the computer network; wherein the means for applying comprises means for selecting between at least two dictionary based language models according to received indications of document type, and means for post-processing the extracted words in accordance with the selected dictionary based language model; and wherein an indication of document type comprises a user specified category selected from a group including business cards and credit card receipts. 25. The system of claim 24 , wherein the means for connecting comprises means for validating mobile devices in the mobile device network. | 0.882906 |
10,096,044 | 2 | 5 | 2. The method of claim 1 , further comprising: generating the text based on speech from the user, wherein the speech is received via passive monitoring of the speech of the user. | 2. The method of claim 1 , further comprising: generating the text based on speech from the user, wherein the speech is received via passive monitoring of the speech of the user. 5. The method of claim 2 , wherein the speech is received at a first device and the advertisement is displayed on a second device different than the first device. | 0.872841 |
8,775,416 | 13 | 18 | 13. A non-transitory machine-readable medium carrying instructions which, when executed by one or more processors, cause: generating a generic relevance function based on training data from a plurality of first users and that is not based on a specific context associated with any of the plurality of first users; storing the generic relevance function in a machine-readable storage medium; collecting context-specific training data, wherein the context-specific training data is based on a plurality of second users and a specific context associated with the plurality of second users; adapting the generic relevance function to produce a context-specific relevance function, wherein the adapting comprises using the generic relevance function and the context-specific training data as input to a machine learning technique to generate the context-specific relevance function; after producing the context-specific relevance function, receiving a query from a particular user; processing the query to identify results of the query; identifying a particular context of the query or of the particular user; selecting, based on the particular context, a particular context-specific relevance function from among the plurality of context-specific relevance functions; using the particular context-specific relevance function to determine relevance of each of the results only in response to determining that the particular context is the same as the specific context upon which the particular context-specific relevance function is based; based on the particular context-specific relevance function, assigning a relevance value to each of the results; and sending, to the particular user, at least a subset of the results to be displayed. | 13. A non-transitory machine-readable medium carrying instructions which, when executed by one or more processors, cause: generating a generic relevance function based on training data from a plurality of first users and that is not based on a specific context associated with any of the plurality of first users; storing the generic relevance function in a machine-readable storage medium; collecting context-specific training data, wherein the context-specific training data is based on a plurality of second users and a specific context associated with the plurality of second users; adapting the generic relevance function to produce a context-specific relevance function, wherein the adapting comprises using the generic relevance function and the context-specific training data as input to a machine learning technique to generate the context-specific relevance function; after producing the context-specific relevance function, receiving a query from a particular user; processing the query to identify results of the query; identifying a particular context of the query or of the particular user; selecting, based on the particular context, a particular context-specific relevance function from among the plurality of context-specific relevance functions; using the particular context-specific relevance function to determine relevance of each of the results only in response to determining that the particular context is the same as the specific context upon which the particular context-specific relevance function is based; based on the particular context-specific relevance function, assigning a relevance value to each of the results; and sending, to the particular user, at least a subset of the results to be displayed. 18. The machine-readable medium of claim 13 , wherein the adapting comprises producing a plurality of context-specific relevance functions, wherein for each context-specific relevance function of the plurality of context-specific relevance functions, the said each context-specific relevance function is adapted from the generic relevance function based on adaptation data that is based on a particular plurality of users and a specific context that indicates one or more characteristics of the particular plurality of users. | 0.596154 |
7,970,721 | 10 | 11 | 10. The system of claim 1 , wherein the inference at least one of predicts quality of the one or more objects of interest, predicts an adjustment of the context, or identifies one or more patterns based upon graphical properties of the one or more sub graphs. | 10. The system of claim 1 , wherein the inference at least one of predicts quality of the one or more objects of interest, predicts an adjustment of the context, or identifies one or more patterns based upon graphical properties of the one or more sub graphs. 11. The system of claim 10 , wherein the quality of the one or more objects of interest is associated with individual objects from the one or more objects of interest, a sub set of the one or more objects of interest, or the entire set of one or more objects of interest. | 0.869837 |
9,342,501 | 15 | 16 | 15. The information handling device of claim 10 , wherein the code is further executable by the processors to: analyze one or more emotion tags; and modify operation of an application based on the analyzing. | 15. The information handling device of claim 10 , wherein the code is further executable by the processors to: analyze one or more emotion tags; and modify operation of an application based on the analyzing. 16. The information handling device of claim 15 , wherein to modify an operation of an application comprises supplementing a search application result utilizing emotion tag searching. | 0.8536 |
8,793,118 | 17 | 22 | 17. A computer implemented system for assisting a user to learn and/or communicate in a visual communication language in one or more of a plurality of modes, comprising: a multimodal communication assist application embedded in a non-transitory computer readable storage medium executable by a processor of said user, comprising: an interactive interface that enables communication of said visual communication language in said modes; a plurality of multimodal communication mappers accessible to said user via said interactive interface, wherein said multimodal communication mappers map a modal input in one of said modes to a modal output in another one or more of said modes; a mapper identification module that identifies a selection of one of said multimodal communication mappers received from said user via said interactice interface; a mapping acquisition module for acquiring user-defined communication mappers; a characteristic information determination module that determines characteristic information of said user based on one or more of said received user-selected multimodal communication mappers and said acqquired user-defined communication mappers; a delay factor determination module that determines a delay factor for said user based on a calculation of response times of said user for repeating each of a set of pre-stored gestures presented to said user along with a corresponding set of words, using a set of formulas:
Delay Factor=Total time T t /( C− 2);
T t =( T 1 +T 2 +T 3 + . . . +T C −T max −T min ); wherein T 1 , T 2 , T 3 , . . . , T C are response times of said user for repeating each of a set of pre-stored gestures presented to said user along with a corresponding set of words; C is total number of said pre-stored gestures and words presented to said user; said T max is slowest among the response times; and said T min is fastest among the response times; a modal input capture module that captures said modal input in one of said modes from said user via said interactive interface based on said determined delay factor and one or more of said characteristic information, wherein said multimodal communication assist application controls sampling rate of said capturing of said modal input based on user's proficiency level, and wherein said proficiency level of said user is determined by said multimodal communication assist application based on said determined delay factor using a criteria set comprising: (a) if said delay factor is greater than or equal to 2.5 seconds, then said multimodal communication assist application defines and displays said proficiency level of said user as “beginner” on said interactive interface; (b) if said delay factor is greater than or equal to 2 seconds and less than 2.5 seconds, then said multimodal communication assist application defines and displays said proficiency level of said user as “advanced beginner” on said interactive interface; (c) if said delay factor is greater than or equal to 1.5 seconds and less than 2 seconds, then said multimodal communication assist defines and displays said proficiency level of said user as “competent” on said interactive interface; (d) if said delay factor is greater than or equal to 1 second and less than 1.5 seconds, then said multimodal communication assist defines and displays said proficiency level of said user as “professional” on said interactive interface; and (e) if said delay factor is less than 1 second, then said multimodal communication assist application defines and displays said proficiency level of said user as “expert” on said interactive interface; and a control unit that processes and transforms said captured modal input in said one of said modes into said modal output in said another one or more of said modes using said selected one or more multimodal communication mappers, wherein said modal output in said another one or more of said modes is rendered to said user via said interactive interface for assisting said user to learn and/or communicate in said visual communication language in said one or more of said modes. | 17. A computer implemented system for assisting a user to learn and/or communicate in a visual communication language in one or more of a plurality of modes, comprising: a multimodal communication assist application embedded in a non-transitory computer readable storage medium executable by a processor of said user, comprising: an interactive interface that enables communication of said visual communication language in said modes; a plurality of multimodal communication mappers accessible to said user via said interactive interface, wherein said multimodal communication mappers map a modal input in one of said modes to a modal output in another one or more of said modes; a mapper identification module that identifies a selection of one of said multimodal communication mappers received from said user via said interactice interface; a mapping acquisition module for acquiring user-defined communication mappers; a characteristic information determination module that determines characteristic information of said user based on one or more of said received user-selected multimodal communication mappers and said acqquired user-defined communication mappers; a delay factor determination module that determines a delay factor for said user based on a calculation of response times of said user for repeating each of a set of pre-stored gestures presented to said user along with a corresponding set of words, using a set of formulas:
Delay Factor=Total time T t /( C− 2);
T t =( T 1 +T 2 +T 3 + . . . +T C −T max −T min ); wherein T 1 , T 2 , T 3 , . . . , T C are response times of said user for repeating each of a set of pre-stored gestures presented to said user along with a corresponding set of words; C is total number of said pre-stored gestures and words presented to said user; said T max is slowest among the response times; and said T min is fastest among the response times; a modal input capture module that captures said modal input in one of said modes from said user via said interactive interface based on said determined delay factor and one or more of said characteristic information, wherein said multimodal communication assist application controls sampling rate of said capturing of said modal input based on user's proficiency level, and wherein said proficiency level of said user is determined by said multimodal communication assist application based on said determined delay factor using a criteria set comprising: (a) if said delay factor is greater than or equal to 2.5 seconds, then said multimodal communication assist application defines and displays said proficiency level of said user as “beginner” on said interactive interface; (b) if said delay factor is greater than or equal to 2 seconds and less than 2.5 seconds, then said multimodal communication assist application defines and displays said proficiency level of said user as “advanced beginner” on said interactive interface; (c) if said delay factor is greater than or equal to 1.5 seconds and less than 2 seconds, then said multimodal communication assist defines and displays said proficiency level of said user as “competent” on said interactive interface; (d) if said delay factor is greater than or equal to 1 second and less than 1.5 seconds, then said multimodal communication assist defines and displays said proficiency level of said user as “professional” on said interactive interface; and (e) if said delay factor is less than 1 second, then said multimodal communication assist application defines and displays said proficiency level of said user as “expert” on said interactive interface; and a control unit that processes and transforms said captured modal input in said one of said modes into said modal output in said another one or more of said modes using said selected one or more multimodal communication mappers, wherein said modal output in said another one or more of said modes is rendered to said user via said interactive interface for assisting said user to learn and/or communicate in said visual communication language in said one or more of said modes. 22. The computer implemented system of claim 17 , wherein said multimodal communication assist application further comprises: an element extraction module in said control unit that extracts one or more text elements from said captured modal input in a text mode of said modes; a mode association module in said control unit that retrieves one or more modal outputs in a visual mode of said modes and/or an audio mode of said modes for said extracted one or more text elements from a database maintained by said multimodal communication assist application; and said database that archives said retrieved one or more modal outputs in said visual mode and/or said audio mode, wherein said archived one or more modal outputs in said visual mode and/or said audio mode are rendered to said user via said interactive interface for assisting said user to learn and/or communicate in said visual communication language. | 0.781953 |
9,916,364 | 1 | 3 | 1. A method of providing news, comprising: receiving, by a processor, a request for news items from a user; identifying, by the processor, a geographic location of the requesting user, the identifying of the geographic location comprising identifying the geographic location from a Designated Market Area (DMA); matching, by the processor, the geographic location of the requesting user with geographic locations associated with news sources; identifying, by the processor, a geographic location of local news sources that is from the same DMA as the geographic location of the requesting user, the identifying comprising obtaining preferred news sources of other users that are located within a similar geographic location as the requested user and eliminating used sources that are popular across multiple other geographic areas; obtaining, by the processor, relevant news items from the identified local news sources; and transmitting, by the processor, the relevant news items for presentation to the requesting user. | 1. A method of providing news, comprising: receiving, by a processor, a request for news items from a user; identifying, by the processor, a geographic location of the requesting user, the identifying of the geographic location comprising identifying the geographic location from a Designated Market Area (DMA); matching, by the processor, the geographic location of the requesting user with geographic locations associated with news sources; identifying, by the processor, a geographic location of local news sources that is from the same DMA as the geographic location of the requesting user, the identifying comprising obtaining preferred news sources of other users that are located within a similar geographic location as the requested user and eliminating used sources that are popular across multiple other geographic areas; obtaining, by the processor, relevant news items from the identified local news sources; and transmitting, by the processor, the relevant news items for presentation to the requesting user. 3. The method of claim 1 , wherein the geographic location of the requesting user is derived from one of an IP address or GPS information of a device employed by the requesting user. | 0.817269 |
7,725,307 | 1 | 13 | 1. A speech query recognition system comprising: a speech recognition engine for generating recognized words taken from an articulated speech utterance; and a natural language engine configured for processing said recognized words to generate at least two different types of search predicates for said articulated speech utterance; wherein said search predicates correspond to logical operators to be satisfied by a potential recognition match; a query formulation engine adapted to convert said recognized words and said search predicates into a structured query suitable for locating a set of one or more corresponding recognized matches for said articulated speech utterance; and said natural language engine further being configured for processing said set of one or more corresponding recognized matches to determine a final match for said articulated speech utterance using both semantic decoding and statistical based processing performed on said recognized words; wherein said semantic decoding is performed on entire word sentences contained in said articulated speech utterance to determine semantic variants of said word sentences in said one or more corresponding recognized matches, said semantic decoding being based on a term frequency calculation, which term frequency calculation is based on calculating a lexical distance between each word in said recognized words with each word of one or more topic query entries using a lexical dictionary. | 1. A speech query recognition system comprising: a speech recognition engine for generating recognized words taken from an articulated speech utterance; and a natural language engine configured for processing said recognized words to generate at least two different types of search predicates for said articulated speech utterance; wherein said search predicates correspond to logical operators to be satisfied by a potential recognition match; a query formulation engine adapted to convert said recognized words and said search predicates into a structured query suitable for locating a set of one or more corresponding recognized matches for said articulated speech utterance; and said natural language engine further being configured for processing said set of one or more corresponding recognized matches to determine a final match for said articulated speech utterance using both semantic decoding and statistical based processing performed on said recognized words; wherein said semantic decoding is performed on entire word sentences contained in said articulated speech utterance to determine semantic variants of said word sentences in said one or more corresponding recognized matches, said semantic decoding being based on a term frequency calculation, which term frequency calculation is based on calculating a lexical distance between each word in said recognized words with each word of one or more topic query entries using a lexical dictionary. 13. The system of claim 1 , wherein said structured query is a full text query containing SQL search predicates. | 0.815789 |
8,402,035 | 15 | 16 | 15. The system of claim 14 , wherein calculating the media value comprises determining an exposure number representing a number of people to which the one or more documents are distributed; determining an economic value per person attributed to exposure of the one or more documents; and multiplying the exposure number by the economic value to generate a media value multiplier. | 15. The system of claim 14 , wherein calculating the media value comprises determining an exposure number representing a number of people to which the one or more documents are distributed; determining an economic value per person attributed to exposure of the one or more documents; and multiplying the exposure number by the economic value to generate a media value multiplier. 16. The system of claim 15 , wherein calculating the media value comprises: multiplying the sentiment activity sum by the media value multiplier to generate the media value attributed to the entity identified in the one or more documents. | 0.911852 |
8,589,449 | 14 | 15 | 14. The method of claim 13 , additionally comprising storing the metadata object on a backup file system in the third format without requiring the backup file system to be able to read the information stored in the metadata object. | 14. The method of claim 13 , additionally comprising storing the metadata object on a backup file system in the third format without requiring the backup file system to be able to read the information stored in the metadata object. 15. The method of claim 14 , additionally comprising storing the first file data in the third format. | 0.961154 |
8,798,996 | 16 | 23 | 16. One or more storage media storing instructions which, when executed by one or more processors, cause: determining a first context from among a plurality of contexts; based on the first context, identifying a first technique for analyzing text data, wherein each context of the plurality of contexts is associated with a different technique for analyzing text data; using the first technique to analyze a string of text that was generated based on audio data; wherein using the first technique comprises identifying a plurality of text segments based on one or more criteria, wherein each segment of the plurality of text segments comprises one or more words in the string of text, wherein at least one text segment of the plurality of text segments comprises a plurality of words; organizing the plurality of text segments into a list of items, wherein each text segment is a separate item in the list. | 16. One or more storage media storing instructions which, when executed by one or more processors, cause: determining a first context from among a plurality of contexts; based on the first context, identifying a first technique for analyzing text data, wherein each context of the plurality of contexts is associated with a different technique for analyzing text data; using the first technique to analyze a string of text that was generated based on audio data; wherein using the first technique comprises identifying a plurality of text segments based on one or more criteria, wherein each segment of the plurality of text segments comprises one or more words in the string of text, wherein at least one text segment of the plurality of text segments comprises a plurality of words; organizing the plurality of text segments into a list of items, wherein each text segment is a separate item in the list. 23. The one or more storage media of claim 16 , wherein the first technique is associated with a first set of one or more database of phrases and a second technique that is associated with a second context of the plurality of contexts involves a second set of one or more databases that is different than the first set of one or more database. | 0.75535 |
10,001,904 | 1 | 4 | 1. A data processing method comprising: receiving a plurality of comments respectively associated with a plurality of video clips from a plurality of videos stored in a video database; receiving comment metadata regarding each comment of the plurality of comments, including a category of a plurality of categories and one or more time values related to a video clip of the plurality of video clips, one or more computers receiving one or more criteria to apply to the comment metadata, wherein the one or more criteria specify at least a particular category of the plurality of categories; the one or more computers selecting two or more video clips by applying the one or more criteria to the comment metadata to identify video clips with comments that are associated with the particular category, the selecting two or more video clips further comprising: identifying two or more comments on different videos of the plurality of comments where the comment metadata specifies the two or more comments as meeting the one or more criteria; and determining, for each comment of the two or more comments, the video clip associated with the comment based on the one or more time values in the comment metadata corresponding to the comment, wherein for each comment of the two or more comments a duration of the video clip associated with the comment is determined based on a user-specified duration of time a default duration of time or a duration of time stored in the comment metadata; the one or more computers displaying the two or more video clips by merging the two or more video clips into a compilation video. | 1. A data processing method comprising: receiving a plurality of comments respectively associated with a plurality of video clips from a plurality of videos stored in a video database; receiving comment metadata regarding each comment of the plurality of comments, including a category of a plurality of categories and one or more time values related to a video clip of the plurality of video clips, one or more computers receiving one or more criteria to apply to the comment metadata, wherein the one or more criteria specify at least a particular category of the plurality of categories; the one or more computers selecting two or more video clips by applying the one or more criteria to the comment metadata to identify video clips with comments that are associated with the particular category, the selecting two or more video clips further comprising: identifying two or more comments on different videos of the plurality of comments where the comment metadata specifies the two or more comments as meeting the one or more criteria; and determining, for each comment of the two or more comments, the video clip associated with the comment based on the one or more time values in the comment metadata corresponding to the comment, wherein for each comment of the two or more comments a duration of the video clip associated with the comment is determined based on a user-specified duration of time a default duration of time or a duration of time stored in the comment metadata; the one or more computers displaying the two or more video clips by merging the two or more video clips into a compilation video. 4. The method of claim 1 , wherein displaying the two or more video clips includes displaying a link for each video clip of the two or more video clips which, when selected, causes a video player window to play the video clip. | 0.787992 |
7,661,071 | 11 | 12 | 11. The method of claim 8 further comprising designing a two-dimensional graphical template; and assigning one or more behavioral properties to the two dimensional graphical template to create the two-dimensional user interface. | 11. The method of claim 8 further comprising designing a two-dimensional graphical template; and assigning one or more behavioral properties to the two dimensional graphical template to create the two-dimensional user interface. 12. The method of claim 11 , wherein the second assigning operation further comprises associating directions for navigating among elements of the two-dimensional graphic template in response to user input. | 0.946081 |
8,458,194 | 5 | 6 | 5. The method of claim 1 , comprising, after the comparing step: generating a new document set including only the source document if the confidence score for each of the document sets is below a second threshold confidence score. | 5. The method of claim 1 , comprising, after the comparing step: generating a new document set including only the source document if the confidence score for each of the document sets is below a second threshold confidence score. 6. The method of claim 5 , further comprising: generating a title for the new document set based on the topic information associated with the source document. | 0.940557 |
8,706,614 | 8 | 11 | 8. A system for facilitating management of risk related to political exposure associated with a financial transaction, comprising: a processor; computer-readable instructions that program the processor to: receive digital financial transaction data associated with the transaction; determine that a participant associated with the financial transaction is a politically identified person (“PIP”) by: comparing data identifying the participant associated with the financial transaction data to information stored in a database, wherein comparing comprises searching for both identical and similar names in the database; and verifying the data identifying the participant by comparing the financial transaction data to information associated with the data identifying the participant stored in the database if the data identifying the participant matches a name in the database; wherein verifying the data identifying the participant further comprises determining a certainty of the match by scoring the comparisons between the financial transaction data and the information associated with the data identifying the participant; calculate an overall transaction political risk quotient associated with the financial transaction; generate based on the overall transaction political risk quotient, a suggested action for the financial transaction. | 8. A system for facilitating management of risk related to political exposure associated with a financial transaction, comprising: a processor; computer-readable instructions that program the processor to: receive digital financial transaction data associated with the transaction; determine that a participant associated with the financial transaction is a politically identified person (“PIP”) by: comparing data identifying the participant associated with the financial transaction data to information stored in a database, wherein comparing comprises searching for both identical and similar names in the database; and verifying the data identifying the participant by comparing the financial transaction data to information associated with the data identifying the participant stored in the database if the data identifying the participant matches a name in the database; wherein verifying the data identifying the participant further comprises determining a certainty of the match by scoring the comparisons between the financial transaction data and the information associated with the data identifying the participant; calculate an overall transaction political risk quotient associated with the financial transaction; generate based on the overall transaction political risk quotient, a suggested action for the financial transaction. 11. The system of claim 8 , wherein the overall transaction political risk quotient is calculated based on weights applied to first and second category political risk scores, the first and second category political risk scores based on the financial transaction data. | 0.745714 |
9,137,220 | 1 | 3 | 1. A method for collaboratively editing a document in a system of sharee clients, the method comprising: creating a document change; generating a document token for encrypting the document change; encrypting, using a processor, the document change with the document token, wherein the encrypted document change is loaded onto one or more Cloud servers; making the encrypted document change available to sharee clients; forming a share document token by splitting the document token into a server document token and a sharee document token; generating a plurality of copies of the sharee document token; encrypting each sharee document token with a respective sharee's public key; distributing each encrypted sharee document token to respective sharee clients; wherein each sharee client is configured to: decrypt the encrypted sharee document token using a respective private key; combine the decrypted sharee document token with the server document token; decrypt the encrypted document change using the combined share document token; and consolidate the decrypted document change into the document. | 1. A method for collaboratively editing a document in a system of sharee clients, the method comprising: creating a document change; generating a document token for encrypting the document change; encrypting, using a processor, the document change with the document token, wherein the encrypted document change is loaded onto one or more Cloud servers; making the encrypted document change available to sharee clients; forming a share document token by splitting the document token into a server document token and a sharee document token; generating a plurality of copies of the sharee document token; encrypting each sharee document token with a respective sharee's public key; distributing each encrypted sharee document token to respective sharee clients; wherein each sharee client is configured to: decrypt the encrypted sharee document token using a respective private key; combine the decrypted sharee document token with the server document token; decrypt the encrypted document change using the combined share document token; and consolidate the decrypted document change into the document. 3. The method of claim 1 , wherein the encrypted document is stored in a document server. | 0.730303 |
8,892,218 | 6 | 8 | 6. The method of claim 1 , wherein the low-level distributed automation device comprises a motor drive or motor starter. | 6. The method of claim 1 , wherein the low-level distributed automation device comprises a motor drive or motor starter. 8. The method of claim 6 , wherein the low-level distributed automation device comprises a motor drive. | 0.976495 |
9,244,536 | 23 | 24 | 23. A method comprising: at a portable electronic device with a touch screen display: receiving a plurality of user character inputs through the touch screen display of the portable electronic device, wherein the user character inputs are received through a virtual keyboard displayed on the touch screen, wherein the virtual keyboard simultaneously displays a first plurality of virtual keys each associated with a respective single letter of the alphabet, and a second plurality of virtual punctuation mark keys; displaying on the touch display a current character string as input by a user; while displaying the current character string, also displaying a suggested replacement character string for the current character string, wherein at the time a suggested replacement character string is displayed, the portable electronic device is able to receive a plurality of possible user single touch inputs that will result in selection of the suggested replacement character string, and wherein said plurality of possible user single touch inputs includes actuation of one of the plurality of virtual punctuation mark keys; receiving a single touch user selection input through one of the plurality of the virtual punctuation mark keys; and in response to the single touch user selection input, replacing the current character set with the suggested replacement character set and adding at the end of said character set a punctuation mark corresponding to the virtual punctuation mark key through which the input was received. | 23. A method comprising: at a portable electronic device with a touch screen display: receiving a plurality of user character inputs through the touch screen display of the portable electronic device, wherein the user character inputs are received through a virtual keyboard displayed on the touch screen, wherein the virtual keyboard simultaneously displays a first plurality of virtual keys each associated with a respective single letter of the alphabet, and a second plurality of virtual punctuation mark keys; displaying on the touch display a current character string as input by a user; while displaying the current character string, also displaying a suggested replacement character string for the current character string, wherein at the time a suggested replacement character string is displayed, the portable electronic device is able to receive a plurality of possible user single touch inputs that will result in selection of the suggested replacement character string, and wherein said plurality of possible user single touch inputs includes actuation of one of the plurality of virtual punctuation mark keys; receiving a single touch user selection input through one of the plurality of the virtual punctuation mark keys; and in response to the single touch user selection input, replacing the current character set with the suggested replacement character set and adding at the end of said character set a punctuation mark corresponding to the virtual punctuation mark key through which the input was received. 24. The method of claim 23 , wherein the plurality of possible single touch user inputs that will result in selection of the suggested replacement character string includes inputs that are not associated with a virtual punctuation mark key. | 0.721578 |
7,805,159 | 22 | 23 | 22. A mobile device, comprising: a dual-mode keypad for use in a text entry mode and a telephony mode, the dual-mode keypad comprising a plurality of dual-mode toggle keys displaying indicia of at least one telephony character and at least two text-entry characters, a plurality of single-mode keys displaying only indicia of at least one text-entry character, a key for switching between the text entry mode and the telephony entry mode, wherein the dual mode keypad provides two distinct ergonomics depending upon whether the device is in the text entry or telephony mode and text-entry characters of the dual-mode and single-mode keys are configured in a QWERTY-style arrangement on the mobile device; when the mobile device is in text-entry mode, the dual-mode toggle keys are configured to input the associated text-entry characters, the dual-mode toggle keys each inputting a first text-entry character when a first portion of the key is pressed and to input a second text-entry character when a second portion of the key is pressed; when the mobile device is in telephony mode, the dual-mode toggle keys are configured to input the associated telephony characters, the dual-mode toggle keys each inputting one of the associated telephony characters when any portion of the key is pressed, and the single-mode keys are not operable to input telephony characters in the telephony mode, wherein the dual mode keys are larger than the single mode keys. | 22. A mobile device, comprising: a dual-mode keypad for use in a text entry mode and a telephony mode, the dual-mode keypad comprising a plurality of dual-mode toggle keys displaying indicia of at least one telephony character and at least two text-entry characters, a plurality of single-mode keys displaying only indicia of at least one text-entry character, a key for switching between the text entry mode and the telephony entry mode, wherein the dual mode keypad provides two distinct ergonomics depending upon whether the device is in the text entry or telephony mode and text-entry characters of the dual-mode and single-mode keys are configured in a QWERTY-style arrangement on the mobile device; when the mobile device is in text-entry mode, the dual-mode toggle keys are configured to input the associated text-entry characters, the dual-mode toggle keys each inputting a first text-entry character when a first portion of the key is pressed and to input a second text-entry character when a second portion of the key is pressed; when the mobile device is in telephony mode, the dual-mode toggle keys are configured to input the associated telephony characters, the dual-mode toggle keys each inputting one of the associated telephony characters when any portion of the key is pressed, and the single-mode keys are not operable to input telephony characters in the telephony mode, wherein the dual mode keys are larger than the single mode keys. 23. The mobile device of claim 22 , wherein at least one of the dual-mode toggle keys are configured to input a third text-entry character when a third portion of the key is pressed. | 0.887931 |
8,051,085 | 2 | 3 | 2. The method of claim 1 , wherein the NFA comprises an initial state and a number of intermediate states between the initial state and the match state, and wherein the match state of the NFA corresponds to an initial state of the reverse NFA. | 2. The method of claim 1 , wherein the NFA comprises an initial state and a number of intermediate states between the initial state and the match state, and wherein the match state of the NFA corresponds to an initial state of the reverse NFA. 3. The method of claim 2 , wherein the initial state of the NFA corresponds to a match state of the reverse NFA. | 0.949367 |
9,141,607 | 13 | 16 | 13. A non-transitory computer-readable storage device having instructions stored thereon that, when executed by a computing device, cause the computing device to perform operations comprising: performing a first optical character recognition process with an optical character recognition engine on one or more pages of a document to generate a first output response for each of multiple writing systems that each comprise a character set that is associated with one or more natural languages, wherein the first optical character recognition process uses a first configuration of the optical character recognition engine, and wherein the first configuration of the optical character recognition engine configures the optical character recognition engine to recognize, for each of the writing systems, a limited subset of characters from the character set of the writing system; using a trained classifier to identify, from among the multiple writing systems, a dominant writing system of the document based on the first output responses generated by the first optical character recognition process; after identifying the dominant writing system of the document and before selecting a dominant natural language of the document, reconfiguring the optical character recognition engine from the first configuration to a second configuration that is different from the first configuration, wherein the second configuration of the optical character recognition engine configures the optical character recognition engine to recognize the full character set that is associated with the one or more natural languages associated with the dominant writing system of the document; performing a second optical character recognition process with the reconfigured optical character recognition engine on one or more pages of the document to generate a second output response, wherein the second optical character recognition process uses the second configuration of the optical character recognition engine; and selecting the dominant natural language from the one or more natural languages associated with the dominant writing system by applying one or more statistical language models to the second output response. | 13. A non-transitory computer-readable storage device having instructions stored thereon that, when executed by a computing device, cause the computing device to perform operations comprising: performing a first optical character recognition process with an optical character recognition engine on one or more pages of a document to generate a first output response for each of multiple writing systems that each comprise a character set that is associated with one or more natural languages, wherein the first optical character recognition process uses a first configuration of the optical character recognition engine, and wherein the first configuration of the optical character recognition engine configures the optical character recognition engine to recognize, for each of the writing systems, a limited subset of characters from the character set of the writing system; using a trained classifier to identify, from among the multiple writing systems, a dominant writing system of the document based on the first output responses generated by the first optical character recognition process; after identifying the dominant writing system of the document and before selecting a dominant natural language of the document, reconfiguring the optical character recognition engine from the first configuration to a second configuration that is different from the first configuration, wherein the second configuration of the optical character recognition engine configures the optical character recognition engine to recognize the full character set that is associated with the one or more natural languages associated with the dominant writing system of the document; performing a second optical character recognition process with the reconfigured optical character recognition engine on one or more pages of the document to generate a second output response, wherein the second optical character recognition process uses the second configuration of the optical character recognition engine; and selecting the dominant natural language from the one or more natural languages associated with the dominant writing system by applying one or more statistical language models to the second output response. 16. The storage device of claim 13 , wherein identifying the dominant writing system of the document comprises: obtaining a unique response from the optical character recognition engine; and classifying the unique response using the trained classifier to identify the dominant writing system of the document. | 0.826577 |
6,122,661 | 21 | 22 | 21. A method according to claim 19, further comprising: storing computer display information representing a plurality of computer displays into the presentation space buffer; receiving the presentation space data stream containing the computer display information representing the plurality of computer displays into the display control of the server application framework of the server computer; and converting substantially the entire presentation space data stream containing the computer display information representing the plurality of computer displays into a single markup language document using the host extension of the server application framework of the serve computer. | 21. A method according to claim 19, further comprising: storing computer display information representing a plurality of computer displays into the presentation space buffer; receiving the presentation space data stream containing the computer display information representing the plurality of computer displays into the display control of the server application framework of the server computer; and converting substantially the entire presentation space data stream containing the computer display information representing the plurality of computer displays into a single markup language document using the host extension of the server application framework of the serve computer. 22. A method according to claim 21, wherein the plurality of computer displays comprises a single session running on the host computer. | 0.953799 |
9,836,646 | 1 | 6 | 1. A method for selecting character candidates in a method for identifying characters in a digital image, the method comprising the steps of: a) applying a first character identification process to determine first character candidates and a list of segmentation points of the first character candidates, b) generating a list of character widths corresponding to a segmentation point from the list of segmentation points, c) determining a portion of the digital image corresponding to the segmentation point and a character width from the list of character widths, d) applying a character classification method on the portion of the digital image to obtain an ID hypothesis of a character possibly present in the portion of the digital image and a likelihood parameter that relates to a likelihood that the ID hypothesis is correct, and e) selecting the ID hypothesis as a second character candidate in the digital image if the likelihood parameter fulfils a first predetermined criterion. | 1. A method for selecting character candidates in a method for identifying characters in a digital image, the method comprising the steps of: a) applying a first character identification process to determine first character candidates and a list of segmentation points of the first character candidates, b) generating a list of character widths corresponding to a segmentation point from the list of segmentation points, c) determining a portion of the digital image corresponding to the segmentation point and a character width from the list of character widths, d) applying a character classification method on the portion of the digital image to obtain an ID hypothesis of a character possibly present in the portion of the digital image and a likelihood parameter that relates to a likelihood that the ID hypothesis is correct, and e) selecting the ID hypothesis as a second character candidate in the digital image if the likelihood parameter fulfils a first predetermined criterion. 6. A method according to claim 1 , wherein steps c, d and e are repeated for another character width of the list of character widths if the likelihood parameter does not fulfil the first predetermined criterion. | 0.797505 |
9,317,550 | 1 | 7 | 1. A method comprising: obtaining a target query; and determining a normalized query according to the obtained target query, the determining including: obtaining session information in a search log; determining a vote similarity degree between a single query and the target query based on the session information, including: obtaining all queries appearing in a single session, calculating a number of votes for each query, the calculating including counting the single query appearing before the target query in the single session as a vote from the single query to the target query, and determining the vote similarity degree between the single query and the target query according to the calculated number of votes; determining a correlation degree between the single query and the target query based in part on the vote similarity degree; and determining the normalized query based in part on the correlation degree between the single query and the target query. | 1. A method comprising: obtaining a target query; and determining a normalized query according to the obtained target query, the determining including: obtaining session information in a search log; determining a vote similarity degree between a single query and the target query based on the session information, including: obtaining all queries appearing in a single session, calculating a number of votes for each query, the calculating including counting the single query appearing before the target query in the single session as a vote from the single query to the target query, and determining the vote similarity degree between the single query and the target query according to the calculated number of votes; determining a correlation degree between the single query and the target query based in part on the vote similarity degree; and determining the normalized query based in part on the correlation degree between the single query and the target query. 7. The method as recited in claim 1 , wherein the determining the vote similarity degree between the single query and the target query further comprises: determining a weight and a base number of each vote of the target query; calculating a score of each vote according to the respective weight and the respective base number; and using a proportion of a total vote score of the single query to the target query to a total vote score of all queries to the target query as the vote similarity degree between the single query and the target query. | 0.624138 |
7,895,225 | 1 | 5 | 1. A computing device configured as an information-processing system, the computing device comprising: a processor; and a memory; wherein the computing device is communicatively coupled to a document corpus; wherein the computing device is further communicatively coupled to a data store, wherein the computing device is further communicatively coupled to a relevance-based search engine that, for each executed query, returns a results set comprising an ordered set of documents of the document corpus ordered according to the relevance of each document to the executed query; and wherein the computing device is configured to: obtain a source document; identify a list of queries corresponding to the source document; execute, using the search engine, each query identified in the list of queries on the document corpus, wherein the execution of each query yields a corresponding results set identifying an ordered set of documents in the document corpus; for each document identified in each results set, generate a document score for the identified document based on the identified document's ordinal position in the results set in which the identified document is found; select a subset of the identified documents of the results sets according to whether the generated document scores meet a predetermined score threshold, wherein selecting the subset of the identified documents of the results sets according to whether the generated document scores meet a predetermined threshold further comprises determining a minimum score for a potential duplicate document from the document corpus that is returned in more than one results set, and including the potential duplicate document in the subset of the identified documents of the results sets in a case where the minimum score meets the predetermined threshold; and store the selected subset of identified documents as potential duplicates of the source document in the data store. | 1. A computing device configured as an information-processing system, the computing device comprising: a processor; and a memory; wherein the computing device is communicatively coupled to a document corpus; wherein the computing device is further communicatively coupled to a data store, wherein the computing device is further communicatively coupled to a relevance-based search engine that, for each executed query, returns a results set comprising an ordered set of documents of the document corpus ordered according to the relevance of each document to the executed query; and wherein the computing device is configured to: obtain a source document; identify a list of queries corresponding to the source document; execute, using the search engine, each query identified in the list of queries on the document corpus, wherein the execution of each query yields a corresponding results set identifying an ordered set of documents in the document corpus; for each document identified in each results set, generate a document score for the identified document based on the identified document's ordinal position in the results set in which the identified document is found; select a subset of the identified documents of the results sets according to whether the generated document scores meet a predetermined score threshold, wherein selecting the subset of the identified documents of the results sets according to whether the generated document scores meet a predetermined threshold further comprises determining a minimum score for a potential duplicate document from the document corpus that is returned in more than one results set, and including the potential duplicate document in the subset of the identified documents of the results sets in a case where the minimum score meets the predetermined threshold; and store the selected subset of identified documents as potential duplicates of the source document in the data store. 5. The information-processing system of claim 1 , wherein the computing device is further configured to display the selected subset of identified documents as potential duplicates of the source document to a user. | 0.693084 |
9,275,246 | 1 | 4 | 1. A method for static detection and categorization of information-flow downgraders, comprising: transforming a program stored in a memory device by statically analyzing program variables to yield a single assignment for each variable in an instruction set; translating the instruction set to production rules with string operations to identify a finite set of strings; generating a context-free grammar from the production rules; and identifying an information-flow downgrader function by checking the finite set of strings against one or more function specifications, wherein the information-flow downgrader function downgrades input information by endorsing integrity and declassifying confidentiality of the information to enable high input information to flow to low program points. | 1. A method for static detection and categorization of information-flow downgraders, comprising: transforming a program stored in a memory device by statically analyzing program variables to yield a single assignment for each variable in an instruction set; translating the instruction set to production rules with string operations to identify a finite set of strings; generating a context-free grammar from the production rules; and identifying an information-flow downgrader function by checking the finite set of strings against one or more function specifications, wherein the information-flow downgrader function downgrades input information by endorsing integrity and declassifying confidentiality of the information to enable high input information to flow to low program points. 4. The method as recited in claim 1 , further comprising comparing the context free grammar with a specification of the security-sensitive function such that if the grammar satisfies the specification, the input is considered properly downgraded. | 0.816143 |
9,563,690 | 1 | 9 | 1. A method for document searching, comprising: receiving, by a server, first search information from a device, the first search information derived from a first image, the first image captured with a camera of the device, the first image comprising a first portion of a document; performing, by the server, a first search on a set of documents based on the first search information; the server sending a first result from the first search to the device, wherein the first result is presented on the device; receiving, by the server, second search information from the device, the second search information derived from joining the first image and a second image, the second image captured with the camera of the device after a movement of the device, the second image comprising a second portion of the document; performing, by the server, a second search of the set of documents based on the second search information; the server updating the first result using the second search information to form a second result; and the server sending the second result to the device, wherein the first result on the device is replaced with the second result. | 1. A method for document searching, comprising: receiving, by a server, first search information from a device, the first search information derived from a first image, the first image captured with a camera of the device, the first image comprising a first portion of a document; performing, by the server, a first search on a set of documents based on the first search information; the server sending a first result from the first search to the device, wherein the first result is presented on the device; receiving, by the server, second search information from the device, the second search information derived from joining the first image and a second image, the second image captured with the camera of the device after a movement of the device, the second image comprising a second portion of the document; performing, by the server, a second search of the set of documents based on the second search information; the server updating the first result using the second search information to form a second result; and the server sending the second result to the device, wherein the first result on the device is replaced with the second result. 9. The method of claim 1 , wherein the first search information or the second search information includes: formatting, logos, graphics, or a whitespace fingerprint. | 0.630631 |
8,756,171 | 7 | 13 | 7. A computer readable storage medium embodying instructions executed by a processor for performing an iterative method for predictive analytics in a semi-structured process, the method comprising: updating, iteratively, at least one probability of a probabilistic process model for the semi-structured process based on a completed task, wherein updating the at least one probability of the probabilistic process model comprises: receiving the probabilistic process model; identifying a present state within the semi-structured process; deriving, from a process policy of the semi-structured process, a set of tasks, of a plurality of tasks associated with the probabilistic process model, that can be performed, at the identified present state, in conforming to the semi-structured process; establishing a todo list including the set of tasks that can be presently performed; defining a cost of each of the plurality of tasks of the todo list; prioritizing the plurality of tasks of the todo list according to the defined costs; and recommending a next task from the todo list according to the prioritization. | 7. A computer readable storage medium embodying instructions executed by a processor for performing an iterative method for predictive analytics in a semi-structured process, the method comprising: updating, iteratively, at least one probability of a probabilistic process model for the semi-structured process based on a completed task, wherein updating the at least one probability of the probabilistic process model comprises: receiving the probabilistic process model; identifying a present state within the semi-structured process; deriving, from a process policy of the semi-structured process, a set of tasks, of a plurality of tasks associated with the probabilistic process model, that can be performed, at the identified present state, in conforming to the semi-structured process; establishing a todo list including the set of tasks that can be presently performed; defining a cost of each of the plurality of tasks of the todo list; prioritizing the plurality of tasks of the todo list according to the defined costs; and recommending a next task from the todo list according to the prioritization. 13. The computer readable storage medium of claim 7 , wherein defining the cost further comprises: determining a total cost of a policy from a current state including a current cost function and a future cost function. | 0.681287 |
7,814,080 | 18 | 19 | 18. The system of claim 13 , wherein the identified first queries comprise statements <t, b 1 > through <t, b m >, m being greater than 1, where t represents a set of one or more tuples, and b 1 through bm represent respective modification operations on the set of one or more tuples, and wherein the second query comprises statement <t, c>, where c represents an aggregation of b 1 through b m . | 18. The system of claim 13 , wherein the identified first queries comprise statements <t, b 1 > through <t, b m >, m being greater than 1, where t represents a set of one or more tuples, and b 1 through bm represent respective modification operations on the set of one or more tuples, and wherein the second query comprises statement <t, c>, where c represents an aggregation of b 1 through b m . 19. The system of claim 18 , wherein c represents an addition of b 1 through b m . | 0.942737 |
9,977,866 | 1 | 2 | 1. A method comprising: receiving, by one or more computing devices of a matching-engine system, from an administrator of the matching-engine system a set of physician-selection parameters comprising: a performance-score-range for physicians; and an experience-score-range for physicians; storing, by one or more of the computing devices, the parameters in a data store of the matching-engine system; receiving, by one or more of the computing devices, a search query from a client device of a user of the matching-engine system, the search query comprising a geographic location of the user and one or more of a user-specified symptom or a user-specified treatment; determining, by one or more of the computing devices, at least one base-concept based on the search query, the base-concept comprising a medical diagnosis or a medical procedure; identifying, by one or more of the computing devices, a set of one or more physicians to be recommended to the user based at least in part on: a geographic location of each of the one or more physicians; a performance-score associated with the at least one base-concept for each of the one or more physicians, wherein the performance-score is calculated based on a weighted aggregate of individual performance-scores for one or more sub-concepts of the base-concept, wherein the individual performance-scores are calculated by: (1) accessing one or more Current Procedural Terminology (CPT) codes from claims data received from the physician, wherein the CPT codes refer to a medical service provided by the physician in connection with the one or more sub-concepts; (2) determining one or more relative value units (RVUs) corresponding to the one or more CPT codes, wherein the RVUs corresponding to a CPT code represents a location-independent measure of overall resources used for the medical service; (3) calculating a physician-cost-factor for the physician associated with the one or more sub-concepts, wherein the physician-cost-factor is based on the RVUs corresponding to the medical service provided by the physician for the one or more sub-concepts; (4) accessing an average cost factor associated with the one or more sub-concepts for a plurality of physicians, wherein the physician and the plurality of physicians share a common specialty and common geographic location; and (5) comparing the physician-cost-factor with the average cost factor, wherein the individual performance-score for the physician for the one or more sub-concepts is increased if the physician-cost-factor is lower than the average cost factor; an experience-score associated with the at least one base-concept each of the one or more physicians; and the set of physician-selection parameters from the administrator; and sending, by one or more computing devices, a search-results page to a client device of the user, the search-results page comprising references to one or more of the physicians in the set of physicians. | 1. A method comprising: receiving, by one or more computing devices of a matching-engine system, from an administrator of the matching-engine system a set of physician-selection parameters comprising: a performance-score-range for physicians; and an experience-score-range for physicians; storing, by one or more of the computing devices, the parameters in a data store of the matching-engine system; receiving, by one or more of the computing devices, a search query from a client device of a user of the matching-engine system, the search query comprising a geographic location of the user and one or more of a user-specified symptom or a user-specified treatment; determining, by one or more of the computing devices, at least one base-concept based on the search query, the base-concept comprising a medical diagnosis or a medical procedure; identifying, by one or more of the computing devices, a set of one or more physicians to be recommended to the user based at least in part on: a geographic location of each of the one or more physicians; a performance-score associated with the at least one base-concept for each of the one or more physicians, wherein the performance-score is calculated based on a weighted aggregate of individual performance-scores for one or more sub-concepts of the base-concept, wherein the individual performance-scores are calculated by: (1) accessing one or more Current Procedural Terminology (CPT) codes from claims data received from the physician, wherein the CPT codes refer to a medical service provided by the physician in connection with the one or more sub-concepts; (2) determining one or more relative value units (RVUs) corresponding to the one or more CPT codes, wherein the RVUs corresponding to a CPT code represents a location-independent measure of overall resources used for the medical service; (3) calculating a physician-cost-factor for the physician associated with the one or more sub-concepts, wherein the physician-cost-factor is based on the RVUs corresponding to the medical service provided by the physician for the one or more sub-concepts; (4) accessing an average cost factor associated with the one or more sub-concepts for a plurality of physicians, wherein the physician and the plurality of physicians share a common specialty and common geographic location; and (5) comparing the physician-cost-factor with the average cost factor, wherein the individual performance-score for the physician for the one or more sub-concepts is increased if the physician-cost-factor is lower than the average cost factor; an experience-score associated with the at least one base-concept each of the one or more physicians; and the set of physician-selection parameters from the administrator; and sending, by one or more computing devices, a search-results page to a client device of the user, the search-results page comprising references to one or more of the physicians in the set of physicians. 2. The method of claim 1 , wherein each physician in the set is associated with: a performance-score within the performance-score-range; and an experience-score within the experience-score-range. | 0.722222 |
7,548,878 | 1 | 2 | 1. A component audit and inventory management system comprising: a receiver, on a target device, including means for receiving an inventory-commence message from a client computer over a data network in response to a request from the client computer to a host unit for accessing hardware and software inventory data on the target device, said inventory-commence message being generated by the host unit and being sent to the client computer, said target device being remote with respect to and not connected to the client computer, said target device being only intermittently accessible on its own to the data network, wherein the target device, the client computer, and the host unit are separate devices from one another and are remote with respect to one another; a detector, on the target device, including means for collecting the hardware and software inventory data relating to hardware and software installed on the target device in response to commands included in the inventory-commence message; a transmitter, on the target device, including means for transmitting from the target device to the host unit of the component audit and inventory management system, through the data network, an inventory data message including the inventory data associated with the target device, wherein the client computer initiates access to the host unit for accessing the hardware and software inventory data of the target device; and an inventory agent comprising executable code for communicating with the receiver, detector, and transmitter. | 1. A component audit and inventory management system comprising: a receiver, on a target device, including means for receiving an inventory-commence message from a client computer over a data network in response to a request from the client computer to a host unit for accessing hardware and software inventory data on the target device, said inventory-commence message being generated by the host unit and being sent to the client computer, said target device being remote with respect to and not connected to the client computer, said target device being only intermittently accessible on its own to the data network, wherein the target device, the client computer, and the host unit are separate devices from one another and are remote with respect to one another; a detector, on the target device, including means for collecting the hardware and software inventory data relating to hardware and software installed on the target device in response to commands included in the inventory-commence message; a transmitter, on the target device, including means for transmitting from the target device to the host unit of the component audit and inventory management system, through the data network, an inventory data message including the inventory data associated with the target device, wherein the client computer initiates access to the host unit for accessing the hardware and software inventory data of the target device; and an inventory agent comprising executable code for communicating with the receiver, detector, and transmitter. 2. An inventory agent as claimed in claim 1 , wherein the receiver includes means for comparing the identity of an inventory-commence message with an identity of the inventory agent. | 0.564593 |
9,002,958 | 9 | 10 | 9. The method of claim 8 further including automatically linking, by the computer system, the performance feedback information to the performance review document in response to designation of the selected text as feedback or in response to association of the performance feedback information with the performance review document. | 9. The method of claim 8 further including automatically linking, by the computer system, the performance feedback information to the performance review document in response to designation of the selected text as feedback or in response to association of the performance feedback information with the performance review document. 10. The method of claim 9 further including automatically embedding, by the computer system, the performance feedback information in the performance review document. | 0.925339 |
9,135,625 | 6 | 8 | 6. A computer-implemented method of determining whether a business listing is legitimate, the method comprising: accessing, by a processor, a list of legitimate business titles, each legitimate business title including one or more words; generating a matrix of surprisingness values, where each surprisingness value indicates a likelihood of a word appearing in a legitimate business title, by: examining each legitimate business title to identify pairs of words occurring in that title, adding a count value to the matrix for each pair of words identified, such that the matrix includes a plurality of count values for different pairs of words occurring in the legitimate business titles, and normalizing the plurality of count values for the matrix to generate the matrix of surprisingness values, where each surprisingness value of the matrix of surprisingness values indicates how likely a pair of words are to appear in a legitimate business title; storing the matrix of surprisingness values in memory; accessing a first plurality of business listings each associated with title data including two or more words; identifying, from the first plurality of business listings, a second plurality of business listings all corresponding to one particular business; for each business listing of the identified second plurality of business listings, determining a surprisingness value indicative of the surprisingness of the title included in the particular business listing based on the stored matrix of surprisingness values; determining an average surprisingness value for the identified second plurality of business listings based on the stored matrix of surprisingness values; selecting a particular business listing of the identified second plurality of business listings; and determining whether the particular business listing is legitimate based on whether the surprisingness value for the particular business listing is greater than the average surprisingness value plus a threshold value. | 6. A computer-implemented method of determining whether a business listing is legitimate, the method comprising: accessing, by a processor, a list of legitimate business titles, each legitimate business title including one or more words; generating a matrix of surprisingness values, where each surprisingness value indicates a likelihood of a word appearing in a legitimate business title, by: examining each legitimate business title to identify pairs of words occurring in that title, adding a count value to the matrix for each pair of words identified, such that the matrix includes a plurality of count values for different pairs of words occurring in the legitimate business titles, and normalizing the plurality of count values for the matrix to generate the matrix of surprisingness values, where each surprisingness value of the matrix of surprisingness values indicates how likely a pair of words are to appear in a legitimate business title; storing the matrix of surprisingness values in memory; accessing a first plurality of business listings each associated with title data including two or more words; identifying, from the first plurality of business listings, a second plurality of business listings all corresponding to one particular business; for each business listing of the identified second plurality of business listings, determining a surprisingness value indicative of the surprisingness of the title included in the particular business listing based on the stored matrix of surprisingness values; determining an average surprisingness value for the identified second plurality of business listings based on the stored matrix of surprisingness values; selecting a particular business listing of the identified second plurality of business listings; and determining whether the particular business listing is legitimate based on whether the surprisingness value for the particular business listing is greater than the average surprisingness value plus a threshold value. 8. The method of claim 6 , further comprising associating each the surprisingness value with each business listing of the identified second plurality of business listings. | 0.667315 |
7,519,739 | 1 | 7 | 1. In a server system coupled to at least one client system, a method for synchronizing a user interface (UI) presentation to be displayed to a user of said at least one client system to a UI description maintained by said server system, said method comprising: converting said UI description into one or more UI object definitions; storing each said UI object definition in a document; and transmitting said document to said at least one client system, said at least one client system adapted to convert said UI object definitions to UI objects to generate said UI presentation at said client system in synch with said server system. | 1. In a server system coupled to at least one client system, a method for synchronizing a user interface (UI) presentation to be displayed to a user of said at least one client system to a UI description maintained by said server system, said method comprising: converting said UI description into one or more UI object definitions; storing each said UI object definition in a document; and transmitting said document to said at least one client system, said at least one client system adapted to convert said UI object definitions to UI objects to generate said UI presentation at said client system in synch with said server system. 7. The method of claim 1 wherein said UI presentation is a graphical user interface (GUI) presentation. | 0.758216 |
10,007,662 | 65 | 66 | 65. The method of claim 64 , wherein the step of generating the dual SSM Matrix (D(S″, S′)) with exponential decay comprises the steps of increasing the histogram element h″ at a time of occurrence of a spike of that channel in S″, exponentially decreasing all elements of the histogram h″ after the time of occurrence, and sampling the value of the histogram h″ at the time of occurrence of a spike on a channel in S′, and adding the sampled vector to the column of D(S″, S′) that corresponds to the channel in S′ on which the spike occurred. | 65. The method of claim 64 , wherein the step of generating the dual SSM Matrix (D(S″, S′)) with exponential decay comprises the steps of increasing the histogram element h″ at a time of occurrence of a spike of that channel in S″, exponentially decreasing all elements of the histogram h″ after the time of occurrence, and sampling the value of the histogram h″ at the time of occurrence of a spike on a channel in S′, and adding the sampled vector to the column of D(S″, S′) that corresponds to the channel in S′ on which the spike occurred. 66. The method of claim 65 , wherein the first time and the second time are synchronized. | 0.960896 |
10,073,860 | 12 | 15 | 12. A non-transitory computer-readable storage medium storing computer executable instructions that when executed by a processor perform operations comprising: determining media content text data representing a plurality of words output in a subset of media content during presentation of the media content; determining a first color palette based at least in part on a first keyword of the media content text data textually corresponding to a first name of the first color palette, the first color palette comprising a plurality of colors, wherein determining the first color palette based at least in part on the first keyword of the media content text data textually corresponding to the first name of the first color palette further comprises at least one of: determining that the first name of the first color palette includes the first keyword of the media content text data, or determining that the first name of the first color palette is related to the first keyword of the media content text data using natural language processing; retrieving the plurality of colors from the first color palette; determining ranking data, wherein determining the ranking data comprises: calculating a first cumulative score for a first color of the plurality of colors and a second cumulative score for a second color of the plurality of colors, wherein calculating the first cumulative score and the second cumulative score further comprises: aggregating a first weight for each color palette of a plurality of color palettes comprising the first color; and aggregating a second weight for each color palette of the plurality of color palettes comprising the second color; selecting the first color from the plurality of colors based at least in part on the ranking data that includes the first cumulative score and the second cumulative score; and causing display of the first color during presentation of the media content. | 12. A non-transitory computer-readable storage medium storing computer executable instructions that when executed by a processor perform operations comprising: determining media content text data representing a plurality of words output in a subset of media content during presentation of the media content; determining a first color palette based at least in part on a first keyword of the media content text data textually corresponding to a first name of the first color palette, the first color palette comprising a plurality of colors, wherein determining the first color palette based at least in part on the first keyword of the media content text data textually corresponding to the first name of the first color palette further comprises at least one of: determining that the first name of the first color palette includes the first keyword of the media content text data, or determining that the first name of the first color palette is related to the first keyword of the media content text data using natural language processing; retrieving the plurality of colors from the first color palette; determining ranking data, wherein determining the ranking data comprises: calculating a first cumulative score for a first color of the plurality of colors and a second cumulative score for a second color of the plurality of colors, wherein calculating the first cumulative score and the second cumulative score further comprises: aggregating a first weight for each color palette of a plurality of color palettes comprising the first color; and aggregating a second weight for each color palette of the plurality of color palettes comprising the second color; selecting the first color from the plurality of colors based at least in part on the ranking data that includes the first cumulative score and the second cumulative score; and causing display of the first color during presentation of the media content. 15. The non-transitory computer-readable storage medium of claim 12 , wherein the media content comprises at least one of audio data or video data. | 0.869912 |
9,858,324 | 1 | 6 | 1. A method of extracting unclassified data from a collection of data including both classified data and unclassified data, the method comprising: providing a plain text format file including a plurality of attributes in a computer system that includes a classified environment having a collection of data, which includes messages of both classified binary data and unclassified binary data, wherein the plain text format file describes each of the attributes in a message data structure in a classified network on a message type basis, wherein at least one of the attributes comprises a security mark; executing a software application within the classified environment, the software application comprising a trusted download toolkit programmed for: (a) processing at least one message contained within the collection of data by receiving target input data associated with the message, wherein the target input data comprises a binary data stream comprising classified binary data, a binary data stream comprising unclassified binary data, or a combination thereof, (b) identifying security mark data in at least one of the binary data streams, and (c) identifying a level of classification for at least a portion of the target input data in response to the security mark data; wherein the trusted download toolkit is independent of the message data structure within the classified environment and extracting at least a portion of the identified unclassified binary data from the collection of data in the classified environment in response to the identified level of classification. | 1. A method of extracting unclassified data from a collection of data including both classified data and unclassified data, the method comprising: providing a plain text format file including a plurality of attributes in a computer system that includes a classified environment having a collection of data, which includes messages of both classified binary data and unclassified binary data, wherein the plain text format file describes each of the attributes in a message data structure in a classified network on a message type basis, wherein at least one of the attributes comprises a security mark; executing a software application within the classified environment, the software application comprising a trusted download toolkit programmed for: (a) processing at least one message contained within the collection of data by receiving target input data associated with the message, wherein the target input data comprises a binary data stream comprising classified binary data, a binary data stream comprising unclassified binary data, or a combination thereof, (b) identifying security mark data in at least one of the binary data streams, and (c) identifying a level of classification for at least a portion of the target input data in response to the security mark data; wherein the trusted download toolkit is independent of the message data structure within the classified environment and extracting at least a portion of the identified unclassified binary data from the collection of data in the classified environment in response to the identified level of classification. 6. The method of claim 1 , wherein the collection of data includes nested data. | 0.829004 |
9,721,207 | 12 | 13 | 12. The computer program product of claim 11 , further comprising inputting user defined metrics via a user interface, wherein the ranking factors include at least one of the user defined metrics. | 12. The computer program product of claim 11 , further comprising inputting user defined metrics via a user interface, wherein the ranking factors include at least one of the user defined metrics. 13. The computer program product of claim 12 , further comprising: setting a scope of the data to be imported via the user interface. | 0.961935 |
8,510,650 | 1 | 16 | 1. A computerized method for constructing, analyzing, displaying, and editing a mathematical formula entered by a user for use in a software application, said computerized method comprising the acts of: responsive to receiving a mathematical formula entered into said software application by said user, said computerized method providing, to a display device viewable by said user, an application-preferred view of said mathematical formula, said application-preferred view suitable for use in said software application utilizing said mathematical formula; responsive to receiving a mathematical formula entered into said software application by said user, said computerized method providing, to said display device viewable by said user, one or more additional views of said mathematical formula, said one or more additional views different from said application-preferred view, wherein at least one of said one or more additional views of said mathematical formula displays a hierarchical diagram graphically clarifying the structure of said mathematical formula; and responsive to providing said one or more additional views of said mathematical formula, providing one or more computerized means configured for synchronizing the display of said application-preferred and said one or more additional views of said mathematical formula. | 1. A computerized method for constructing, analyzing, displaying, and editing a mathematical formula entered by a user for use in a software application, said computerized method comprising the acts of: responsive to receiving a mathematical formula entered into said software application by said user, said computerized method providing, to a display device viewable by said user, an application-preferred view of said mathematical formula, said application-preferred view suitable for use in said software application utilizing said mathematical formula; responsive to receiving a mathematical formula entered into said software application by said user, said computerized method providing, to said display device viewable by said user, one or more additional views of said mathematical formula, said one or more additional views different from said application-preferred view, wherein at least one of said one or more additional views of said mathematical formula displays a hierarchical diagram graphically clarifying the structure of said mathematical formula; and responsive to providing said one or more additional views of said mathematical formula, providing one or more computerized means configured for synchronizing the display of said application-preferred and said one or more additional views of said mathematical formula. 16. The method of claim 1 , wherein said one or more additional views of said mathematical formula displays a list of constituent phrases in said mathematical formula along with potential abbreviations or aliases for said constituent phrases. | 0.781982 |
9,672,251 | 6 | 7 | 6. The method of claim 5 , wherein determining the respective score for each candidate additional fact comprises: determining a respective combined score for each pattern used to generate the candidate additional fact; and determining the score for the candidate additional fact from the combined scores of the patterns used to generate the candidate additional fact. | 6. The method of claim 5 , wherein determining the respective score for each candidate additional fact comprises: determining a respective combined score for each pattern used to generate the candidate additional fact; and determining the score for the candidate additional fact from the combined scores of the patterns used to generate the candidate additional fact. 7. The method of claim 6 , wherein determining the respective combined score for each pattern used to generate the candidate additional fact comprises: determining a frequency score for the pattern from a total number of extractions generated by applying the pattern; determining a coherence score for the pattern that measures how semantically related the attributes for which the pattern generates facts are; and determining the combined score for the pattern by combining the frequency score and the coherence score. | 0.883266 |
9,424,249 | 8 | 15 | 8. A system, comprising: at least one computing device; and a plurality of computer instructions executable by the at least one computing device, wherein the plurality of computer instructions, when executed, cause the at least one computing device to at least: designate a series of characters in a text block as being a text unit; bind the series of characters together in response to the series of characters being designated as the text unit; assign a label to the text unit based at least upon content in the text unit, wherein the label specifies that the text unit is a particular class of text unit; encode the text block to generate an encoded text block for an application, the encoded text block specifying the label for the text unit and comprising a first signal that instructs an application to: cause an entirety of the series of characters in the text unit to be selected in response to a first selection of a subset of the series of characters; and cause a text format of the text unit to be visually contrasted from a remainder of the text block; decode the encoded text block to generate a decoded text block, the decoded text block comprising the series of characters bound as the text unit; and encode, in response to a second selection of the subset of the series of characters in the decoded text block, the decoded text block to generate an additional encoded text block, wherein the additional encoded text block comprises metadata indicating an unbinding of the series of characters as the text unit and comprises a second signal that instructs the application to: cause the label to be removed; and cause the entirety of the series of characters to be treated as being unbound. | 8. A system, comprising: at least one computing device; and a plurality of computer instructions executable by the at least one computing device, wherein the plurality of computer instructions, when executed, cause the at least one computing device to at least: designate a series of characters in a text block as being a text unit; bind the series of characters together in response to the series of characters being designated as the text unit; assign a label to the text unit based at least upon content in the text unit, wherein the label specifies that the text unit is a particular class of text unit; encode the text block to generate an encoded text block for an application, the encoded text block specifying the label for the text unit and comprising a first signal that instructs an application to: cause an entirety of the series of characters in the text unit to be selected in response to a first selection of a subset of the series of characters; and cause a text format of the text unit to be visually contrasted from a remainder of the text block; decode the encoded text block to generate a decoded text block, the decoded text block comprising the series of characters bound as the text unit; and encode, in response to a second selection of the subset of the series of characters in the decoded text block, the decoded text block to generate an additional encoded text block, wherein the additional encoded text block comprises metadata indicating an unbinding of the series of characters as the text unit and comprises a second signal that instructs the application to: cause the label to be removed; and cause the entirety of the series of characters to be treated as being unbound. 15. The system of claim 8 , wherein the plurality of computer instructions further cause the at least one computing device to at least format a presentation of the text unit to distinguish the text unit from at least a portion of the text block. | 0.704819 |
9,437,195 | 17 | 19 | 17. The method of claim 14 wherein generating the phrase is performed by searching for a phrase in the library that contains user distinguishing phonemes. | 17. The method of claim 14 wherein generating the phrase is performed by searching for a phrase in the library that contains user distinguishing phonemes. 19. The system of claim 17 wherein the generated phrase contains at least two instances of a user distinguishing phoneme. | 0.9566 |
7,702,458 | 1 | 2 | 1. A method for entering a street name to determine an address of a destination for a navigation system, comprising the following steps of: displaying a screen for searching a street name, the screen including a street name input field for accepting a user's input of characters either by a full name or a base name of a street; distinguishing a non-base name element from a base name of a street name in the input character and displaying the non-base name element by a selected method on the screen; comparing the base name detected from the characters input by the user with entries in a base name database that stores base names of streets; retrieving base names from the base name database that match the base name detected from the characters input by the user and displaying a list of the retrieved base names; repeating the above steps of comparing and retrieving the base names every time when additional information is supplied by the user; and retrieving full names of streets from a full name database that stores full names of streets to determine a correct address of the destination. | 1. A method for entering a street name to determine an address of a destination for a navigation system, comprising the following steps of: displaying a screen for searching a street name, the screen including a street name input field for accepting a user's input of characters either by a full name or a base name of a street; distinguishing a non-base name element from a base name of a street name in the input character and displaying the non-base name element by a selected method on the screen; comparing the base name detected from the characters input by the user with entries in a base name database that stores base names of streets; retrieving base names from the base name database that match the base name detected from the characters input by the user and displaying a list of the retrieved base names; repeating the above steps of comparing and retrieving the base names every time when additional information is supplied by the user; and retrieving full names of streets from a full name database that stores full names of streets to determine a correct address of the destination. 2. A method for entering a street name as defined in claim 1 , wherein said step of displaying the non-base name element in the input characters by a selected method includes a step of changing a color or brightness of the non-base name element from that of the base name. | 0.675418 |
7,961,943 | 20 | 21 | 20. The method according to claim 1 wherein the executing said one or more machine executable processes to embed the embeddable revisions associated with said selected elementary units includes emulating keystrokes. | 20. The method according to claim 1 wherein the executing said one or more machine executable processes to embed the embeddable revisions associated with said selected elementary units includes emulating keystrokes. 21. The method according to claim 20 wherein a macro is assigned to a keystroke within the emulated keystrokes. | 0.985981 |
7,966,556 | 1 | 5 | 1. A method comprising: receiving from a reviewer a plurality of comments about a word processing document, the comments including a first and second comment; associating the comments with the word processing document; assigning the comments to a plurality of editors, including assigning the first comment to a first editor and assigning the second comment to a second editor; and presenting at least one of the first comment to the first editor and the second comment to the second editor. | 1. A method comprising: receiving from a reviewer a plurality of comments about a word processing document, the comments including a first and second comment; associating the comments with the word processing document; assigning the comments to a plurality of editors, including assigning the first comment to a first editor and assigning the second comment to a second editor; and presenting at least one of the first comment to the first editor and the second comment to the second editor. 5. The method of claim 1 , further comprising: receiving from the first editor input requesting reassignment of the first comment from the first editor to the second editor; and in response to the input, performing the requested reassignment. | 0.759443 |
8,180,788 | 12 | 15 | 12. An image search server that supports delivery of search result pages of images to a client device, the image search server comprising: a processor; a memory communicatively coupled to the processor; and a program of instructions to be stored in the memory and executed by the processor, the program of instructions comprising: an image correlation module configured to correlate characteristic parameters of a search image with that of a plurality of images in a database, the image correlation module further configured to respond to the client device sending a search string and search image to the image search server by selecting, from a plurality of images in a database, a first set of images having characteristic parameters correlating to the characteristic parameters of the search image; an image text search module configured to match words in a search string with that of titles of the plurality of images in the database, the image text search module further configured to respond to the client device sending a search string and search image to the image search server by selecting, from a plurality of images in a database, a second set of images having titles matching words in the search string; an adult content filter module configured to filter adult content in the plurality of images in the database, the adult content filter module further configured to filter adult content from images selected using the search string and search image in the plurality of images in the database; the client device sends a search string and search image to the image search server; and the image search server configured to deliver a first search result page comprising a first few images of the first set of images and a second few images of the second set of. | 12. An image search server that supports delivery of search result pages of images to a client device, the image search server comprising: a processor; a memory communicatively coupled to the processor; and a program of instructions to be stored in the memory and executed by the processor, the program of instructions comprising: an image correlation module configured to correlate characteristic parameters of a search image with that of a plurality of images in a database, the image correlation module further configured to respond to the client device sending a search string and search image to the image search server by selecting, from a plurality of images in a database, a first set of images having characteristic parameters correlating to the characteristic parameters of the search image; an image text search module configured to match words in a search string with that of titles of the plurality of images in the database, the image text search module further configured to respond to the client device sending a search string and search image to the image search server by selecting, from a plurality of images in a database, a second set of images having titles matching words in the search string; an adult content filter module configured to filter adult content in the plurality of images in the database, the adult content filter module further configured to filter adult content from images selected using the search string and search image in the plurality of images in the database; the client device sends a search string and search image to the image search server; and the image search server configured to deliver a first search result page comprising a first few images of the first set of images and a second few images of the second set of. 15. The image search server of claim 12 , the program of instructions further comprising an image listing module. | 0.728365 |
8,645,390 | 4 | 6 | 4. The method of claim 1 , wherein each search context is associated with a respective group of users and a respective class of search queries. | 4. The method of claim 1 , wherein each search context is associated with a respective group of users and a respective class of search queries. 6. The method of claim 4 , wherein the respective class for a particular search query is a query type determined in accordance with one or more of the terms of the particular search query. | 0.949244 |
7,895,137 | 1 | 2 | 1. A method of transforming data into computer executable rules for mining and constructing situation categories that are applied to information technology resource messages or events comprising: receiving computer readable data by a computer processing device from at least one of: a raw log and a catalog, where the received data is at least one of: initial seed data and knowledge data, to derive the computer executable rules for mining and constructing situation categories; transforming the received data into a predetermined standard format if the received data is not already in the predetermined standard format; parsing the predetermined standard formatted data; performing an outer, iterative loop until at least one predetermined stopping criterion is met, comprising: utilizing a keyword rule classifier by the computer processing device to automatically pre-classify at least a portion of the parsed data; performing an inner iterative loop within the outer iterative loop, comprising: selecting a subset of the parsed data for expert review; using at least one of keyword rules, features, and classifications to find, within data available to the computer processing device, a corresponding previously labeled subset of data that has similar semantics to semantics of the selected subset of data; labeling the selected subset of data with the label associated with the corresponding previously labeled subset of data; and repeating the inner iterative loop if another subset of data is to be processed; storing each labeled subset of data on a data storage device; generating new computer executable rules for mining and constructing situation categories from the stored labeled subsets of data; transforming keyword list classifiers using the stored labeled subsets of data; and repeating the outer iterative loop if the predetermined stopping criterion is not met. | 1. A method of transforming data into computer executable rules for mining and constructing situation categories that are applied to information technology resource messages or events comprising: receiving computer readable data by a computer processing device from at least one of: a raw log and a catalog, where the received data is at least one of: initial seed data and knowledge data, to derive the computer executable rules for mining and constructing situation categories; transforming the received data into a predetermined standard format if the received data is not already in the predetermined standard format; parsing the predetermined standard formatted data; performing an outer, iterative loop until at least one predetermined stopping criterion is met, comprising: utilizing a keyword rule classifier by the computer processing device to automatically pre-classify at least a portion of the parsed data; performing an inner iterative loop within the outer iterative loop, comprising: selecting a subset of the parsed data for expert review; using at least one of keyword rules, features, and classifications to find, within data available to the computer processing device, a corresponding previously labeled subset of data that has similar semantics to semantics of the selected subset of data; labeling the selected subset of data with the label associated with the corresponding previously labeled subset of data; and repeating the inner iterative loop if another subset of data is to be processed; storing each labeled subset of data on a data storage device; generating new computer executable rules for mining and constructing situation categories from the stored labeled subsets of data; transforming keyword list classifiers using the stored labeled subsets of data; and repeating the outer iterative loop if the predetermined stopping criterion is not met. 2. The method of claim 1 , wherein labeling the selected subset of data comprises re-labeling previously labeled data in the selected subset of data. | 0.968841 |
10,049,676 | 3 | 4 | 3. The system of claim 1 , wherein a subset of the one or more recognizers is configured to provide a confidence metric to the recognition decision engine, the recognition decision engine using the confidence metric in the dynamically selecting. | 3. The system of claim 1 , wherein a subset of the one or more recognizers is configured to provide a confidence metric to the recognition decision engine, the recognition decision engine using the confidence metric in the dynamically selecting. 4. The system of claim 3 , wherein the confidence metric includes a threshold, the threshold varying based on resource availability. | 0.914948 |
8,676,891 | 3 | 4 | 3. The system of claim 1 , wherein the second data further comprises one or more classifications of the one or more first contacts associated with the author user, each of the one or more classifications having been defined by the author user using the social networking service. | 3. The system of claim 1 , wherein the second data further comprises one or more classifications of the one or more first contacts associated with the author user, each of the one or more classifications having been defined by the author user using the social networking service. 4. The system of claim 3 , wherein at least one of the classifications comprises a particular social circle defined by the author user. | 0.971757 |
4,675,840 | 17 | 18 | 17. A computer system as defined in claim 16 wherein said memory address signal generating means further includes: counter means, connected to the address inputs of said speech memory, selecting the locations of said memory which will be read from or written into; wherein said counter means is incremented in response to said memory address signal generating means accessing said speech memory; and means responsive to a control signal from said computer for generating a reset signal which is applied to said counter means to reset the location of said speech memory addressed to its initial location. | 17. A computer system as defined in claim 16 wherein said memory address signal generating means further includes: counter means, connected to the address inputs of said speech memory, selecting the locations of said memory which will be read from or written into; wherein said counter means is incremented in response to said memory address signal generating means accessing said speech memory; and means responsive to a control signal from said computer for generating a reset signal which is applied to said counter means to reset the location of said speech memory addressed to its initial location. 18. A computer system as defined in claim 17 which further includes: buffer means, responsive to a read data signal, for connecting to output of said speech memory to said data bus of the computer. | 0.913216 |
7,849,048 | 1 | 30 | 1. A system for making unstructured data available to structured data tools comprising: a core server computer, wherein the core server computer performs steps comprising: accessing a source of unstructured data; reading the unstructured data from the source of unstructured data; sending the unstructured data to one or more transformation tools; parsing, via a natural-language processing transformation tool, the unstructured data to extract sentences from the unstructured data and then further extract from the extracted sentences sentence-level natural-language processed entities, wherein the sentence-level natural-language processed entities are at least noun phrases; extracting, via a linguistic processing transformation tool, sentence-level linguistically-processed relationships, wherein the sentence-level linguistically-processed relationships comprise associations between the sentence-level natural-language processed entities; sending the sentence-level natural-language processed entities and the sentence-level linguistically-processed relationships from the one or more transformation tools to a categorization tool; determining, via the categorization tool, categorization data elements present in each extracted sentence, wherein the categorization data elements are based on the sentence-level natural-language processed entities and the sentence-level linguistically-processed relationships, and are placed within predetermined categories, and a confidence level for each categorization data element, wherein the confidence level for each categorization data element combines one or more data points linked to the sentence-level natural-language processed entities and the sentence-level linguistically-processed relationships to create a statistically-oriented calculation of confidence assigned to the categorization data element; outputting the confidence level for at least one of the categorization data elements for use in structured data tools; and wherein the one or more data points are selected from the group consisting of: confidence score of value provided by the one or more transformation tools, number of relationships found in the source of unstructured data compared to the size of the source of unstructured data, average number of relationships per kilobyte for relationships of the same type as a selected relationship, number of entities found to be associated with a relationship compared to an average number of entities for relationships in a same hierarchy, number of times similar relationships have been found in the past, number of entities that are grouped together to form a master entity, a number of times an entity occurred in the source of unstructured data compared to the average number of occurrences for entities in the same hierarchy, weighted confidences based on hierarchy of a relationship or entity, measures of data extraction confidence integrated with the system via an analysis schema, measures based on a fullness of a relationship's attributes, measures based on the confluence of a same finding by multiple transformation tools, measures based on the source of the unstructured data, and combinations thereof. | 1. A system for making unstructured data available to structured data tools comprising: a core server computer, wherein the core server computer performs steps comprising: accessing a source of unstructured data; reading the unstructured data from the source of unstructured data; sending the unstructured data to one or more transformation tools; parsing, via a natural-language processing transformation tool, the unstructured data to extract sentences from the unstructured data and then further extract from the extracted sentences sentence-level natural-language processed entities, wherein the sentence-level natural-language processed entities are at least noun phrases; extracting, via a linguistic processing transformation tool, sentence-level linguistically-processed relationships, wherein the sentence-level linguistically-processed relationships comprise associations between the sentence-level natural-language processed entities; sending the sentence-level natural-language processed entities and the sentence-level linguistically-processed relationships from the one or more transformation tools to a categorization tool; determining, via the categorization tool, categorization data elements present in each extracted sentence, wherein the categorization data elements are based on the sentence-level natural-language processed entities and the sentence-level linguistically-processed relationships, and are placed within predetermined categories, and a confidence level for each categorization data element, wherein the confidence level for each categorization data element combines one or more data points linked to the sentence-level natural-language processed entities and the sentence-level linguistically-processed relationships to create a statistically-oriented calculation of confidence assigned to the categorization data element; outputting the confidence level for at least one of the categorization data elements for use in structured data tools; and wherein the one or more data points are selected from the group consisting of: confidence score of value provided by the one or more transformation tools, number of relationships found in the source of unstructured data compared to the size of the source of unstructured data, average number of relationships per kilobyte for relationships of the same type as a selected relationship, number of entities found to be associated with a relationship compared to an average number of entities for relationships in a same hierarchy, number of times similar relationships have been found in the past, number of entities that are grouped together to form a master entity, a number of times an entity occurred in the source of unstructured data compared to the average number of occurrences for entities in the same hierarchy, weighted confidences based on hierarchy of a relationship or entity, measures of data extraction confidence integrated with the system via an analysis schema, measures based on a fullness of a relationship's attributes, measures based on the confluence of a same finding by multiple transformation tools, measures based on the source of the unstructured data, and combinations thereof. 30. The system of claim 1 , wherein the confidence level is assigned to individual categorization data elements. | 0.962008 |
9,160,719 | 9 | 14 | 9. A system for providing security in electronic messaging comprising: an input device that receives a message input; a processor configured to encode said message input to provide ciphertext, to further transform said ciphertext to disguise the ciphertext and make it appear to be similar to or indistinguishable from, at least by certain automated processes, a natural language short message; and a message transmitter that transmits said further-transformed ciphertext as a short message over an electronic network using a short-messaging protocol. | 9. A system for providing security in electronic messaging comprising: an input device that receives a message input; a processor configured to encode said message input to provide ciphertext, to further transform said ciphertext to disguise the ciphertext and make it appear to be similar to or indistinguishable from, at least by certain automated processes, a natural language short message; and a message transmitter that transmits said further-transformed ciphertext as a short message over an electronic network using a short-messaging protocol. 14. The system according to claim 9 , wherein the processor is further configured to generate individual sentence parameters including a number of words to a sentence, where to insert punctuation marks, and other characteristics in order to make the transformed ciphertext appear as natural language of at least one short message. | 0.501511 |
6,031,537 | 9 | 10 | 9. A method for organizing and processing information using a computer, said information comprising a plurality of thoughts, and said method comprising the steps of: defining a matrix comprising the plurality of thoughts and further comprising a plurality of network relationships among the thoughts, wherein each thought may be related to at least one other of said thoughts, and wherein at least one of the thoughts is directly related to one of the other thoughts, wherein said network relationships may be forgotten; displaying an indicium of a first thought as a central thought on a display; displaying an indicium of a second thought on the display, wherein the second thought having a direct relation to the first thought; and selecting said second thought to be a new central thought, whereby indicia of those thoughts having defined relations with the second thought will be displayed on the display. | 9. A method for organizing and processing information using a computer, said information comprising a plurality of thoughts, and said method comprising the steps of: defining a matrix comprising the plurality of thoughts and further comprising a plurality of network relationships among the thoughts, wherein each thought may be related to at least one other of said thoughts, and wherein at least one of the thoughts is directly related to one of the other thoughts, wherein said network relationships may be forgotten; displaying an indicium of a first thought as a central thought on a display; displaying an indicium of a second thought on the display, wherein the second thought having a direct relation to the first thought; and selecting said second thought to be a new central thought, whereby indicia of those thoughts having defined relations with the second thought will be displayed on the display. 10. The method of claim 9 wherein said network relationships may be forgotten automatically in accordance with particular usage statistics. | 0.911012 |
7,698,326 | 19 | 20 | 19. A memory device having stored thereon sequences of instructions executable by at least one processor, the memory device comprising: one or more instructions to access web sites based on input from a user; one or more instructions to store, in a cache, web site identifiers and web site content associated with accessed web sites; one or more instructions to receive one or more characters from the user via an alpha-numeric keypad; one or more instructions to search a database to identify at least one first potential word or identifier based on receiving a first input from the user, where the database is different from the cache and stores words and phrases based on text included in messages sent or received by the user; one or more instructions to receive an indication that the user did not select the identified at least one first potential word or identifier, based on receiving additional input from the user; one or more instructions to search the web site identifiers and web site content stored in the cache to identify at least one second potential word or identifier which the user intends to input, based on the received first input and based on the received additional input; and one or more instructions to provide the identified at least one second potential word or identifier to the user via a display. | 19. A memory device having stored thereon sequences of instructions executable by at least one processor, the memory device comprising: one or more instructions to access web sites based on input from a user; one or more instructions to store, in a cache, web site identifiers and web site content associated with accessed web sites; one or more instructions to receive one or more characters from the user via an alpha-numeric keypad; one or more instructions to search a database to identify at least one first potential word or identifier based on receiving a first input from the user, where the database is different from the cache and stores words and phrases based on text included in messages sent or received by the user; one or more instructions to receive an indication that the user did not select the identified at least one first potential word or identifier, based on receiving additional input from the user; one or more instructions to search the web site identifiers and web site content stored in the cache to identify at least one second potential word or identifier which the user intends to input, based on the received first input and based on the received additional input; and one or more instructions to provide the identified at least one second potential word or identifier to the user via a display. 20. The memory device of claim 19 , further comprising instructions for causing the at least one processor to: receive selections from the user corresponding to words or identifiers provided via the display and that were identified from the cache; and store the selected words or identifiers in the database, the database corresponding to a more permanent database than the cache. | 0.730114 |
7,680,812 | 73 | 74 | 73. The method of claim 72 , further comprising: truncating said hyperlink neighborhood. | 73. The method of claim 72 , further comprising: truncating said hyperlink neighborhood. 74. The method of claim 73 , further comprising: displaying said hyperlink neighborhood. | 0.969759 |
9,319,469 | 18 | 19 | 18. The method of claim 14 characterized in that said metadata response to said method call for getting metadata about the document comprises metadata selected from the group consisting of a file version identifier, a base filename, an owner identifier, a file size value, and a file hash code. | 18. The method of claim 14 characterized in that said metadata response to said method call for getting metadata about the document comprises metadata selected from the group consisting of a file version identifier, a base filename, an owner identifier, a file size value, and a file hash code. 19. The method of claim 18 characterized in that said metadata response to said method call for getting metadata about the document further comprises metadata selected from the group consisting of a client URL used to access the document provided by the host, a download URL used to trigger the native download function of a user agent, a close URL used when the document is closed, a host view URL used to access a view page provided by the host, a host edit URL used for access to an edit page provided by the host, a user write permission flag, a read only flag, a public flag, a hide formulas flag, an update support flag, a lock support flag indicating, a virtual application support flag, a container support flag, a file URL for directly accessing a document, a privacy URL, and a terms of use URL. | 0.814687 |
9,195,946 | 1 | 4 | 1. A system for maintaining a representative data set in a document classification system, the system comprising: a computer processor configured to: include an initial set of seed representative data in a representative data set (RDS) implemented for a knowledge base (KB), wherein the KB is trained to classify documents provided to a document classification system based on analysis of representative documents included in the RDS and a set of rules, wherein the seed representative data includes a balanced number of representative data across a plurality of classes; update the RDS by adding or removing representative data from the RDS based on feedback received about accuracy of classification of one or more documents by the classification system, wherein the representative data is associated with one or more classes in the plurality of classes; further update the RDS such that a number of classes with which the representative data is associated and the number of representative data in each class is adjusted to maintain a balanced inclusion of representative data in each class; and retrain the KB, wherein the retraining is performed based on occurrence of one or more events. | 1. A system for maintaining a representative data set in a document classification system, the system comprising: a computer processor configured to: include an initial set of seed representative data in a representative data set (RDS) implemented for a knowledge base (KB), wherein the KB is trained to classify documents provided to a document classification system based on analysis of representative documents included in the RDS and a set of rules, wherein the seed representative data includes a balanced number of representative data across a plurality of classes; update the RDS by adding or removing representative data from the RDS based on feedback received about accuracy of classification of one or more documents by the classification system, wherein the representative data is associated with one or more classes in the plurality of classes; further update the RDS such that a number of classes with which the representative data is associated and the number of representative data in each class is adjusted to maintain a balanced inclusion of representative data in each class; and retrain the KB, wherein the retraining is performed based on occurrence of one or more events. 4. The system of claim 1 wherein the one or more events comprise adopting a certain number of feedbacks by adding or deleting one or more representative data from the RDS. | 0.782995 |
8,903,811 | 1 | 5 | 1. A computer-implemented method comprising: generating a score for a subject and search keyword objects of target object provided by an online computer-based search directed to an object type, either by explicit or by behaviorally inferred subject responses, wherein the generated object score is based on relevancy to the subject and search keywords of the target objects provided by the online computer-based search; representing each of said subject and said objects in individualized vector form at a computer server, wherein the predicted relevancy of a target object to a search keyword object is generated by matching the search keyword object vector to the target object vector, and the predicted affinity of the subject to the target object is generated by matching the subject vector to the target object vector, further wherein said matching is calculated as the dot product between said vectors; generating search results by matching profiles of said subject and keywords with profiles in a target object catalog and ranking against said profile of said subject and keywords; and presenting said subject with top-ranking target objects, such that said top-ranking target objects are tailored to said subject; wherein the presented target objects are restricted to the object type of the online computer-based search; and wherein the computer server is further configured to generate the subject vector and the object vector by producing initial subject vectors and initial object vectors having respective initial dimensions, to determine predicted search relevance scores based on the initial subject vectors, initial object vectors, and subject search response data, and to calculate a cost function as the mean squared error between the predicted relevance scores and actual relevance scores across all said subject responses; and wherein the computer server iteratively increases the dimensions of the generated subject and object vectors and generates the values of the added dimensions of both said subject and object vectors to reduce the cost function based on the differences between the predicted relevance scores and actual relevance scores, until the cost function decreases to a predetermined value, and wherein the actual relevance scores are based on user input. | 1. A computer-implemented method comprising: generating a score for a subject and search keyword objects of target object provided by an online computer-based search directed to an object type, either by explicit or by behaviorally inferred subject responses, wherein the generated object score is based on relevancy to the subject and search keywords of the target objects provided by the online computer-based search; representing each of said subject and said objects in individualized vector form at a computer server, wherein the predicted relevancy of a target object to a search keyword object is generated by matching the search keyword object vector to the target object vector, and the predicted affinity of the subject to the target object is generated by matching the subject vector to the target object vector, further wherein said matching is calculated as the dot product between said vectors; generating search results by matching profiles of said subject and keywords with profiles in a target object catalog and ranking against said profile of said subject and keywords; and presenting said subject with top-ranking target objects, such that said top-ranking target objects are tailored to said subject; wherein the presented target objects are restricted to the object type of the online computer-based search; and wherein the computer server is further configured to generate the subject vector and the object vector by producing initial subject vectors and initial object vectors having respective initial dimensions, to determine predicted search relevance scores based on the initial subject vectors, initial object vectors, and subject search response data, and to calculate a cost function as the mean squared error between the predicted relevance scores and actual relevance scores across all said subject responses; and wherein the computer server iteratively increases the dimensions of the generated subject and object vectors and generates the values of the added dimensions of both said subject and object vectors to reduce the cost function based on the differences between the predicted relevance scores and actual relevance scores, until the cost function decreases to a predetermined value, and wherein the actual relevance scores are based on user input. 5. The method according to claim 1 , wherein said step of representing said subject and said objects in vector form includes: deriving said vector form solely from said subject's relevancy ratings of said objects weighting relevancy ratings of users designated as expert, higher than relevancy ratings of users designated as non-experts and increasing said weighting for repeated occurrences of either viewing or not viewing a search result, | 0.501131 |
8,086,039 | 1 | 10 | 1. A method of generating fine-grain fingerprints for identifying content in a rendered document, the method comprising: applying image-based techniques to identify patterns in a document rendered by an electronic document rendering system, irrespective of a file format in which the rendered document was electronically created, the applying of the image-based techniques including: i. scanning, by at least one electronic processor, the rendered document one scanline at a time to identify image pixels, wherein at each current pixel a determination is made by the at least one electronic processor whether the local neighborhood of pixels corresponding to a local image neighborhood is a member of a new or previously existing local image neighborhood, ii. identifying candidate keypoints at locations in a local image neighborhood of the document, by the at least one electronic processor, and iii. combining the locations of the candidate keypoints to form fine-grain fingerprints indentifying patterns representing content in the document, by the at least one electronic processor. | 1. A method of generating fine-grain fingerprints for identifying content in a rendered document, the method comprising: applying image-based techniques to identify patterns in a document rendered by an electronic document rendering system, irrespective of a file format in which the rendered document was electronically created, the applying of the image-based techniques including: i. scanning, by at least one electronic processor, the rendered document one scanline at a time to identify image pixels, wherein at each current pixel a determination is made by the at least one electronic processor whether the local neighborhood of pixels corresponding to a local image neighborhood is a member of a new or previously existing local image neighborhood, ii. identifying candidate keypoints at locations in a local image neighborhood of the document, by the at least one electronic processor, and iii. combining the locations of the candidate keypoints to form fine-grain fingerprints indentifying patterns representing content in the document, by the at least one electronic processor. 10. The method according to claim 1 wherein various types of fine-grain fingerprints can be computed from the candidate keypoint locations depending on the desired feature size and level of accuracy needed, including (i) word size fingerprints for matching document content and (ii) character or sub-character level fingerprints. | 0.926134 |
9,158,825 | 13 | 14 | 13. The computer-readable storage medium of claim 12 , wherein determining how much non-indexed information is stored in the data backup system as related to the search query comprises: querying a target image set related to the search query; and determining an amount of non-indexed information of the target image set. | 13. The computer-readable storage medium of claim 12 , wherein determining how much non-indexed information is stored in the data backup system as related to the search query comprises: querying a target image set related to the search query; and determining an amount of non-indexed information of the target image set. 14. The computer-readable storage medium of claim 13 , wherein the set of indexing options comprises: an indexing cost associated with indexing a portion of accessible non-indexed information, wherein the indexing cost comprises a value of computational cost associated with indexing the portion of accessible non-indexed information to increase relevancy of current searches and subsequent searches related to the search query. | 0.857428 |
9,026,904 | 4 | 5 | 4. The method of claim 1 , wherein: identifying a markup page of content items comprises: constructing a web portal as an aggregated markup page by aggregating at least a first subsection of content items and a second subsection of content items, where the first and second subsections are obtained from different sources so as to consolidate multiple applications and databases. | 4. The method of claim 1 , wherein: identifying a markup page of content items comprises: constructing a web portal as an aggregated markup page by aggregating at least a first subsection of content items and a second subsection of content items, where the first and second subsections are obtained from different sources so as to consolidate multiple applications and databases. 5. The method of claim 4 , wherein: each content item of the first subsection shares at least one super-theme and a first common theme, and each content item of the second subsection shares at least one super-theme and a second common theme, such that each super-theme of the first subsection is different than each super-theme of the second subsection. | 0.912667 |
8,706,742 | 4 | 5 | 4. The system according to claim 3 wherein said document discriminator comprises a metadata-based relevant-irrelevant document discriminator characterized in that said at least one metadata value correlates with relevance of said electronic documents to the issue. | 4. The system according to claim 3 wherein said document discriminator comprises a metadata-based relevant-irrelevant document discriminator characterized in that said at least one metadata value correlates with relevance of said electronic documents to the issue. 5. The system according to claim 4 wherein said at least one metadata value for at least one metadata parameter comprises a logical combination of conditions according to which a plurality of metadata parameters respectively assume a plurality of metadata ranges each range including at least one metadata value, wherein said logical combination is characterized in that documents from among said multiplicity of documents which satisfy said condition, statistically tend to be relevant to the issue, more than documents from among said multiplicity of documents, which do not satisfy said condition. | 0.890909 |
9,361,360 | 7 | 8 | 7. A non-transitory computer-readable storage medium encoded with computer-executable instructions for retrieving information from a semantic database having a plurality of semantic data in response to a query, which in response to execution by a computing device, causes the computing device to: translate each of the plurality of semantic data to a first-order logic formula constructed by one or more atomic symbols and operators; select a first semantic data as a hub in an offline environment from the plurality of semantic data, wherein the first semantic data is resolved with a number of semantic data based on a resolution rule, and the number of the semantic data resolved with the hub is greater than a threshold, wherein a first standard formula transformed from the translated first-order logic formula of the first semantic data is resolved with a second standard formula transformed from the translated first-order logic formula of any of the number of semantic data, and further wherein one atomic symbol of the atomic symbols exists in the first standard formula and the negation of the atomic symbol exists in the second standard formula; calculate the semantic data set by calculating in a first level of a searching approach, a first resolvent of (1) the hub and (2) a second semantic data which directly links to the hub based on a resolution rule, and in response to the second semantic data being resolved with the hub, selecting the second semantic data as a part of the semantic data set in the offline environment; calculate the semantic data set by calculating in a second level of the searching approach, a second resolvent of (1) the semantic data set resulted in the first level of the searching approach and (2) a third semantic data which is within a predetermined distance from the hub, and in response to the third semantic data being resolved with any semantic data of the semantic data set resulted in the first level of the searching approach, selecting the third semantic data as a part of the semantic data set in the offline environment, wherein the calculating of the semantic data set is continuously executed in a background of the semantic database until a particular calculation limit is reached; index the semantic data set in the offline environment; modify the semantic database to include the indexed semantic data set in the offline environment; and retrieve information from the semantic data set in an online environment in response to the query. | 7. A non-transitory computer-readable storage medium encoded with computer-executable instructions for retrieving information from a semantic database having a plurality of semantic data in response to a query, which in response to execution by a computing device, causes the computing device to: translate each of the plurality of semantic data to a first-order logic formula constructed by one or more atomic symbols and operators; select a first semantic data as a hub in an offline environment from the plurality of semantic data, wherein the first semantic data is resolved with a number of semantic data based on a resolution rule, and the number of the semantic data resolved with the hub is greater than a threshold, wherein a first standard formula transformed from the translated first-order logic formula of the first semantic data is resolved with a second standard formula transformed from the translated first-order logic formula of any of the number of semantic data, and further wherein one atomic symbol of the atomic symbols exists in the first standard formula and the negation of the atomic symbol exists in the second standard formula; calculate the semantic data set by calculating in a first level of a searching approach, a first resolvent of (1) the hub and (2) a second semantic data which directly links to the hub based on a resolution rule, and in response to the second semantic data being resolved with the hub, selecting the second semantic data as a part of the semantic data set in the offline environment; calculate the semantic data set by calculating in a second level of the searching approach, a second resolvent of (1) the semantic data set resulted in the first level of the searching approach and (2) a third semantic data which is within a predetermined distance from the hub, and in response to the third semantic data being resolved with any semantic data of the semantic data set resulted in the first level of the searching approach, selecting the third semantic data as a part of the semantic data set in the offline environment, wherein the calculating of the semantic data set is continuously executed in a background of the semantic database until a particular calculation limit is reached; index the semantic data set in the offline environment; modify the semantic database to include the indexed semantic data set in the offline environment; and retrieve information from the semantic data set in an online environment in response to the query. 8. The non-transitory computer-readable storage medium of claim 7 , further containing additional instructions, which in response to execution by the computing device, causes the computing device to repeatedly select the hub, calculate the semantic data set, index the semantic data set, and modify the semantic database in sequence. | 0.501497 |
8,392,429 | 6 | 10 | 6. A system comprising: one or more computers; and a computer-readable medium coupled to the one or more computers having instructions stored thereon which, when executed by the one or more computers, cause the one or more computers to perform operations comprising: receiving in a computer system a search query; determining, for each web page in a plurality of web pages that satisfy a search query and have a position in a ranked order of web pages that satisfy the search query, whether the web page is a reference page for a book, wherein a particular web page is determined to be a reference page for a particular book when (i) the web page includes a reference to the particular book and has a citation score that satisfies a citation criterion for the particular book, (ii) a citation score of the web page for the particular book exceeds a citation threshold, and (iii) citation scores of the web page for other books referenced on the web page are each less than the citation score for the particular book, wherein the citation score is a measure of relevance of book metadata for the particular book to content of the web page; selecting, from among the web pages, a subset of web pages that are each a reference page for a respective book; assigning a book score to each of the books for which there is at least one reference page in the subset of web pages, where each of the reference pages in the subset referencing a respective book contributes to the book score for the respective book; selecting one or more of the books based on the book scores; generating a book reference for each of the one or more books, wherein each book reference includes at least one of citation information of the respective book or a link to a page of the respective book; and providing, in a response to the search query the book references in addition to one or more web content references, each web content reference linking to one of the plurality of web pages. | 6. A system comprising: one or more computers; and a computer-readable medium coupled to the one or more computers having instructions stored thereon which, when executed by the one or more computers, cause the one or more computers to perform operations comprising: receiving in a computer system a search query; determining, for each web page in a plurality of web pages that satisfy a search query and have a position in a ranked order of web pages that satisfy the search query, whether the web page is a reference page for a book, wherein a particular web page is determined to be a reference page for a particular book when (i) the web page includes a reference to the particular book and has a citation score that satisfies a citation criterion for the particular book, (ii) a citation score of the web page for the particular book exceeds a citation threshold, and (iii) citation scores of the web page for other books referenced on the web page are each less than the citation score for the particular book, wherein the citation score is a measure of relevance of book metadata for the particular book to content of the web page; selecting, from among the web pages, a subset of web pages that are each a reference page for a respective book; assigning a book score to each of the books for which there is at least one reference page in the subset of web pages, where each of the reference pages in the subset referencing a respective book contributes to the book score for the respective book; selecting one or more of the books based on the book scores; generating a book reference for each of the one or more books, wherein each book reference includes at least one of citation information of the respective book or a link to a page of the respective book; and providing, in a response to the search query the book references in addition to one or more web content references, each web content reference linking to one of the plurality of web pages. 10. The system of claim 6 , wherein the operations further comprise: providing the book references in a web page in which the book references will be displayed distinctively from other search results. | 0.530516 |
9,607,436 | 1 | 15 | 1. A system to display augmented data, comprising: a processor; and a memory communicatively coupled to the processor, the memory bearing processor instructions that, when executed by the processor, cause the system to at least: determine augmentation data based on a context associated with a user device, the augmentation data comprising a plurality of augmentations, each of the plurality of augmentations associated with one of a plurality of physical venues including a certain physical venue, and wherein the context includes information regarding the user's environment; generate one or more clusters, each cluster of the one or more clusters comprising a subset of the plurality of augmentations, wherein each cluster of the one or more clusters represents one of the plurality of physical venues; grouping each of the plurality of augmentations into one of the one or more clusters based on a respective location of the plurality of physical venues; determine rendering formats for each of the one or more clusters, wherein the rendering formats determine at least a look of conceptual representations of the one or more clusters, wherein the look of the conceptual representation of a cluster is based on at least a first property of the subset of the plurality of augmentations grouped into the cluster, and wherein the conceptual representation is different from individual augmentations within the cluster, and wherein a respective rendering format for a certain cluster of the one or more clusters associated with the certain physical venue is based on the subset of the plurality of augmentations associated with the certain physical venue, and wherein the certain cluster of the one or more clusters comprises at least one lower-level cluster, wherein the rendering formats for the at least one lower-level cluster is based on an at least one second property of a second subset of the subset of the plurality of augmentations. | 1. A system to display augmented data, comprising: a processor; and a memory communicatively coupled to the processor, the memory bearing processor instructions that, when executed by the processor, cause the system to at least: determine augmentation data based on a context associated with a user device, the augmentation data comprising a plurality of augmentations, each of the plurality of augmentations associated with one of a plurality of physical venues including a certain physical venue, and wherein the context includes information regarding the user's environment; generate one or more clusters, each cluster of the one or more clusters comprising a subset of the plurality of augmentations, wherein each cluster of the one or more clusters represents one of the plurality of physical venues; grouping each of the plurality of augmentations into one of the one or more clusters based on a respective location of the plurality of physical venues; determine rendering formats for each of the one or more clusters, wherein the rendering formats determine at least a look of conceptual representations of the one or more clusters, wherein the look of the conceptual representation of a cluster is based on at least a first property of the subset of the plurality of augmentations grouped into the cluster, and wherein the conceptual representation is different from individual augmentations within the cluster, and wherein a respective rendering format for a certain cluster of the one or more clusters associated with the certain physical venue is based on the subset of the plurality of augmentations associated with the certain physical venue, and wherein the certain cluster of the one or more clusters comprises at least one lower-level cluster, wherein the rendering formats for the at least one lower-level cluster is based on an at least one second property of a second subset of the subset of the plurality of augmentations. 15. The system of claim 1 , wherein the scene is associated with a first set of coordinates and wherein the rendering formats are associated with a second set of coordinates, the system further comprising processor instructions that, when executed, cause the system to align the rendering formats with the image of the scene based on the first and second sets of coordinates. | 0.549279 |
9,880,699 | 4 | 5 | 4. A computer-readable storage device including instructions that, when executed by a programmable processor, cause performance of operations that comprise: receiving, by the computing system and during the presentation of the map view of the particular geographic area, an indication of a user input to select a first user interface element; displaying, by the computing system and as a result of having received the indication of the user input to select the first user interface element, a second user interface element that is configured to enable toggling from the presentation of the map view of the particular geographic area to a presentation of an overhead image view of the particular geographic area, wherein displaying the second user interface element as a result of having received the indication of the user input to select the first user interface element includes displaying the second user interface element in a menu of user interface elements that the computing system displays in response to having received the indication of the user input to select the first user interface element; receiving, by the computing system, an indication of a user input to select the second user interface element; toggling, by the computing system as a result of having received the indication of the user input to select the second user interface element, the presentation of the map view of the particular geographic area to the presentation of the overhead image view of the particular geographic area; and receiving, by the computing system and during the presentation of the overhead image view of the particular geographic area, an indication of another user input to select the first user interface element; and displaying, by the computing system as a result of receiving the indication of the another user input to select the first user interface element, the menu of user interface elements with the display of the second user interface element in the collection having changed from (i) presenting an indication that selection of the second user interface element toggles the presentation of the geographic area to the overhead image view, to (ii) presenting an indication that selection of the second user interface element toggles the presentation of the geographic area to the map view. | 4. A computer-readable storage device including instructions that, when executed by a programmable processor, cause performance of operations that comprise: receiving, by the computing system and during the presentation of the map view of the particular geographic area, an indication of a user input to select a first user interface element; displaying, by the computing system and as a result of having received the indication of the user input to select the first user interface element, a second user interface element that is configured to enable toggling from the presentation of the map view of the particular geographic area to a presentation of an overhead image view of the particular geographic area, wherein displaying the second user interface element as a result of having received the indication of the user input to select the first user interface element includes displaying the second user interface element in a menu of user interface elements that the computing system displays in response to having received the indication of the user input to select the first user interface element; receiving, by the computing system, an indication of a user input to select the second user interface element; toggling, by the computing system as a result of having received the indication of the user input to select the second user interface element, the presentation of the map view of the particular geographic area to the presentation of the overhead image view of the particular geographic area; and receiving, by the computing system and during the presentation of the overhead image view of the particular geographic area, an indication of another user input to select the first user interface element; and displaying, by the computing system as a result of receiving the indication of the another user input to select the first user interface element, the menu of user interface elements with the display of the second user interface element in the collection having changed from (i) presenting an indication that selection of the second user interface element toggles the presentation of the geographic area to the overhead image view, to (ii) presenting an indication that selection of the second user interface element toggles the presentation of the geographic area to the map view. 5. The computer-readable storage device of claim 4 , wherein each of the user interface elements in the menu of user interface elements other than the second user interface element remains the same as a result of (i) receiving the indication of the user input to select the first user interface element, and (ii) receiving the indication of the another user input to select the first user interface element. | 0.5375 |
7,814,161 | 31 | 36 | 31. A system for locating a certificate for an electronic messaging system, the system comprising: a plurality of devices capable of sending and receiving electronic messages; a server for processing at least some electronic messages; program logic executable by a processor and operable to search for a certificate of a sender or a recipient of an electronic message based on a canonical name associated with an address of the sender or the recipient respectively, wherein the address of the sender or the recipient comprises at least a first user name and at least a first domain name, and wherein the canonical name is the address of the sender or the recipient with both the first domain name truncated to identify a second domain name and the first user name truncated to identify a second user name. | 31. A system for locating a certificate for an electronic messaging system, the system comprising: a plurality of devices capable of sending and receiving electronic messages; a server for processing at least some electronic messages; program logic executable by a processor and operable to search for a certificate of a sender or a recipient of an electronic message based on a canonical name associated with an address of the sender or the recipient respectively, wherein the address of the sender or the recipient comprises at least a first user name and at least a first domain name, and wherein the canonical name is the address of the sender or the recipient with both the first domain name truncated to identify a second domain name and the first user name truncated to identify a second user name. 36. The system according to claim 31 , wherein before searching for the certificate of the sender or the recipient based on the canonical name associated with the address of the sender or the recipient respectively, said program logic operates to obtain the address of the sender or the recipient and to search for a certificate of the sender or the recipient based on a match with the address of the sender or the recipient respectively, wherein the program logic operates to automatically perform said searching for the certificate of the sender or the recipient based on the canonical name associated with the address of the sender or the recipient respectively if no match occurs when searching based on the address of the sender or the recipient, and provide an indication of whether the certificate of the sender or the recipient has been found. | 0.500587 |
4,477,880 | 10 | 11 | 10. The method recited in claim 9 further including the step of transferring feature programming modules from a feature program source diskette containing a selected feature program to said destination diskette, said feature programming module transfer step including first, transferring the said feature programming modules from said feature program source diskette to said destination diskette beginning with program modules which are assigned to the lowest order data sets while updating the corresponding said data set directories stored in said system and allocating space on said destination diskette for a new data set directory immediately following any feature programming modules previously transferred to said destination diskette, and recording all said transferred feature programming modules for that data set following said allocated space. | 10. The method recited in claim 9 further including the step of transferring feature programming modules from a feature program source diskette containing a selected feature program to said destination diskette, said feature programming module transfer step including first, transferring the said feature programming modules from said feature program source diskette to said destination diskette beginning with program modules which are assigned to the lowest order data sets while updating the corresponding said data set directories stored in said system and allocating space on said destination diskette for a new data set directory immediately following any feature programming modules previously transferred to said destination diskette, and recording all said transferred feature programming modules for that data set following said allocated space. 11. The method recited in claim 10 further including the step of transferring selected language modules and dictionaries from one of said program source diskettes to said destination diskette, said language module transfer step including first, transferring in data set order said language programming modules assigned to data sets having data set directories previously transferred to said destination diskette, allocating other data set directory space on said destination diskette, and transferring any remaining language programming modules to said destination diskette immediately following said other directory space. | 0.787951 |
9,104,689 | 10 | 12 | 10. The method of claim 1 wherein the client database includes a plurality of client documents, the method further comprising designating for deletion one of the client documents based on a document score of a complementary document in the server database. | 10. The method of claim 1 wherein the client database includes a plurality of client documents, the method further comprising designating for deletion one of the client documents based on a document score of a complementary document in the server database. 12. The method of claim 10 further comprising increasing a data storage capacity of the client by deleting the one of the client documents designated for deletion. | 0.954947 |
8,300,023 | 28 | 38 | 28. A computing device coupled to a touch sensitive surface, comprising: means for receiving a series of coordinates of a series of user touches on the touch sensitive surface; means for correlating a plurality of keys with the received series of coordinates; means for determining an average of received coordinates correlated with each of the plurality of keys, wherein the means for determining the average of received coordinates correlated with each of the plurality of keys comprises: means for calculating a variability of key-strike locations for each of the plurality of keys; means for determining when the calculated variability of key-strike locations for each of the plurality of keys has plateaued; and means for calculating an average of coordinate key strike locations for each of the plurality of keys once the variability of key strike locations has plateaued; means for saving the calculated average coordinates for each of the plurality of keys in keypad layout data; and means for generating an image of a virtual keypad using the keypad layout data. | 28. A computing device coupled to a touch sensitive surface, comprising: means for receiving a series of coordinates of a series of user touches on the touch sensitive surface; means for correlating a plurality of keys with the received series of coordinates; means for determining an average of received coordinates correlated with each of the plurality of keys, wherein the means for determining the average of received coordinates correlated with each of the plurality of keys comprises: means for calculating a variability of key-strike locations for each of the plurality of keys; means for determining when the calculated variability of key-strike locations for each of the plurality of keys has plateaued; and means for calculating an average of coordinate key strike locations for each of the plurality of keys once the variability of key strike locations has plateaued; means for saving the calculated average coordinates for each of the plurality of keys in keypad layout data; and means for generating an image of a virtual keypad using the keypad layout data. 38. The computing device of claim 28 , further comprising: means for receiving a user input to select and move a key relative to another key; and means for adjusting layout dimensions of the generated image of the virtual keypad based upon the user input and generating another image of the virtual keypad. | 0.753226 |
10,095,692 | 11 | 17 | 11. A system for bootstrapping a set of templates for generating natural language sentences, the system comprising: a) at least one database comprising a corpus of documents; b) a computer comprising a processor and a memory, the memory containing a set of executable code executable by the processor; c) a search controller configured to receive a set of original templates and generate a query based on the set of original templates; d) a search engine adapted to receive the query from the search controller and search the corpus of documents using the query based on the set of original templates to identify a set of candidate sentences from the corpus of documents; e) a template analyzer adapted to: i) select a set of similar sentences from the identified set of candidate sentences by using a similarity measure to determine a similarity score for each selected sentence, wherein the similarity measure comprises extracting a first set of tokens from at least one template from the set of original templates and extracting a second set of tokens from at least one candidate sentence from the set of candidate sentences, the first set of tokens and the second set of tokens each comprising a set of token-level 1 to token-level n grams, and further comprises comparing the extracted first set of tokens with the extracted second set of tokens by determining a first value representing an intersection of the extracted first and second sets of tokens, and dividing that first value by a second value derived by applying a minimum function to the extracted first and second sets of tokens to determine the similarity score; ii) automatically eliminate candidate sentences from the set of candidate sentences based upon a similarity score threshold to arrive at a reduced set of candidate sentences determined to be syntactically similar to the at least one template; and iii) generate a set of natural language generation templates based at least in part on the similarity scores that, when processed by a computer and combined with a set of determined words or phrases, generate natural language text. | 11. A system for bootstrapping a set of templates for generating natural language sentences, the system comprising: a) at least one database comprising a corpus of documents; b) a computer comprising a processor and a memory, the memory containing a set of executable code executable by the processor; c) a search controller configured to receive a set of original templates and generate a query based on the set of original templates; d) a search engine adapted to receive the query from the search controller and search the corpus of documents using the query based on the set of original templates to identify a set of candidate sentences from the corpus of documents; e) a template analyzer adapted to: i) select a set of similar sentences from the identified set of candidate sentences by using a similarity measure to determine a similarity score for each selected sentence, wherein the similarity measure comprises extracting a first set of tokens from at least one template from the set of original templates and extracting a second set of tokens from at least one candidate sentence from the set of candidate sentences, the first set of tokens and the second set of tokens each comprising a set of token-level 1 to token-level n grams, and further comprises comparing the extracted first set of tokens with the extracted second set of tokens by determining a first value representing an intersection of the extracted first and second sets of tokens, and dividing that first value by a second value derived by applying a minimum function to the extracted first and second sets of tokens to determine the similarity score; ii) automatically eliminate candidate sentences from the set of candidate sentences based upon a similarity score threshold to arrive at a reduced set of candidate sentences determined to be syntactically similar to the at least one template; and iii) generate a set of natural language generation templates based at least in part on the similarity scores that, when processed by a computer and combined with a set of determined words or phrases, generate natural language text. 17. The system of claim 11 wherein the template analyzer is adapted to identify a set of candidate sentences that relate to a topic similar to a topic associated with the set of original templates. | 0.754364 |
8,561,185 | 1 | 15 | 1. A method performed by data processing apparatus, the method comprising: accessing, by a data processing apparatus, personally identifiable information (PII) type definitions that characterize PII types; identifying, by the data processing apparatus, PII type information included in a sub-portion of content of a web page, the PII type information being information matching at least one PII type definition, the sub-portion of content defined by a predefined text distance relative to a location of the PII type information; identifying, by the data processing apparatus, secondary information included in the content of the web page, the secondary information being information that is predefined as being associated with PII type information, the identifying the secondary information comprising: comparing text within the pre-defined text distance of the sub-portion of content to a plurality of keywords that are defined as being associated with the PII type information; and identifying, within the predefined text distance of the sub-portion of content, text that matches one or more of the keywords as secondary information; determining a risk score from the PII type information and the secondary information; and classifying the web page as a personal information exposure risk if the risk score meets a confidentiality threshold, wherein the personal information exposure risk is indicative of the web page including personally identifiable information. | 1. A method performed by data processing apparatus, the method comprising: accessing, by a data processing apparatus, personally identifiable information (PII) type definitions that characterize PII types; identifying, by the data processing apparatus, PII type information included in a sub-portion of content of a web page, the PII type information being information matching at least one PII type definition, the sub-portion of content defined by a predefined text distance relative to a location of the PII type information; identifying, by the data processing apparatus, secondary information included in the content of the web page, the secondary information being information that is predefined as being associated with PII type information, the identifying the secondary information comprising: comparing text within the pre-defined text distance of the sub-portion of content to a plurality of keywords that are defined as being associated with the PII type information; and identifying, within the predefined text distance of the sub-portion of content, text that matches one or more of the keywords as secondary information; determining a risk score from the PII type information and the secondary information; and classifying the web page as a personal information exposure risk if the risk score meets a confidentiality threshold, wherein the personal information exposure risk is indicative of the web page including personally identifiable information. 15. The method of claim 1 , further comprising: adjusting a web page resource index so that the web page will not be identified as being responsive to a query. | 0.87381 |
9,934,775 | 1 | 13 | 1. A system for unit-selection text-to-speech synthesis, the system comprising: one or more processors; and memory storing one or more programs, wherein the one or more programs include instructions which, when executed by the one or more processors, cause the one or more processors to: receive text to be converted to speech; generate a sequence of target units representing a spoken pronunciation of the text; determine, based on a plurality of linguistic features associated with each target unit of the sequence of target units, predicted statistical parameters for each of a plurality of acoustic features associated with each target unit, wherein a second acoustic feature of the plurality of acoustic features represents a change of a first acoustic feature of the plurality of acoustic features across a portion of a respective target unit of the sequence of target units; select, based on the plurality of linguistic features associated with each target unit, a plurality of candidate speech segments corresponding to the sequence of target units; for each candidate speech segment of the plurality of candidate speech segments: determine a target cost based on the predicted statistical parameters of the first acoustic feature associated with a respective target unit of the sequence of target units; and determine a plurality of concatenation costs with respect to a plurality of subsequent candidate speech segments, the plurality of concatenation costs determined based on the predicted statistical parameters of the second acoustic feature associated with the respective target unit of the sequence of target units; select from the plurality of candidate speech segments a subset of candidate speech segments for speech synthesis, the selecting based on a combined cost associated with the subset of candidate speech segments, wherein the combined cost is determined based on the target cost and the plurality of concatenation costs of each candidate speech segment; and generate speech corresponding to the received text using the subset of candidate speech segments. | 1. A system for unit-selection text-to-speech synthesis, the system comprising: one or more processors; and memory storing one or more programs, wherein the one or more programs include instructions which, when executed by the one or more processors, cause the one or more processors to: receive text to be converted to speech; generate a sequence of target units representing a spoken pronunciation of the text; determine, based on a plurality of linguistic features associated with each target unit of the sequence of target units, predicted statistical parameters for each of a plurality of acoustic features associated with each target unit, wherein a second acoustic feature of the plurality of acoustic features represents a change of a first acoustic feature of the plurality of acoustic features across a portion of a respective target unit of the sequence of target units; select, based on the plurality of linguistic features associated with each target unit, a plurality of candidate speech segments corresponding to the sequence of target units; for each candidate speech segment of the plurality of candidate speech segments: determine a target cost based on the predicted statistical parameters of the first acoustic feature associated with a respective target unit of the sequence of target units; and determine a plurality of concatenation costs with respect to a plurality of subsequent candidate speech segments, the plurality of concatenation costs determined based on the predicted statistical parameters of the second acoustic feature associated with the respective target unit of the sequence of target units; select from the plurality of candidate speech segments a subset of candidate speech segments for speech synthesis, the selecting based on a combined cost associated with the subset of candidate speech segments, wherein the combined cost is determined based on the target cost and the plurality of concatenation costs of each candidate speech segment; and generate speech corresponding to the received text using the subset of candidate speech segments. 13. The system of claim 1 , wherein the predicted statistical parameters for each of the plurality of acoustic features associated with each target unit are determined using a statistical model. | 0.868207 |
4,627,091 | 6 | 7 | 6. In a speech recognition system, an apparatus for storing speech parameters, said apparatus comprising: transducer means responsive to acoustic energy for transforming said acoustic energy into analog electric signals, wherein said acoustic energy comprises voiced speech, unvoiced speech and background noise; signal processing means for converting said analog signals to substantially equivalent forms of speech parameters and for sampling said speech parameters at a predetermined sampling rate; first storage means coupled to said signal processing means for temporarily storing a plurality of samples of said speech parameters from said signal processing means; a binary adder coupled to said signal processing means for computing the average signal level of samples of said speech parameters from said signal processing means during predetermined periods of time; second storage means coupled to said first storage means for receiving speech parameters transferred from said first storage means; means coupled to said second storage means for generating sequential addresses corresponding to individual storage locations of said second storage means to permit storage of said transferred speech parameters therein; and means coupled to said address generating means and to said binary adder for resetting said generating means to a reference starting address when the average signal level computed during any of said predetermined periods of time fails to exceed a predetermined signal level. | 6. In a speech recognition system, an apparatus for storing speech parameters, said apparatus comprising: transducer means responsive to acoustic energy for transforming said acoustic energy into analog electric signals, wherein said acoustic energy comprises voiced speech, unvoiced speech and background noise; signal processing means for converting said analog signals to substantially equivalent forms of speech parameters and for sampling said speech parameters at a predetermined sampling rate; first storage means coupled to said signal processing means for temporarily storing a plurality of samples of said speech parameters from said signal processing means; a binary adder coupled to said signal processing means for computing the average signal level of samples of said speech parameters from said signal processing means during predetermined periods of time; second storage means coupled to said first storage means for receiving speech parameters transferred from said first storage means; means coupled to said second storage means for generating sequential addresses corresponding to individual storage locations of said second storage means to permit storage of said transferred speech parameters therein; and means coupled to said address generating means and to said binary adder for resetting said generating means to a reference starting address when the average signal level computed during any of said predetermined periods of time fails to exceed a predetermined signal level. 7. The apparatus according to claim 6 wherein said first storage means is a shift register. | 0.948991 |
8,150,872 | 1 | 2 | 1. A query system, comprising: a computing device communicatively coupled to a network and configured to receive audio input comprising a query and determine location information; and a server communicatively coupled to the computing device via the network, wherein the server is configured to: receive the query from the computing device; perform natural language processing on the query using lexicons and grammar rules to parse sentences and determine a meaning of the query, wherein the natural language processing comprises converting text in natural language form to text in searchable form using the lexicons and grammar rules to determine the meaning of the query; utilize location information to further determine the meaning of the query; perform a database look up based on the determined meaning of the query, wherein the database look up is provided with a context and environment for narrowing and streamlining the database look up utilizing the location information; and rank responses of the database lookup using an accuracy algorithm. | 1. A query system, comprising: a computing device communicatively coupled to a network and configured to receive audio input comprising a query and determine location information; and a server communicatively coupled to the computing device via the network, wherein the server is configured to: receive the query from the computing device; perform natural language processing on the query using lexicons and grammar rules to parse sentences and determine a meaning of the query, wherein the natural language processing comprises converting text in natural language form to text in searchable form using the lexicons and grammar rules to determine the meaning of the query; utilize location information to further determine the meaning of the query; perform a database look up based on the determined meaning of the query, wherein the database look up is provided with a context and environment for narrowing and streamlining the database look up utilizing the location information; and rank responses of the database lookup using an accuracy algorithm. 2. The query system of claim 1 , wherein the computing device comprises one of a smart phone or tablet device. | 0.720812 |
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